Daniela Camilla

Diana

Manuela Lena

Juan Pablo

Andre

Pauline, Rogerio, Zeinabouth, Kathy Stephanie Hebert

I sincerely thank Ivo Mueller for his patience and trust in me in these 3.5 years of PhD. For allowing me to enjoy doing science and to be proud to belong to the MPH laboratory. Thanks, Ivo, for helping me understand that in science, as in life, everything has a maturing process. Thank you very much, Michael White, for your support and teaching me to trust myself more, and above all, for showing me, that science can always have a more human face. Infinite thanks to Dioni Gamboa for the unlimited support that you have always shown despite the distances and difficulties. Another step

Chapter 1 provides an overview of the epidemiology of Plasmodium vivax malaria, with special focus on the epidemiology of malaria in the Americas and in Peru, and how aspects of Plasmodium vivax biology make this parasite difficult to eradicate.

Epidemiology of Plasmodium vivax

Malaria is a preventable disease caused by parasites of the Plasmodium genus and transmitted by female Anopheles mosquitoes. Six Plasmodium species infect humans: Plasmodium falciparum, P. vivax, P. malariae, P. ovale, P. knowlesi, and P. cynomolgi of which P. falciparum and P. vivax are the most prevalent.

P. knowlesi and P. cynomolgi are Southeast Asian, zoonotic malaria parasites that naturally infect primates.

Global epidemiology of Plasmodium vivax

Despite being a preventable disease, malaria continues impacting people's health and life around the world. In 2019, the World Health Organization (WHO) reported 229 million cases of malaria in 87 countries (95 % confidence interval (CI): 211 -252 million), an important decrease compared to 247 million cases reported in 2010 . Of these number of cases in 2019, 94% (215 million) were in the WHO African Region, 3 % in WHO East Asia Region, 2% in WHO Eastern Mediterranean Region, 0.7% in WHO Western Pacific Region, and 0.3% in WHO Region of the Americas (World Health Organization 2020).

In most countries where malaria is endemic, the disease affects mainly residents in economically disadvantaged countries with limited access to the health services.

Plasmodium falciparum is the predominant malaria parasite in the African Region. It accounts for almost 100% of the cases reported by the WHO African Region. Plasmodium vivax, however, is mainly distributed outside the African cone. The vivax cases in the South East Asia and the Eastern Mediterranean region account for 50% and 35% of the global vivax burden, respectively (Battle et al. 2019, World Health Organization 2020).

The global vivax malaria incidence and P. vivax parasite prevalence have decreased in the last 9 years due to the implementation of interventions in many countries; in particular, there have been significant

changes since 2014 onwards. Three per cent of malaria cases in 2019 were caused by P. vivax (6.9 million),

showing a significant decrease of 40.5% since 2010 (7% i.e. 17 million malaria cases) (World Health Organization 2020). In 2010, the incidence was 3.1 cases per 1000 population at risk, whereas in 2019, 1.67 cases per 1000 population at risk was estimated (Figure 1.1A). The number of cases predicted are shown on a spectrum of light blue to red. Areas where P. vivax is known to be endemic, but there was not sufficient information to generate a prediction are shown in light grey. These maps were created by Malaria Atlas Project (MAP) (https://malariaatlas.org/).

For many years, P. vivax was considered as benign when compared to P. falciparum; however, a case fatality rates of up to 0.3% have been reported [START_REF] Rahimi | Severe vivax malaria: a systematic review and meta-analysis of clinical studies since 1900[END_REF]. In patients hospitalized for P. vivax malaria death occurred in 28.2% of cases [START_REF] Rahimi | Severe vivax malaria: a systematic review and meta-analysis of clinical studies since 1900[END_REF]. Children under 1 year and 1-5 years are the most vulnerable group by cumulative severe anemia from repeated P. vivax malaria episodes [START_REF] Kenangalem | Plasmodium vivax infection: a major determinant of severe anaemia in infancy[END_REF].

Malaria transmission control, although highly cost-effective, is one of the great challenges in public health.

Annually, US $ 3 billion are invested in malaria control and elimination efforts globally. The WHO has established an ambitious plan for elimination of the disease by 2030 in at least 35 countries that were endemic in 2015, the Global Technical Strategy (World Health Organization 2015a). According to the last malaria report, the number of countries with fewer than 100 indigenous cases increased from 17 countries in 2010, to 27 countries in 2018. Between 2000 and 2019, 21 countries had achieved 3 consecutive years of zero malaria cases, and 10 of these countries were certified malaria free by the WHO. Despite this progress, the disease remains endemic in all six WHO regions, thus all the preventative efforts must focus on developing sustainable measures.

1.1.2. P. vivax epidemiology in the Americas About 139 million people in 18 countries in the Americas are at risk of malaria. Most malaria cases are caused by Plasmodium vivax (76%), followed by Plasmodium falciparum in single or mixed infections (24%).

Significant advances had taken place in the region between 2010 and 2015, when the incidence of cases declined from 821 000 to 561 000; however, the number of cases dramatically increased in 2019 reaching a figure of 889 000 cases. The main strategies implemented have been rapid diagnosis, treatment with artemisinin-based combination therapy (ACT), indoor residual spraying (IRS), and insecticide-treated bednets (ITNs) or long-lasting insecticide-treated nets (LLIN) [START_REF] Flores | Rapid assessment of the performance of malaria control strategies implemented by countries in the Amazon subregion using adequacy criteria: case study[END_REF]. In 2019, LLIN distribution increased by 128% compared with 2015, while the number of people protected by IRS decreased by 48%

(World Health Organization 2020).

Three countries -Venezuela, Brazil and Colombia -accounted for almost 90% of malaria cases in the Americas in 2019, of which 55% of the reported cases of malaria came from Venezuela. The transmission in these countries is focal, i.e. consisting of large areas of low-transmission interspersed with hot-spots where transmission is spatial and temporally focused (da [START_REF] Da Silva-Nunes | Amazonian malaria: asymptomatic human reservoirs, diagnostic challenges, environmentally driven changes in mosquito vector populations, and the mandate for sustainable control strategies[END_REF]. In particular, it is high in regions such as Choco in Colombia, Loreto in Peru and Bolivar in Venezuela.

Some of the main challenges to the elimination of malaria in this region are a lack of sustainable international and domestic funding for surveillance and elimination programs, crisis in the health system due to political conflicts (e.g. Venezuela), environmental changes, and insecticide resistance in mosquito vectors [START_REF] Quinones | Insecticide Resistance in Areas Under Investigation by the International Centers of Excellence for Malaria Research: A Challenge for Malaria Control and Elimination[END_REF]. Additional challenges are the rise of malaria incidence in Venezuela and the emigration of Venezuelan people to neighboring countries [START_REF] Grillet | Venezuela's humanitarian crisis, resurgence of vectorborne diseases, and implications for spillover in the region[END_REF][START_REF] Jaramillo-Ochoa | Effects of Political Instability in Venezuela on Malaria Resurgence at Ecuador-Peru Border, 2018[END_REF], submicroscopic and asymptomatic infections [START_REF] Rosas-Aguirre | Micro-heterogeneity of malaria transmission in the Peruvian Amazon: a baseline assessment underlying a population-based cohort study[END_REF], increase of cases of gold miningrelated malaria [START_REF] Quinones | Insecticide Resistance in Areas Under Investigation by the International Centers of Excellence for Malaria Research: A Challenge for Malaria Control and Elimination[END_REF], and increase of P. falciparum transmission in Colombia and Peru (World Health Organization 2020).

Currently, there are 7 countries with nationwide elimination programs: Argentina, Belize, Costa Rica, Ecuador, El Salvador, Mexico and Suriname. El Salvador has reported zero indigenous cases for three consecutive years (2018, 2019 and 2020), as has Argentina (2016, 2017 and 2018), and together with Paraguay, they have been certified as malaria free countries by the WHO in 2021 (World Health Organization 2021a), 2019 and 2018, respectively.

P. vivax epidemiology in Peru

Peru is a region with low transmission of vivax malaria, with 19,600 cases reported in 2019 (Ministerio de Salud Perú 2020). Over the last 20 years, the case incidence of vivax malaria in Peru has been fluctuating

(Figure 1.3); especially over the last 9 years due to the influence of intervention programs. After the cessation of the Global Fund Malaria Project "PAMAFRO" in 2010, there was a noteworthy increase in the number of vivax malaria cases [START_REF] Rosas-Aguirre | Epidemiology of Plasmodium vivax Malaria in Peru[END_REF], with a steady rise from 21 984 cases in 2011 to 54 819 cases in 2014 (World Health Organization 2020). The causes of this resurgence were (1) budgetary constraints;

(2) the perception that malaria was under control; and (3) a concurrent regional dengue In the most recent WHO report, Peru reported 5 % of the malaria cases from the Americas. The majority of cases are concentrated in Loreto Department in the north-west Amazon region, which accounts for 96.5% of reported cases (Ministerio de Salud Perú 2020) (Figure 1.4). The transmission is constant, with a peak season between January and June, and epidemic increases of clinical cases detected by routine passive case detection (PCD) in some communities [START_REF] Rosas-Aguirre | Hotspots of Malaria Transmission in the Peruvian Amazon: Rapid Assessment through a Parasitological and Serological Survey[END_REF]. P. vivax infections occur mainly in people living in poorly built houses located in rural and remote villages along the main Amazon River and its tributaries the Itaya and Nanay Rivers to the south and west of Iquitos, and also in highway Among the ecological factors that drive spatial heterogeneity are those that facilitate mosquito breeding and resting sites associated with deforestation, and natural and man-made standing water bodies [START_REF] Rosas-Aguirre | Epidemiology of Plasmodium vivax Malaria in Peru[END_REF]). In addition, rural activities [START_REF] Rosas-Aguirre | Micro-heterogeneity of malaria transmission in the Peruvian Amazon: a baseline assessment underlying a population-based cohort study[END_REF], occupational mobility [START_REF] Carrasco-Escobar | High prevalence of very-low Plasmodium falciparum and Plasmodium vivax parasitaemia carriers in the Peruvian Amazon: insights into local and occupational mobility-related transmission[END_REF], migration and poorer housing conditions [START_REF] Grietens | Traditional nets interfere with the uptake of long-lasting insecticidal nets in the Peruvian Amazon: the relevance of net preference for achieving high coverage and use[END_REF]) are sociodemographic factors that drive malaria transmission.

The vast majority of infections are asymptomatic with low and submicroscopic (subpatent) parasite densities [START_REF] Carrasco-Escobar | High prevalence of very-low Plasmodium falciparum and Plasmodium vivax parasitaemia carriers in the Peruvian Amazon: insights into local and occupational mobility-related transmission[END_REF]. This feature has an impact on the detection and accurate recording of cases in the Peruvian Amazon. The detection system is constituted by light microscopy (LM) at health posts and hospitals, where a symptomatic patient with a positive microscopy result will get treated.

Treatment with primaquine is given only to symptomatic P. vivax patients with positive results by LM, but the effectiveness of primaquine is unclear because the administration is not typically supervised [START_REF] Grietens | Adherence to 7-day primaquine treatment for the radical cure of P. vivax in the Peruvian Amazon[END_REF].

Nyssorhynchus darlingi (previously Anopheles darlingi) is the main malaria vector in the Peruvian Amazon [START_REF] Saavedra | Higher risk of malaria transmission outdoors than indoors by Nyssorhynchus darlingi in riverine communities in the Peruvian Amazon[END_REF]. It is mainly anthropophilic [START_REF] Moreno | Intensive trapping of blood-fed Anopheles darlingi in Amazonian Peru reveals unexpectedly high proportions of avian blood-meals[END_REF], and poses a challenge for vector control because it feeds and rests outdoors [START_REF] Prussing | Decreasing proportion of Anopheles darlingi biting outdoors between long-lasting insecticidal net distributions in peri-Iquitos, Amazonian Peru[END_REF]).

P. vivax life cycle

P. vivax has morphologically and antigenically distinct stages (Figure 1.4). The cycle starts with the infective stage called the sporozoite, present in the salivary glands of an infected female Anopheles mosquito and is injected into the host skin during a blood meal. The sporozoite is the uninucleate, extracellular and motile stage of the parasite coated with the circumsporozoite protein (PvCSP). The pre-erythrocytic stage immunity is mainly targeted at this protein. From the rodent malaria model, P. berghei, it is known that the sporozoite can i) either remain in the skin (up to 60% of injected sporozoites) (Amino et al. 2006a) ii) or be transported in lymph vessels to draining lymph nodes, where most are degraded but some can mature into early exo-erythrocytic forms (Amino et al. 2006b), or iii) travel through the bloodstream to the liver [START_REF] Vanderberg | Intravital microscopy demonstrating antibody-mediated immobilisation of Plasmodium berghei sporozoites injected into skin by mosquitoes[END_REF]).

In the liver, sporozoites traverse the Kupffer cells and invade hepatocytes within minutes to develop into either an actively dividing schizont or a dormant hypnozoite [START_REF] Krotoski | Demonstration of hypnozoites in sporozoitetransmitted Plasmodium vivax infection[END_REF][START_REF] Krotoski | Discovery of the hypnozoite and a new theory of malarial relapse[END_REF]. By mitotic division, the hepatic parasite divides to become a multinucleated schizont. After 9-16 days, the schizont bursts releasing thousands of merozoites packaged together inside vesicles (merosomes), surrounded by hepatocyte membrane [START_REF] Menard | Looking under the skin: the first steps in malarial infection and immunity[END_REF]. A unique biologic feature of P. vivax and P. ovale, is their ability to remain in the liver as dormant stages for weeks and months. As a result, many recurrent episodes can results from the activation of hypnozoites following a single mosquito bite [START_REF] White | Relapse[END_REF]. Most importantly, hypnozoite relapses can account for approximately 80% of the vivax cases in some endemic regions [START_REF] Robinson | Strategies for understanding and reducing the Plasmodium vivax and Plasmodium ovale hypnozoite reservoir in Papua New Guinean children: a randomised placebocontrolled trial and mathematical model[END_REF]. Various relapse patterns have been described for P. vivax with two extreme phenotypes described: Some tropical strains that relapse at three week intervals, and some temperate strains with long incubations of 8-10 months prior to the first erythrocytic episode [START_REF] White | Relapse[END_REF]). The number of sporozoites inoculated by mosquito seems to be an important determinant of both the timing and the number of relapses [START_REF] White | Relapse[END_REF]. After activation, hypnozoites will continue with the development program and differentiate into liver schizonts Within one or two minutes of release to the blood circulation, the merozoites will invade the immature reticulocytes that have been recently formed in the bone marrow (BM) and express the transferrin receptor 1 (CD71) [START_REF] Malleret | Plasmodium vivax: restricted tropism and rapid remodeling of CD71-positive reticulocytes[END_REF]. Reticulocytes constitute only 0.3-4% of red blood cells (RBC) and have a lifespan of 1 day in the peripheral blood, compared to mature RBC with 120 days lifespan [START_REF] Bessman | Reticulocytes[END_REF]). The preference to infecting reticulocytes over the mature RBC may be related to the plasticity provided by the reticulocytes as the parasites develop. Increased deformability is needed to periodically squeeze through the splenic chords [START_REF] Handayani | High deformability of Plasmodium vivax-infected red blood cells under microfluidic conditions[END_REF]. Apart from infecting reticulocytes in peripheral blood, there is a growing body of evidence that P. vivax infects erythroblasts [START_REF] Ru | Invasion of erythroblasts by Pasmodium vivax: A new mechanism contributing to malarial anemia[END_REF]) and early reticulocytes in the BM [START_REF] Ovchynnikova | DARC extracellular domain remodeling in maturating reticulocytes explains Plasmodium vivax tropism[END_REF]). Higher P. vivax biomass has been detected in the BM compared to blood [START_REF] Silva-Filho | Plasmodium vivax in Hematopoietic Niches: Hidden and Dangerous[END_REF], supporting the idea that BM is a site for parasite replication and re-infection. All these features contribute to maintain P. vivax at low densities in the blood circulation [START_REF] Cheng | Systematic review of sub-microscopic P. vivax infections: prevalence and determining factors[END_REF] and to stay hidden in hematopoietic niches. This has been an impediment to develop long-term culture of P. vivax, unlike in P. falciparum that infects RBC of all ages [START_REF] Galinski | Plasmodium vivax: modern strategies to study a persistent parasite's life cycle[END_REF].

Invasion into the RBC depends on a range of ligand-receptor interactions. The initial interactions are due to low-affinity interactions between merozoite surface proteins (PvMSPs) and as yet to be characterized surface RBC receptors. These interactions are followed by the reorientation of merozoites apical ends onto the RBC surface and secretion of invasion ligands from two apical complex organelles, rhoptries and micronemes. To date, the only known ligand/receptor pair associated with reticulocyte selectivity is the transferrin receptor 1 CD71 with one of the P. vivax reticulocyte-binding protein (PvRBP) family of adhesins, called PvRBP2b [START_REF] Gruszczyk | Transferrin receptor 1 is a reticulocyte-specific receptor for Plasmodium vivax[END_REF].

Next, an irreversible tight-junction results when the microneme apical membrane antigen 1 (PvAMA-1) binds to the rhoptry neck protein complex and forms a connective ring between the merozoite and the RBC. The irreversible interaction between the cysteine-rich domain II of the Duffy-binding protein (PvDBP)

and the Duffy antigen/receptor for chemokines (DARC) also takes place during the formation of this junction [START_REF] Horuk | A receptor for the malarial parasite Plasmodium vivax: the erythrocyte chemokine receptor[END_REF]. Finally, the invading merozoite is totally within the RBC and then it sheds its PvMSP1 surface coat. Two types of sporozoites are injected into the host: those that will differentiate into merozoites by active mitotic division right after hepatocyte invasion (1), and those that will stay in a quiescent stage within the hepatocyte, hypnozoites (2). Created with BioRender.com.

After invasion, the merozoites transform into the ring-stage form, followed by the metabolically active trophozoites. Trophozoites mitotically multiply to become schizonts, which then mature and on rupture, each schizont releases 8 to 16 merozoites, each with the potential to invade new reticulocytes.

Unlike P. falciparum, gametogenesis in P. vivax is shorter [START_REF] Bousema | Epidemiology and infectivity of Plasmodium falciparum and Plasmodium vivax gametocytes in relation to malaria control and elimination[END_REF]). After 3-5 days, some P. vivax merozoites (< 10% of asexual parasites) develop into sexual stages known as gametocytes [START_REF] Mckenzie | Gametocytemia in Plasmodium vivax and Plasmodium falciparum infections[END_REF][START_REF] Huh | Parasitemia characteristics of Plasmodium vivax malaria patients in the Republic of Korea[END_REF]. The immature gametocytes will differentiate later into male (microgametes) and female (macrogametes). A single parasite clone can produce both male and female gametocytes and can self-fertilize [START_REF] Sinden | Sexual development of malarial parasites[END_REF]. In controlled experimental infections, gametocytes can be found in the bloodstream within 7 days of direct P. vivax sporozoite infection [START_REF] Vallejo | Plasmodium vivax gametocyte infectivity in sub-microscopic infections[END_REF], meaning that in natural infections, it could be possible for the transmission to occur before the infections can be diagnosed and treated, which poses another threat to eliminate and control vivax malaria.

The cycle is completed with the ingestion of mature gametocytes by an Anopheles mosquito female during blood feeding. In the midgut lumen of the mosquito, they transform into gametes and lose their RBC membrane outer coat. In a process named ex-flagellation, the nucleus of the microgamete fractures into eight nuclei with the corresponding formation of eight flagellating bodies [START_REF] Carter | Control of gamete formation (exflagellation) in malaria parasites[END_REF]. When a microgamete penetrates a macrogamete, a diploid zygote is formed that over a 24-48 h period metamorphoses into an ookinete, another tissue invasive parasite stage. The ookinete is formed by a single meiotic recombination of the zygote which acquires motility. Next, the ookinete penetrates, travels, and emerges on the other site of the midgut epithelium. Finally, in the luminal side of the epithelium, by mitosis division the ookinete becomes an oocyst, resulting in thousands of sporozoites. The rupture of oocyst releases sporozoites in the mosquito hemolymph and these then migrate to the salivary glands with the consequent transmission to humans during a mosquito bite.

1.3. P. vivax pathology and treatment 1.3.1. Pathology of P. vivax All the symptoms and pathology of malaria are caused by blood-stage parasites. It has been often regarded as a benign self-limiting infection, but the disease burden, economic impact and severity of disease of P.

vivax has been recently underestimated [START_REF] Suwanarusk | Chloroquine resistant Plasmodium vivax: in vitro characterisation and association with molecular polymorphisms[END_REF]. Vivax malaria is able to incapacitate individuals from all ages causing frequent febrile episodes, severe anemia, dyserythropoiesis, respiratory distress, cerebral malaria, poor outcomes in pregnancy, and occasionally multi-organ failure [START_REF] Kochar | Severe Plasmodium vivax malaria: a report on serial cases from Bikaner in northwestern India[END_REF].

Severe disease has been recorded in low-resources populations, tropical settings with high hypnozoite relapses and comorbidities such as nutritional, genetic and immunological disorders; indicating that these could be risk factor for severe disease [START_REF] Price | New developments in Plasmodium vivax malaria: severe disease and the rise of chloroquine resistance[END_REF].

There are some differences in pathobiology between P. vivax and P. falciparum that are important in understanding the pathology of vivax malaria: parasite biomass, relapse, greater inflammatory response in P. vivax than P. falciparum, cytoadherence and deformability and fragility of P. vivax -infected RBCs. These are briefly described in the following lines.

P. vivax infected erythrocytes sequester in the bone marrow and spleen [START_REF] Silva-Filho | Plasmodium vivax in Hematopoietic Niches: Hidden and Dangerous[END_REF] causing parasitaemias lower than 1%, yet still having the potential for severe disease. A report in Brazil indicated a threshold of 500 parasites/µL to severe disease in vivax malaria patients admitted to intensive care [START_REF] Lanca | Risk factors and characterization of Plasmodium vivax-associated admissions to pediatric intensive care units in the Brazilian Amazon[END_REF]. In contrast, the WHO considers parasite counts > 250 000/µL as a risk for severe disease in P.

falciparum infections.

A fundamental difference between P. falciparum and P. vivax is the ability of P. vivax to relapse from dormant hypnozoites and cause repeated episodes of clinical and subclinical infections. Frequent recurrent infections prevent adequate time for the patient to recover from each haemolytic attack [START_REF] Anstey | Plasmodium vivax: clinical spectrum, risk factors and pathogenesis[END_REF].

Even when P. vivax infections have low parasitaemia, these can cause a greater inflammatory response than P. falciparum [START_REF] Anstey | Plasmodium vivax: clinical spectrum, risk factors and pathogenesis[END_REF]. Cytokine production, endothelial activation and pulmonary responses are higher during and after P. vivax infections than in P. falciparum infections of similar parasite biomass [START_REF] Anstey | Plasmodium vivax: clinical spectrum, risk factors and pathogenesis[END_REF]).

Cytoadherence has been described in P. falciparum as a mechanism to sequester and obstruct micro blood vessels [START_REF] Anstey | Plasmodium vivax: clinical spectrum, risk factors and pathogenesis[END_REF]. In vitro data show that P. vivax -infected RBCs do cytoadhere to endothelial cells via ICAM1 and chondroitin sulphate-A, with similar strength but lower frequency than P. falciparuminfected RBCs [START_REF] Carvalho | On the cytoadhesion of Plasmodium vivax-infected erythrocytes[END_REF]. Cytoadherence could be linked with sequestration by infected red cells in lungs and placenta, although in vivo findings of cytoadherence are limited [START_REF] Anstey | Plasmodium vivax: clinical spectrum, risk factors and pathogenesis[END_REF].

In P. falciparum, deformability of infected and non-infected erythrocytes is reduced and is thought to contribute to impair organ perfusion [START_REF] Anstey | Plasmodium vivax: clinical spectrum, risk factors and pathogenesis[END_REF]). In P. vivax, the deformability of infected RBCs is increased and makes impaired organ unlikely, but such deformability is accompanied by increased fragility of both infected and non-infected RBCs [START_REF] Handayani | High deformability of Plasmodium vivax-infected red blood cells under microfluidic conditions[END_REF]).

Treatment of P. vivax

Chloroquine has been the recommended first-line drug to fight asexual parasites of vivax malaria for over the last 70 years; with a recommended dose of 10 mg/kg body weight for two days and 5 mg/kg body weight on the third day [START_REF] Baird | Diagnosis and Treatment of Plasmodium vivax Malaria[END_REF], World Health Organization 2021b). Nevertheless, resistance to this drug has been reported in Oceania and Indonesia since the 1990s [START_REF] White | Antimalarial drug resistance[END_REF]) and is appearing in Southeast Asia, South Asia, the Middle East, East Africa, and the Americas [START_REF] Ferreira | Monitoring Plasmodium vivax resistance to antimalarials: Persisting challenges and future directions[END_REF], thus representing an increasing threat to existing control and prevention programs. The molecular basis of chloroquine resistance in P. vivax has not been extensively studied; nevertheless, some in vivo and in vitro studies reported mutations in genes encoding essential enzymes or transporters such as P. vivax multidrug resistance (Pvmdr) and putative transporter protein (Pvcrt-o) [START_REF] Brega | Identification of the Plasmodium vivax mdr-like gene (pvmdr1) and analysis of single-nucleotide polymorphisms among isolates from different areas of endemicity[END_REF][START_REF] Suwanarusk | Chloroquine resistant Plasmodium vivax: in vitro characterisation and association with molecular polymorphisms[END_REF].

In many regions where chloroquine resistance in P. vivax is seen, artemisinin combination therapy (ACT)

along with primaquine is now used as an alternative treatment (World Health Organization 2015b). ACT such as dihydroartermisinin-piperaquine and artesunate-mefloquine have proven efficacious and due to their long half-lives also provide effective post-exposure prophylaxis against early recurrence of P. vivax infection (Douglas 2010).

Due to the relapsing nature of vivax malaria, a drug targeting the dormant liver stage parasites and providing radical cure is needed. Primaquine is an 8-aminoquinoline with proven hypnozoitocidal activity.

Although highly effective, hemolytic anemia is a complication caused by administering primaquine in individuals with moderate to severe glucose-6-phosphate dehydrogenase (G6PD) deficiency [START_REF] Baird | Diagnosis and Treatment of Plasmodium vivax Malaria[END_REF]. In view of the risk of adverse reactions, primaquine dosing strategies are influenced by concerns over toxicity. In an attempt to reduce potential toxicity, the WHO recommends a daily dose of 0.25-0.5 mg/kg/body weight/day for 14 days in children and adults with normal G6PD activity, co-administered with chloroquine or ACT depending on chloroquine sensitivity in the region (World Health Organization 2021b); whereas in people with G6PD deficiency, WHO recommends a weekly dose of 0.75 mg/kg/body weight for 8 weeks (World Health Organization 2021b).

While primaquine is an effective treatment for radical cure, better hypnozoitocidal alternatives with lower risk of adverse effects and better adherence are essential to reach elimination of this disease. Recently, a new drug of the family of 8-aminoquinoline has been evaluated in clinical trials, tafenoquine. Tafenoquine is administered as a single-dose of 300 mg, and has shown to be as efficient as primaquine to prevent relapses [START_REF] Lacerda | Single-Dose Tafenoquine to Prevent Relapse of Plasmodium vivax Malaria[END_REF], Llanos-Cuentas et al. 2019). In 2021, the Peruvian Ministry of Health approved tafenoquine for the radical treatment of vivax malaria in patients 16 years of age or older who are receiving chloroquine for acute infections (GlaxoSmithKline 2021).

Challenges to P. vivax elimination

The elimination of Plasmodium vivax is challenging mainly due to its key biologic features: i) Up to 90% of cases are low-density infections and asymptomatic, thus evading detection by routine diagnostic systems based on light microscopy and rapid diagnostic tests; ii) high transmissibility linked to the early and continuous gametocyte production even before the onset of the symptoms; and iii) ability to form longlasting quiescent liver stages (i.e. the hypnozoites) to produce relapsing infections months to years after a primary infection.

To help in the fight towards vivax malaria elimination, we should be focused on these pillars:

 Development of sensitive tools to detect submicroscopic infections for prompt treatment  Deploy tools to target the silent reservoir of transmission, i.e. hypnozoite carriers  Development of tools to characterize the contribution of hypnozoite relapses to the burden of

blood-stage infections and transmission

This thesis aims to characterize the antibody responses to novel serological markers of exposure to P. vivax in areas of high and moderate transmission of the Peruvian Amazon and to use this knowledge to develop a new sero-diagnostic assay for exposure to SARS-CoV-2. The fundamental aspects of the immune response to P. vivax and the application to inform malaria transmission will be broadly covered in the next pages.

Naturally Acquired Immunity to P. vivax

Naturally acquired immunity (NAI) to P. vivax is not sterilizing and manifests itself as protection against high-density parasite infections, which prevents the development of clinical disease characterized by fever, malaise and anaemia. The NAI, particularly antibody production, is developed predominantly by the natural exposure of the human host to primary blood-stage infections and relapse infections in the blood.

Robert Koch empirically described concepts on NAI to malaria in his expedition in the German New Guinea (now Papua New Guinea): He showed that among Chinese and Malay plantation workers, the prevalence of malaria was much higher in recent comers than in long-term residents. Among the indigenous population, children were commonly infected, and no parasites were found in adults. Adults from an island

where Plasmodium malariae was only found became sick with Plasmodium falciparum after migrating to a highly endemic area of mainland New Guinea (Koch 1900b). He also discovered the existence of asymptomatic infections and recognized that these carriers were an important source of transmission (Koch 1900a).

Other important contributions to the knowledge on immunity development came from malaria therapy studies. In the first half of the 20 th century, before the discovery of Penicillin for treating syphilis, malaria therapy based on P. vivax strains was an efficient alternative to boost febrile responses and attack cerebral syphilis [START_REF] Vogel | Malaria as lifesaving therapy[END_REF]. Data collected from experimental malaria therapies showed that patients developed quick immunity after a single primary infection [START_REF] Collins | A retrospective examination of reinfection of humans with Plasmodium vivax[END_REF]. Parasitaemia levels of these patients significantly decreased when they were reinfected with a homologous P. vivax strain; whereas, comparable parasitaemia levels were found after reinfection with a heterologous strain [START_REF] Collins | A retrospective examination of reinfection of humans with Plasmodium vivax[END_REF].

Reports in children from Papua New Guinea (PNG), a highly co-endemic setting for P. vivax and P.

falciparum [START_REF] Mueller | The epidemiology of malaria in Papua New Guinea[END_REF], showed that in children after 5 years of continuous exposure, clinical immunity to P. vivax is much more rapidly acquired than that to P. falciparum [START_REF] Lin | Differential patterns of infection and disease with P. falciparum and P. vivax in young Papua New Guinean children[END_REF]. Likewise, the incidence of P. vivax febrile illness decreases from the second year of life [START_REF] Mueller | Natural acquisition of immunity to Plasmodium vivax: epidemiological observations and potential targets[END_REF]. This acquisition of clinical immunity also impacts the age-specific P. vivax prevalence. The prevalence of P. vivax detected by light microscopy (LM) peaks in children aged 4-6.9 years (24%); whereas, in adults, the P. vivax prevalence is less frequently detected by LM (10%). When PCR prevalence is evaluated, the peak prevalence is shifted into older groups (Mueller et al. 2009b). This phenomenon of decreasing P. vivax malaria incidence with age was also found in endemic areas of low transmission level on the Thai-Myanmar border [START_REF] Lawpoolsri | The impact of human reservoir of malaria at a community-level on individual malaria occurrence in a low malaria transmission setting along the Thai-Myanmar border[END_REF], Sri Lanka [START_REF] Mendis | The neglected burden of Plasmodium vivax malaria[END_REF]Brazil (da Silva-Nunes et al. 2008),

supporting the notion that P. vivax clinical immunity is developed rapidly even at lower levels of exposure.

From all this evidence, we can draw the following conclusions about the immunity to P. vivax: (1) the NAI is most rapidly developed in inhabitants from areas with high transmission where multiple exposures to the parasite and relapses over time provide a varied repertoire of strain-specific immunity. NAI in lower transmission areas is slower and may never be complete. ( 2) Regular antigen exposure, or premunition, is a condition required to maintain NAI, and it is diminished after transmission cessation, but in individuals with high immunity levels, NAI may persist longer [START_REF] Mueller | Natural acquisition of immunity to Plasmodium vivax: epidemiological observations and potential targets[END_REF]). ( 3) There is evidence showing that NAI is species-specific, at least with respect to P. vivax versus P. falciparum [START_REF] Collins | A retrospective examination of sporozoite-and trophozoiteinduced infections with Plasmodium falciparum in patients previously infected with heterologous species of Plasmodium: effect on development of parasitologic and clinical immunity[END_REF]. Although strain-specific immunity to P. vivax has not been well characterized, it is thought that there could be some degree of cross-protection [START_REF] Collins | A retrospective examination of reinfection of humans with Plasmodium vivax[END_REF]). The study of strain-specific immunity in relapsing infections remains an unexplored topic.

Most of this knowledge has been drawn from studies in high-transmission areas and from controlled human infections to treat syphilis with malaria therapy; however, NAI to P. vivax in low and intermediate transmission conditions are still being explored (Longley et al. 2020). With the introduction of control measures and the goal of malaria elimination, a better understanding of NAI in elimination scenarios remains essential.

Naturally acquired cellular immunity

P. vivax contains more than 5000 proteins [START_REF] Aurrecoechea | PlasmoDB: a functional genomic database for malaria parasites[END_REF]); while some have been selected for vaccine candidates, we do not yet know which ones are responsible for clinical immunity or can act as vaccine candidates.

Antibodies or humoral immunity are important components of the NAI; nevertheless, cellular immunity may provide more protection than appreciated. The research on cellular immune responses to P. vivax is limited due to the lack of a culture system of the parasite. Evidence of the role of T cells during blood-stage infections is increasing [START_REF] Junqueira | Cytotoxic CD8(+) T cells recognize and kill Plasmodium vivax-infected reticulocytes[END_REF], and they are also thought to be essential to attack hidden liver-stage parasites in other Plasmodium species [START_REF] Hoffman | Sporozoite vaccine induces genetically restricted 125 T cell elimination of malaria from hepatocytes[END_REF][START_REF] Weiss | Cytotoxic T cells recognize a peptide from the circumsporozoite protein on malaria-infected hepatocytes[END_REF]). Generally, sporozoites fail to elicit a robust immune response due to the low exposure during natural transmission (as sporozoites only remain in the blood for hours before entering the liver). This hypothesis has been confirmed by a controlled infection study showing that the transcriptome of CD4+ T, CD8+ T and B cells is mostly activated by blood-stage and not by pre-erythrocytic parasite stages [START_REF] Rojas-Pena | Transcription Profiling of Malaria-Naive and Semi-immune Colombian Volunteers in a Plasmodium vivax Sporozoite Challenge[END_REF]; however, some immunization studies with other Plasmodium species demonstrated that attenuated sporozoites could induce protective CD8+ T cells [START_REF] Rodrigues | The relative contribution of antibodies, CD4+ and CD8+ T cells to sporozoite-induced protection against malaria[END_REF]). In addition, CD4+ T cells have been thought to be involved in the development of protective immunity by helping the antibody responses; this data has been tested in both P. falciparum [START_REF] Chan | Th2-like T Follicular Helper Cells Promote Functional Antibody Production during Plasmodium falciparum Infection[END_REF]) and P. vivax patients [START_REF] Figueiredo | T follicular helper cells regulate the activation of B lymphocytes and antibody production during Plasmodium vivax infection[END_REF] (Figure 2.1). Therefore, T cells are likely essential to mount cytotoxic responses and to mediate antibody responses to P. vivax infections.

Reports show that there are cellular responses to the P. vivax antigens tryptophan-rich antigens (TRAgs) (Alam et al. 2008a, Alam et al. 2008b[START_REF] Mittra | Plasmodium vivax: immunological properties of tryptophanrich antigens PvTRAg 35.2 and PvTRAg 80.6[END_REF][START_REF] Zeeshan | CD4+ T cell response correlates with naturally acquired antibodies against Plasmodium vivax tryptophan-rich antigens[END_REF], PvMSP1 [START_REF] Soares | Acquired immune responses to the N-and C-terminal regions of Plasmodium vivax merozoite surface protein 1 in individuals exposed to malaria[END_REF][START_REF] Seth | Acquired immune response to defined Plasmodium vivax antigens in individuals residing in northern India[END_REF][START_REF] Riccio | Cellular and humoral immune responses against the Plasmodium vivax MSP-1(1)(9) malaria vaccine candidate in individuals living in an endemic area in north-eastern Amazon region of Brazil[END_REF] and limited information about PvAMA1 [START_REF] Bueno | Plasmodium vivax recombinant vaccine candidate AMA-1 plays an important role in adaptive immune response eliciting differentiation of dendritic cells[END_REF]), PvMSP9 (Lima-Junior et al. 2008) and DBP region II (PvDBPII) [START_REF] Xainli | Age-dependent cellular immune responses to Plasmodium vivax Duffy binding protein in humans[END_REF]. PvCSP is the most studied nonblood stage protein [START_REF] Herrera | Human recognition of T cell epitopes on the Plasmodium vivax circumsporozoite protein[END_REF][START_REF] Bilsborough | Identification of Caucasian CD4 T cell epitopes on the circumsporozoite protein of Plasmodium vivax. T cell memory[END_REF][START_REF] Migot | Humoral and cellular immune responses to the circumsporozoite protein of Plasmodium vivax in Madagascar[END_REF][START_REF] Suphavilai | Analysis of circumsporozoite protein-specific immune responses following recent infection with Plasmodium vivax[END_REF][START_REF] Seth | Acquired immune response to defined Plasmodium vivax antigens in individuals residing in northern India[END_REF]. The frequency of recognition of T cells to PvCSP peptides varied from less than 20% in Thai individuals [START_REF] Suphavilai | Analysis of circumsporozoite protein-specific immune responses following recent infection with Plasmodium vivax[END_REF] to almost 60% in a region of the Colombian Pacific Coast [START_REF] Herrera | Human recognition of T cell epitopes on the Plasmodium vivax circumsporozoite protein[END_REF]) and in Caucasian volunteers who had previously lived for more than 7 years in a malariaendemic area (PNG, the Solomon Islands or south-east Asia) [START_REF] Bilsborough | Identification of Caucasian CD4 T cell epitopes on the circumsporozoite protein of Plasmodium vivax. T cell memory[END_REF]. Still, a more detailed characterization of T cells responses and their contribution to clinical immunity is needed.

B-cell activation and differentiation into memory B cells and plasma cells ─ resulting in antibody production

─ are essential components of the naturally acquired immunity that develops following Plasmodium exposure. Although there is no direct evidence of the development of B lymphocyte and humoral immunological memory, it has been proposed that constant antigenic stimulation is needed for maintaining memory T-and B-cell populations [START_REF] Bilsborough | Identification of Caucasian CD4 T cell epitopes on the circumsporozoite protein of Plasmodium vivax. T cell memory[END_REF]) and that dormant hypnozoite could stimulate the immune system. Interestingly, it was also demonstrated that B cells are still found even when plasma antibodies no longer existed [START_REF] Ndungu | Memory B cells are a more reliable archive for historical antimalarial responses than plasma antibodies in no-longer exposed children[END_REF], indicating the maintenance of memory cells without antigenic stimulation is possible. The explanation of the persistence of antibody may fit in three possible scenarios, according to Longley et al. (Longley et al. 2016b): i) memory B cells are stimulated to differentiate into plasma cells, antibody secretory cells, either by reinfection or by antigens captured on follicular dendritic cell (Figure 2.1); ii) memory B cells must be stimulated by some form of polyclonal activation, or iii) long-lived plasma cells must be generated. To date, the limited evidence showed that acute P. vivax infections increase the frequency of activated memory cells (CD19 + CD21 -CD27 + ) and atypical memory B cells (CD19 + CD21 -CD27 -) [START_REF] Figueiredo | T follicular helper cells regulate the activation of B lymphocytes and antibody production during Plasmodium vivax infection[END_REF][START_REF] Kochayoo | The persistence of naturally acquired antibodies and memory B cells specific to rhoptry proteins of Plasmodium vivax in patients from areas of low malaria transmission[END_REF][START_REF] Soares | Apical membrane protein 1-specific antibody profile and temporal changes in peripheral blood B-cell populations in Plasmodium vivax malaria[END_REF][START_REF] Rivera-Correa | Atypical memory B-cells and autoantibodies correlate with anemia during Plasmodium vivax complicated infections[END_REF], and convalescence is associated with increased frequency of classical memory cells (CD19 + CD21 + CD27 + ) and naïve B cells (CD19 + CD21 + CD27 -) [START_REF] Figueiredo | T follicular helper cells regulate the activation of B lymphocytes and antibody production during Plasmodium vivax infection[END_REF][START_REF] Kochayoo | The persistence of naturally acquired antibodies and memory B cells specific to rhoptry proteins of Plasmodium vivax in patients from areas of low malaria transmission[END_REF]. Furthermore, after repeated infections, a relative expansion of classical memory B cells and a reduction in other B-cell subpopulations is observed [START_REF] Figueiredo | T follicular helper cells regulate the activation of B lymphocytes and antibody production during Plasmodium vivax infection[END_REF]). However, more longitudinal studies are needed to understand the relationship between specific cellular responses following P. vivax infection and clinical immunity, and especially against the liver stages, as was shown in the closely related relapsing monkey malaria Plasmodium cynomolgi [START_REF] Joyner | Humoral immunity prevents clinical malaria during Plasmodium relapses without eliminating gametocytes[END_REF].

Naturally acquired humoral immunity

This section will review the antibody-mediated immunity in three subsections: the relationship with clinical protection, transmission-blocking immunity, and markers of exposure.

Antibodies and their role in clinical protection

Even when NAI to P. vivax is rapidly acquired, this immunity is not sterilizing; thus, it does not give full protection, and subjects can be reinfected many times. Although it is a controversial topic, pre-erythrocytic antibody responses can be developed following natural P. vivax infections, as reports of anti-CSP antibody responses have been described in multiple geographic regions (Longley et al. 2016a). It is unclear whether these antibodies against pre-erythrocytic proteins can prevent infection. Antibodies to PvDBP [START_REF] King | Naturally acquired Duffy-binding protein-specific binding inhibitory antibodies confer protection from blood-stage Plasmodium vivax infection[END_REF], Cole-Tobian et al. 2009, Franca et al. 2017, He et al. 2019b) and PvMSP1 [START_REF] Nogueira | A reduced risk of infection with Plasmodium vivax and clinical protection against malaria are associated with antibodies against the N terminus but not the C terminus of merozoite surface protein 1[END_REF]) have shown a correlation with clinical protection for P. vivax, i.e. delayed time to reinfection and reduction in parasitaemia levels, without necessarily implying a causal relationship with protective immunity. However, it has been demonstrated that antibodies to PvDBP region II (PvDBPII) can block the binding of DBPII to RBCs [START_REF] Michon | Naturally acquired and vaccine-elicited antibodies block erythrocyte cytoadherence of the Plasmodium vivax Duffy binding protein[END_REF] and that the titers of anti-DBPII correlate with the level of inhibition, suggesting that with enough antibodies against DBP, protective immunity could result. Given the importance of these proteins in the invasion of RBC, they are strong candidates as vaccine targets [START_REF] Chitnis | Identification of the erythrocyte binding domains of Plasmodium vivax and Plasmodium knowlesi proteins involved in erythrocyte invasion[END_REF][START_REF] Singh | Malaria vaccine candidate based on Duffy-binding protein elicits strain transcending functional antibodies in a Phase I trial[END_REF].

Moreover, the presence of antibodies against PvMSP3, PvMSP9, PvRBP2b and PvRBP1a has shown a correlation with decreasing clinical incidence in children from PNG [START_REF] Stanisic | Naturally acquired immune responses to P. vivax merozoite surface protein 3alpha and merozoite surface protein 9 are associated with reduced risk of P. vivax malaria in 138 young Papua New Guinean children[END_REF][START_REF] Franca | Plasmodium vivax Reticulocyte Binding Proteins Are Key Targets of Naturally AcqCQCCWuired Immunity in Young Papua New Guinean Children[END_REF], so do antibodies to PvRBP2b with a delayed time to first clinical episode observed in low prevalence population of Thailand and Brazil (He et al. 2019a). No evidence of inhibition of P. vivax invasion by antibodies to these proteins has been found; however. More studies to distinguish between functional antibodies and exposure marker antibodies are needed. immunity. TB has been largely studied in avian and falciparum malaria; though some studies reported that gametocyte specific antibodies produced in subjects infected by P. vivax from high [START_REF] Mendis | Malaria transmissionblocking immunity induced by natural infections of Plasmodium vivax in humans[END_REF][START_REF] Ranawaka | Boosting of transmission-blocking immunity during natural Plasmodium vivax infections in humans depends upon frequent reinfection[END_REF]) and low endemic settings [START_REF] Ramsey | Acquired transmission-blocking immunity to Plasmodium vivax in a population of southern coastal Mexico[END_REF][START_REF] Sattabongkot | Comparison of artificial membrane feeding with direct skin feeding to estimate the infectiousness of Plasmodium vivax gametocyte carriers to mosquitoes[END_REF][START_REF] Coleman | Infectivity of asymptomatic Plasmodium-infected human populations to Anopheles dirus mosquitoes in western Thailand[END_REF][START_REF] Arevalo-Herrera | Plasmodium vivax: transmission-blocking immunity in a malaria-endemic area of Colombia[END_REF][START_REF] Arevalo-Herrera | Characterization of Plasmodium vivax transmission-blocking activity in low to moderate malaria transmission settings of the Colombian Pacific coast[END_REF]) inhibited transmission in direct membrane feeding assays. Antibodies from individuals experiencing their second P. vivax attack within the first 4 months are more efficient in reducing transmission than those from first-attack patients [START_REF] Ranawaka | Boosting of transmission-blocking immunity during natural Plasmodium vivax infections in humans depends upon frequent reinfection[END_REF]. Antibodies directed to gametocyte surface antigens may prevent fertilization via direct lysis of gametocytes by activation of the complement system, or opsonization resulting in immune cell-mediated lysis or agglutination of gametocytes [START_REF] De Jong | Immunity against sexual stage Plasmodium falciparum and Plasmodium vivax parasites[END_REF]. Up to date, Pvs25, which is expressed in zygotes and ookinetes, has been proposed as a promising post-fertilization vaccine antigen candidate [START_REF] Hisaeda | Antibodies to malaria vaccine candidates Pvs25 and Pvs28 completely block the ability of Plasmodium vivax to infect mosquitoes[END_REF][START_REF] Sauerwein | Transmission blocking malaria vaccines: Assays and candidates in clinical development[END_REF].

Antibodies as markers of exposure

Following a P. vivax infection, antibody responses are mounted against thousands of parasite proteins. The intensity and duration of these antibody responses could vary for each protein, i.e. different antibody acquisition, boosting and decay of antibody responses. However, the role of premunition in maintaining NAI is controversial, the precise duration of NAI is unknown as is the effect of age and the difference in transmission levels. Researchers are trying to answer these questions by studying the kinetics of antibodies to P. vivax (Kerkhof et al. 2016b, Longley et al. 2017b).

An early study by Ray et al. showed that the majority of Indian patients studied (95%) had antibody responses against P. vivax blood-stage lysate [START_REF] Ray | Plasmodium vivax: immune responses in a cross-section of the population in the Delhi area of India[END_REF]. The response was also found in the group of recovered patients and the control group (50%), compared with zero of nine naïve controls from abroad.

This demonstrates that seroconversion following P. vivax infection was possible and that these responses can be maintained (as shown in the Indian control group). This study was informative but did not characterize the immunodominant antigens of P. vivax. Other studies have also demonstrated that there is good maintenance of positive antibody responses to P. vivax (extensive review in (Longley et al. 2016b)).

Although the duration of antibody response varies enormously among studies, all of them coincide in that antibody titers decline over time (Longley et al. 2016b). The knowledge of the duration of NAI could benefit from determining whether there was previous exposure or even an estimation of the time of last exposure.

The sources of variation for estimating the duration of antibody responses in these studies can be classified as technical and epidemiological factors. Among the technical factors, the quality of the antigen tested, the technology deployed to measure antibodies and cut-offs utilized to classify a positive response are thought to be important. The epidemiological factors include the age of participants as well as the transmission intensity and the number of infections experienced by subjects prior to the study. For example, Braga et al reported that both antibody responses to PvCSP and PvMSP1 could last 7 years in absence of re-infections after the cessation of transmission in Minas Gerais in Brazil [START_REF] Braga | Persistence of humoral response against sporozoite and blood-stage malaria antigens 7 years after a brief exposure to Plasmodium vivax[END_REF].

Interestingly, antibodies to PvMSP1 decayed faster than those to PvCSP and were present only in those subjects who experienced clinical events in the previous 7 years. Conversely, another study, in the city of Belem in Brazil, found that antibodies to PvMSP1 declined over 2-4 months [START_REF] Soares | Longevity of naturally acquired antibody responses to the N-and C-terminal regions of Plasmodium vivax merozoite surface protein 1[END_REF]). These contradictory results may be due to some factors mentioned above; nonetheless, this work reflects that proteins can be broadly categorized in two groups: markers of lifetime exposure and markers of recent exposure.

The unveiling of the full genome [START_REF] Carlton | Comparative genomics of the neglected human malaria parasite Plasmodium vivax[END_REF]) and transcriptome [START_REF] Bozdech | The transcriptome of Plasmodium vivax reveals divergence and diversity of transcriptional regulation in malaria parasites[END_REF] symptomatic Pv patients of hypoendemic Peruvian Amazon using the protein array described by Finney et al. [START_REF] Chuquiyauri | Genome-Scale Protein Microarray Comparison of Human Antibody Responses in Plasmodium vivax Relapse and Reinfection[END_REF]. They demonstrated that the number of antigens recognized by patients with P. vivax relapses (219) did not differ from those of Pv reinfected patients (285); the number of antigens recognized by patients after a second relapse episode was slightly higher but not significantly different than that after a first relapse episode. They also identified candidates for seroepidemiological tools in endemic P. vivax regions [START_REF] Chuquiyauri | Genome-Scale Protein Microarray Comparison of Human Antibody Responses in Plasmodium vivax Relapse and Reinfection[END_REF]. The lack of sensitive molecular tools to differentiate between relapses and reinfections was a limitation of this study, but it set a precedent for investigating the relationship between antibody boosting and recurrent P. vivax infections.

Another critical study was the one by King et al [START_REF] King | Biosignatures of Exposure/Transmission and Immunity[END_REF]. King and colleagues initially compared the antibody responses to 4441 Pv proteins from adults of moderate/high transmission settings (PNG, China, Thailand, and Peru) to sera from non-endemic individuals. In the second phase of this study, they tested the antibody intensity to the 500 Pv most seroreactive antigens downselected in the first phase in sera from participants ranging diverse transmission settings and all ages: Low transmission: Madre de Dios in Peru (n=40), Solomon Islands (n=164), Colombia (n=340); Moderate: Brazil (n=281), Thailand (n=153), Santa Emilia in Peru (n=324). Although this study sought to identify markers associated with transmission and acquired immunity, it was clear that previous infection history affected the antibody baseline of participants: the antibody response was higher and more diverse in the higher transmission population [START_REF] King | Biosignatures of Exposure/Transmission and Immunity[END_REF]. The authors advised that short-lived antibody responses would be suitable to detect recent infections in high endemic settings, whereas moderate lived antibodies would be suitable in low endemic settings [START_REF] King | Biosignatures of Exposure/Transmission and Immunity[END_REF]. This study set the basis for more detailed studies to identify optimal antigens for biosignatures; however, it was limited because it was based only on cross-sectional surveys.

A study on P. falciparum showed for the first time the relationship between antibody levels and the time elapsed since previous P. falciparum infection [START_REF] Helb | Novel serologic biomarkers provide accurate estimates of recent Plasmodium falciparum exposure for individuals and communities[END_REF]. Helb et al. explored falciparum exposure data were available. The work showed that subjects with recent infections have higher antibody titers than those with old infections. Moreover, antibody titers to the top 10 antigens were positively correlated with the individual's age and with the number of positive infections in the preceding year. Among the 856 proteins, antibody responses to 3 proteins accurately classified whether an individual was infected within the last 30, 90 or 365 days. In contrast, antibody responses to 6 proteins accurately estimated an individual's malaria incidence in the previous 12 months [START_REF] Helb | Novel serologic biomarkers provide accurate estimates of recent Plasmodium falciparum exposure for individuals and communities[END_REF]. Among the lessons learnt from this study, the following can be highlighted: i) a detailed history of malaria exposure is crucial for the phase of antigen discovery of exposure markers, e.g. longitudinal cohorts; ii) by combining high throughput assays and machine learning algorithms, we can optimize the selection of markers informative of recent past exposure; iii) candidate proteins have to be validated in cohorts with different transmission intensity and in participants of all ages; iv) antibody responses to a handful of carefully selected proteins can be used to extract useful information about the history of malaria infection in a population in crosssectional studies.

Recently, Longley et al. described the utility of novel serological markers to inform about the exposure to P. vivax in the previous 9 months (Longley et al. 2020). Initially, they explored the magnitude and longevity of IgG responses to 342 Pv proteins following symptomatic P. vivax infections to identify proteins suitable to detect recent exposure in longitudinal plasma samples from P. vivax patients from Thailand (n=32) and Brazil (n=33) (Longley et al. 2017b). Of these 342 Pv proteins, 60 were prioritized as candidate serological exposure markers (SEM) with the selection of those that had IgG half-lives spanning 6 months and were immunogenic at the time of infection (>50% seroprevalence) (Longley et al. 2020). By combining machine learning algorithms and high throughput multiplex fluorescence antibody assays, they disentangled the relationship between the time of the last infection and antibody levels to 60 SEM in three year-long cohort studies: Thailand (n=829), Brazil (n=928) and the Solomon Islands (n=754). Antibody responses to 8 of these antigens classified infections in the previous 9 months, with 80% sensitivity and 80% specificity. Given that most P. vivax relapses occur within the first 9 months after the primary mosquito bite, these 8 SEM ensure the detection of the likely Pv hypnozoite carriers. These novel markers thus allow not just to identify potential hypnozoite carriers, but they could also be deployed in serological testing and treatment interventions (SeroTaT) with anti-hypnozoite therapy in low or close-to-elimination malaria settings, with the benefits of a mass drug administration (MDA) combined with a low overtreatment rate (Longley et al. 2020). Despite the great promise of these novel SEM in low P. vivax transmission settings, their performance in moderate and high transmission settings remained to be tested. Preliminary data described by Longley and collaborators showed that antibody responses to SEMs might last longer in regions with high P. vivax exposure than in low transmission regions (Longley et al. 2017b). Therefore the applicability of these SEMs in high endemic regions could be limited.

A few studies have explored the benefits of measuring antibody responses to estimate the exposure to P. To summarize, antibody responses can be used to explore exposure to P. vivax. The principle concept is that antibodies persist longer than blood-stage infections, which provides a unique signature shaped by the previous exposure to the parasite (Figure 2.2 A). Antibody responses could be categorized as shortlived and long-lived responses (Kerkhof et al. 2016b). The duration of these antibody responses will depend on the epidemiology context where it is studied (Figure 2.2 B). To investigate antibody responses as potential markers of exposure, it is therefore essential to explore the contribution of previous lifetime exposure, the boosting by recurrent infections, and the impact of diverse malaria transmission intensities.

Well-designed cohort studies and the use of high-throughput assays will provide the best scenario to answer these questions. relapses in a time period of 6 months. Short-lived antibodies can be detected up to 20-30 days after infection; whereas moderate-lived Ab can be detected up to 45-98 days after infection (Longley et al. 2017b). Ideally, moderate-and short-lived antibodies would be suitable to detect recent exposure in low transmission settings, while short-lived antibodies would be suitable to detect recent exposure in high transmission settings. B) Antibody kinetics of a shortlived antibody response in low-and high-transmission setting. Antibody response can be boosted by reinfections or relapses in high transmission settings, causing a slow decay and a remaining tail response. Created with BioRender.com.

Application of serology data

In the previous section, I described the effectors of the naturally acquired immunity to P. vivax, emphasising humoral immunity. In this second part, I intend to explain why measuring exposure to P. vivax is relevant in the context of elimination and why antibody responses may be the best tool to do so.

In 2017, the first malaria serology working group meeting took place in Paris to discuss the priorities of using serological tools in the era of malaria elimination. In this section, the classical use of malaria serology, as well as the newly proposed use cases defined by the malaria serology working group are described: (i) inform malaria endemicity; (ii) document absent of transmission; (iii) stratification of transmission; (iv) measure the impact of interventions; and (v) P. vivax sero-test-and-treat [START_REF] Greenhouse | Priority use cases for antibodydetecting assays of recent malaria exposure as tools to achieve and sustain malaria elimination[END_REF].

Whereas this section deals exclusively with the uses of serology tools in the context of malaria elimination, these can also apply to other infectious diseases such as COVID-19.

Why study P. vivax exposure?

Measuring P. vivax exposure is beneficial for understanding transmission risk in low transmission and elimination settings. Instead of identifying only people with ongoing blood-stage infections, measuring exposure would detect individuals with at least one infection in a defined period in the recent past. It could also identify hypnozoite carriers at high risk of relapsing. This approach is essential in low transmission settings, where most sensitive molecular tools will miss the majority of actively infected individuals as they have low parasite density infections. Moreover, a recent exposure tool could detect those individuals that recently cleared infections and have contributed to the transmission and were missed by sensitive diagnostic tools [START_REF] Greenhouse | Priority use cases for antibodydetecting assays of recent malaria exposure as tools to achieve and sustain malaria elimination[END_REF].

A sensitive recent exposure test will be more cost-effective than a population survey to yield information on whether and where transmission has been occurring because it will require a smaller sample size [START_REF] Helb | Novel serologic biomarkers provide accurate estimates of recent Plasmodium falciparum exposure for individuals and communities[END_REF]. In terms of the timing of surveys, an approach based on recent exposure will be more flexible, i.e. during or after malaria transmission season, because the test will capture information over a span of time longer than the time of survey [START_REF] Greenhouse | Priority use cases for antibodydetecting assays of recent malaria exposure as tools to achieve and sustain malaria elimination[END_REF]).

The antibody signature can reconstruct each participants' individual history of exposure and how it is shaped by cumulative exposure and recent exposure. Several researchers are characterizing the suitability of antibody responses for estimating recent exposure in different age groups and in different transmission settings [START_REF] Finney | Predicting antidisease immunity using proteome arrays and sera from children naturally exposed to malaria[END_REF][START_REF] Chuquiyauri | Genome-Scale Protein Microarray Comparison of Human Antibody Responses in Plasmodium vivax Relapse and Reinfection[END_REF][START_REF] Helb | Novel serologic biomarkers provide accurate estimates of recent Plasmodium falciparum exposure for individuals and communities[END_REF][START_REF] King | Biosignatures of Exposure/Transmission and Immunity[END_REF], Kerkhof et al. 2016b, Longley et al. 2017b, Longley et al. 2020).

Continuous, not binary, antibody data to better estimate recent exposure

A common strategy to estimate transmission intensity from serological surveys has been the seroconversion rate (SCR) based on analyzing seropositivity to a single antigen stratified by age. The SCR typically shows that individuals in endemic areas are more likely to have been exposed to malaria and, therefore, be seropositive [START_REF] Greenhouse | Taking Sharper Pictures of Malaria with CAMERAs: Combined Antibodies to Measure Exposure Recency Assays[END_REF]. By age-stratified antibody prevalence, SCR can provide metrics that reflect transmission in a community ( Detecting transmission changes by SCR is characterised by abrupt changes in specific age groups or changes in the SCR from year to year, indicating a shift in transmission intensity at some point in time in the past [START_REF] Cook | Serological markers suggest heterogeneity of effectiveness of malaria control interventions on Bioko Island, equatorial Guinea[END_REF]. Measuring recent exposure and rapid changes in transmission via SCR however, is challenging due to the binary data obtained from long-lived antibody responses [START_REF] Greenhouse | Taking Sharper Pictures of Malaria with CAMERAs: Combined Antibodies to Measure Exposure Recency Assays[END_REF].

Responses to several antigens measured at the same time will provide tremendous value to answer different epidemiological questions. For example, Baum et al. demonstrated that of 857 P. falciparum antigens screened, only a subset of targets was more efficient in distinguishing transmission in two areas of highland Kenya [START_REF] Baum | Protein microarray analysis of antibody responses to Plasmodium falciparum in western Kenyan highland sites with differing transmission levels[END_REF]. Another advantage of the multiplex antibody measures is the ability to use antibody level readouts (titers) to estimate better age-stratified antibody prevalence allowing different rates of boost or decay of relative antibody units [START_REF] Yman | Antibody acquisition models: A new tool for serological surveillance of malaria transmission intensity[END_REF]. Once the most informative antibodies of recent exposure have been selected, they can be used to detect spatial heterogeneity in transmission in a community, outperforming parasite prevalence in precision and dynamic range from even small sample sizes [START_REF] Helb | Novel serologic biomarkers provide accurate estimates of recent Plasmodium falciparum exposure for individuals and communities[END_REF][START_REF] Greenhouse | Taking Sharper Pictures of Malaria with CAMERAs: Combined Antibodies to Measure Exposure Recency Assays[END_REF]

(Figure 2.3 C).
These examples clearly show the advantages of looking at antibody responses to multiple antigens rather than looking at one antigen to provide precise past exposure information. The new multiplex antibody assays, further refinement of markers, and new analytical methods will shed light on their role in malaria surveillance.

Assessing malaria endemicity

The age-seroprevalence curves can reflect transmission intensity. The scenarios for this use-case are sites with moderate to low transmission (Figure 2.4). In low transmission areas, the antibody acquisition process or seroconversion is low and maybe only observed in adults. On the other hand, in high transmission areas, seroconversion is faster, and most of the population will be seropositive, with the exception of younger children. Serology data has been classically used to estimate malaria transmission intensity in areas where the transmission is low, and the parasite prevalence and annual entomologic inoculation rate (EIR) are insensitive. Serology has also been used to detect focal transmission of malaria. One of the first serology work to detect malaria outbreak in Peru was published by [START_REF] Sulzer | A focus of hyperendemic Plasmodium malariae-P. vivax with no P. falciparum in a primitive population in the Peruvian Amazon jungle[END_REF][START_REF] Sulzer | A focus of hyperendemic Plasmodium malariae-P. vivax with no P. falciparum in a primitive population in the Peruvian Amazon jungle[END_REF]). Sulzer et al. executed a parasitological and serological (immunofluorescence antibody test, IFAT) survey for P. vivax, P. falciparum and P. brasilianum (considered as P. malariae now) to determine the extent of malaria infection in an isolated community in southeastern Peru. Microscopy results showed up to 83 % malaria prevalence, whereas seroprevalence was 91% [START_REF] Sulzer | A focus of hyperendemic Plasmodium malariae-P. vivax with no P. falciparum in a primitive population in the Peruvian Amazon jungle[END_REF]. The author concluded that the strongest reaction (high antibody titers) are likely to reflect the most recent infection, and high titers can be interpreted as recent or indeed current infections.

Document the absence of local transmission

The absence of antibodies for recent malaria exposure will confirm the absence of local transmission in a given population [START_REF] Drakeley | Chapter 5. Potential contribution of sero-epidemiological analysis for monitoring malaria control and elimination: historical and current perspectives[END_REF]). This could be the case of a program that may have missed some indigenous cases through passive case detection and needs additional evidence from another reliable approach to confirm the absence of transmission over a defined period of time. If there is no evidence of transmission, then the strategy should shift to prevention of reintroduction. In case of evidence of transmission, then the strategy should focus on reducing transmission by interventions such as IRS, etc. [START_REF] Greenhouse | Priority use cases for antibodydetecting assays of recent malaria exposure as tools to achieve and sustain malaria elimination[END_REF]. This approach should be accompanied by routine passive case detection and case investigation and metrics like annual blood slide examination rates. The scenarios for this use-case are sites with approaching zero or zero transmission (Figure 2.4). The assay format to address this question could be an antibody test performed by central laboratories, either through surveys of general public, sentinel populations at high risk, or participants passively screened at health facilities [START_REF] Greenhouse | Priority use cases for antibodydetecting assays of recent malaria exposure as tools to achieve and sustain malaria elimination[END_REF]).

Stratification of transmission

As malaria-endemicity decreases to low or very low levels, transmission becomes heterogeneous, with active transmission sites (hot-spots or residual pockets) surrounded by extensive areas of no transmission [START_REF] Reiner | Mapping residual transmission for malaria elimination[END_REF]. At a village level, malaria transmission can be focused in some households with high infection levels, whereas others appear to be free of exposure. There are many factors affecting malaria heterogeneity: human mobility, forest-related jobs, housing design, proximity to favourable mosquito breeding sites, correct use of mosquito nets, geographic topography.

As broad coverage of interventions may not be cost-effective, surveillance for stratification becomes an integral part of the control and elimination program (World Health Organization 2015a). Antibody responses can be used to identify foci of high active transmission and optimize focalized interventions. By combining spatial analysis with seroprevalence data, a map of risk of exposure can be developed to guide intervention strategy (Kerkhof et al. 2016a). The level of application goes from medium (heterogeneous)

to very low transmission (Figure 2.4). Based on the results, the actions taken would encompass policy recommendation, continued or adjusted use of interventions, and report on impact. This use is ideal for low to approaching zero transmission settings [START_REF] Greenhouse | Priority use cases for antibodydetecting assays of recent malaria exposure as tools to achieve and sustain malaria elimination[END_REF]. As presented by Corran et al., at low transmission settings, serology should be the test of choice to assess exposure levels as parasite rates will be extremely low and numbers of infected mosquitoes lower [START_REF] Corran | Serology: a robust indicator of malaria transmission intensity?[END_REF]).

Measuring the impact of interventions

The first study to confirm malaria eradication by antibody prevalence was executed in Mauritius back in 1973 [START_REF] Bruce-Chwatt | Seroepidemiological evidence of eradication of malaria from Mauritius[END_REF]. Antibodies were highly detected in subjects over 20 years old; it reflected the intense past exposure before the intervention program started in 1949. The low proportion of seropositivity in the age of 5 -20 years old indicated that malaria control activities were successfully implemented. The lack of antibodies in the population under five years old showed malaria transmission cessation [START_REF] Bruce-Chwatt | Seroepidemiological evidence of eradication of malaria from Mauritius[END_REF]. The authors also highlighted the need for high sensitivity tools not to miss any low antibody titers, although this should be followed up to verify if necessary.

P. vivax sero-test and treat

With this approach, serology turns to a new venture: focalized treatment for radical cure of P. vivax. P.

vivax poses a menace for elimination because of the relapsing hypnozoites, and the asymptomatic and low parasitaemia infections that constitute a hidden reservoir for transmission. This new approach could identify individuals infected with P. vivax in a determined period and address treatment with hypnozoiticidal drugs (Longley et al. 2020). Depending on the results, this approach should be accompanied by G6PD testing and therapy with primaquine/tafenoquine for sero-positive individuals (exposed in the last 9 months). In contrast to the previously described use-cases, this new use case explores the individual malaria exposure history, so its application level is circumscribed to the district, village or population at high risk [START_REF] Greenhouse | Priority use cases for antibodydetecting assays of recent malaria exposure as tools to achieve and sustain malaria elimination[END_REF]. This use should be applicable to low to approaching zero transmission settings due to the potential application of focalised interventions.

Assays for measuring antibody responses

In the last 100 years, assays to detect malarial antibodies have dramatically evolved in terms of better diagnostic parameters, throughput and ease of use [START_REF] Drakeley | Chapter 5. Potential contribution of sero-epidemiological analysis for monitoring malaria control and elimination: historical and current perspectives[END_REF]). Among the tests are complement fixation test, indirect haemagglutination assay, IFAT, enzyme-linked immunosorbent assay (ELISA), protein arrays and multiplex microsphere assays [START_REF] Drakeley | Chapter 5. Potential contribution of sero-epidemiological analysis for monitoring malaria control and elimination: historical and current perspectives[END_REF][START_REF] Greenhouse | Taking Sharper Pictures of Malaria with CAMERAs: Combined Antibodies to Measure Exposure Recency Assays[END_REF]. The first application for serology assays was a diagnostic tool; however, many studies found that malaria antibodies did not appear until several days after evident parasitaemia making their diagnostic use limited [START_REF] Warren | The seroepidemiology of malaria in Middle America. III. Serologic assessment of localized Plasmodium falciparum epidemics[END_REF]). Their use to determine malaria endemicity's level became obvious when applied in studies of non-immune French soldiers deployed to highly endemic areas of Gabon and Côte d'Ivoire in 2002 [START_REF] Drakeley | Chapter 5. Potential contribution of sero-epidemiological analysis for monitoring malaria control and elimination: historical and current perspectives[END_REF]. A significant advance is the combined measurement of multiple antibody responses to provide more precise estimates of recent exposure in the individual screened (Longley et al. 2020). Following infection, antibody levels change over time. It has been documented that antibodies to SARS-CoV-2 reach a peak level 5-15 days after the onset of symptoms [START_REF] Borremans | Quantifying antibody kinetics and RNA detection during early-phase SARS-CoV-2 infection by time since symptom onset[END_REF]). However, not all antigens would have the same kinetics and antibody responses will decay at different rates. If a tool is going to be used in serological surveys then there is a major risk that individuals that were seropositive turn seronegative [START_REF] Adams | Antibody testing for COVID-19: A report from the National COVID Scientific Advisory Panel[END_REF]). In addition, the seropositivity will depend on the antigen used in the tool, the validation panel used to define a cut-off, the dynamic range of detection of the tool, and possible cross-reactions with other pathogens. These technical features will impact on estimates of seroprevalence.

Choosing a serological test has enormous implications. Good examples of this are the Amazonian cities of Manaus (Brazil) and Iquitos (Peru) that were hit hard after the first epidemic wave. The estimated population seroprevalence was 76% in Manaus [START_REF] Buss | Three-quarters attack rate of SARS-CoV-2 in the Brazilian Amazon during a largely unmitigated epidemic[END_REF]) and 70% in Iquitos (Álvarez-Antonio et al.

2021

), using a chemiluminescent microparticle immunoassay to detect IgG to the SARS-CoV-2 nucleocapsid (N) protein (AdviseDx, Abbott) and a COVID-19 Rapid Test to detect IgG/Immunoglobulin M (IgM) to the SARS-CoV-2 Spike S1 subunit protein (Orient Gene, Biotech), respectively. The prevalence in those cities was above the theoretical herd immunity threshold (67%) [START_REF] Fontanet | COVID-19 herd immunity: where are we?[END_REF]; however, a second wave of cases was observed soon after 7 months [START_REF] Sabino | Resurgence of COVID-19 in Manaus, Brazil, despite high seroprevalence[END_REF]. The questions that remained to be answered are: Was the herd immunity achieved in those communities? How quickly did levels of immunity decrease? Were new strains evading pre-existing immune responses? Did the commercial diagnostic tests overestimate the population exposure in the first epidemic wave?

To understand the transmission dynamics of SARS-CoV-2 in a population, better tools are needed. The knowledge from malaria serology can be translated to COVID19. For instance, on the fundamental aspects of humoral immunity, multiplex antibody assays could provide detailed information about antibody kinetics, cross-reaction with other human-coronaviruses, estimate the time since last exposure and correlates of clinical protection. On the epidemiological side, a multiplex antibody assay could give data to estimate the exposure to SARS-CoV-2 and associated risk factors at the population level, map transmission clusters and estimate lethality rate more accurately.

Research questions and objectives

After a decade of significant gains, Plasmodium vivax is still the dominant Plasmodium parasite in Peru. Key to its elimination is the ability to detect and treat people with asymptomatic blood-stage infections and those that carry dormant liver-stage infections. To better target these hidden stages, it is essential to gain a better understanding of the epidemiology of relapsing infections in different areas and develop diagnostic tools that will be able to identify people that are likely carrier of hypnozoites in their livers.

Malaria transmission in the Peruvian Amazon is heterogeneous, and this challenges the implementation of sustainable control measures, but it is also an opportunity for the introduction of novel tools and intervention strategies.

Our group recently validated a serological assay to measure antibodies to a panel of P. vivax antigens selected to identify people with both ongoing and past infections in the last nine months (Longley et al. 2020). This antigen panel has been validated in low transmission sites and can be deployed to measure the impact of interventions and target treatment in close to elimination settings (Longley et al. 2020).

As Peru has started a national malaria control and elimination program, this new serological assay could be a suitable tool for estimating transmission changes and measuring the effectiveness of control measures. They may also be used to identify and treat people harboring hidden liver-stage infections. To do so in all of Peru, these markers need to work not only in very low transmission settings but also in higher transmission settings in the Amazon region. Therefore, it is essential to validate these new serological markers in settings with moderate transmission like the Peruvian Amazon.

As this thesis work progressed, SARS-CoV-2 emerged and attacked many communities in the department of Loreto, already plagued by infectious diseases like malaria. Therefore, this thesis also aims to contribute to the development of a tool that could be applied to map the spread of SARS-CoV-2 those communities.

This thesis aims to answer the following research questions:

-What is the longevity of antibody responses to SEM in regions with a moderate transmission of vivax malaria?

-What factors drive the antibody responses to P. vivax in population in the Peruvian Amazon?

-Is serology a better tool than molecular testing to estimate exposure of vivax in the Peruvian Amazon?

-Can we characterize the vivax malaria transmission structure in the Peruvian Amazon?

-How can we transfer the knowledge gained on vivax serology to SARS-CoV-2 serology?

For answering these research questions, three objectives were established:

1) Validate the utility of a recently developed sero-diagnostic assay for identifying people at high risk of relapsing P. vivax infections in longitudinal studies conducted in the Peruvian Amazon. Compare the antibody responses measured in the Peruvian cohort to those obtained in longitudinal cohorts from areas with low transmission.

2) Measure the exposure to P. vivax by molecular and serological assays and determine risk factors in cross-sectional surveys executed in low and moderate transmission areas of Loreto (peri-urban and rural areas).

3) Develop a multiplex serological assay for SARS-Cov-2 by translating the platform developed for the P.

vivax antibody assay.

Authors and Affiliations

Jason Rosado 1, 2 , Michael T. White 1 , Rhea J. Longley 3,4 , Marcus Lacerda 5,6 , Wuelton Monteiro 6 , Jessica Brewster 3 , Jetsumon Sattabongkot 7 , Mitchel Guzman-Guzman 9 , Alejandro Llanos-Cuentas 8 , Joseph M. Vinetz 8,9,10,11 , Dionicia Gamboa 9,11 , Ivo Mueller

Methodology

We validated a panel of 34 SEMs in a Peruvian cohort with up to three years' longitudinal follow-up using a multiplex platform and compared results to data from cohorts in Thailand and Brazil. Linear regression models were used to characterize the association between antibody responses and age, the number of detected blood-stage infections during follow-up, and time since previous infection. Receiver Operating Characteristic (ROC) analysis was used to test the performance of SEMs to identify P. vivax infections in the previous 9 months.

Principal findings

Antibody titers were associated with age, the number of blood-stage infections, and time since previous P.

vivax infection in all three study sites. The association between antibody titers and time since previous P.

vivax infection was stronger in the low transmission settings of Thailand and Brazil compared to the higher transmission setting in Peru. Of the SEMs tested, antibody responses to RBP2b had the highest performance for classifying recent exposure in all sites, with area under the ROC curve (AUC) = 0.83 in Thailand, AUC = 0.79 in Brazil, and AUC = 0.68 in Peru.

Conclusions

In low transmission settings, P. vivax SEMs can accurately identify individuals with recent blood-stage infections. In higher transmission settings, the accuracy of this approach diminishes substantially. We recommend using P. vivax SEMs in low transmission settings pursuing malaria elimination, but they are likely to be less effective in high transmission settings focused on malaria control.

Author Summary

Plasmodium vivax still poses a threat in many countries due to its ability to cause recurrent infections. Key to achieving the goal of malaria elimination is the ability to quickly detect and treat carriers of relapsing parasites. Failing to identify this transmission reservoir will hinder progress towards malaria elimination.

Recently, novel serological exposure markers (SEM) to P. vivax have been developed and validated in low transmission settings. It is still poorly understood what factors affect the antibody response to these markers when evaluated in contrasting endemic contexts. To determine the factors that influence the antibody response to SEM, we compared the antibody levels in three sites with different transmission intensity: Thailand (low), Brazil (moderate) and Peru (high). In this study, we found that transmission intensity plays a key role in the acquisition of the antibody repertoire to P. vivax. In highly endemic sites, it is likely that immunological memory resulting from a constant and sustained exposure will impact the performance of SEMs to detect individuals with recent exposure to P. vivax. In summary, SEMs that perform well in low transmission sites do not perform as well in high transmission regions.

Introduction

Plasmodium vivax is the most geographically widespread Plasmodium species and the second largest cause of clinical malaria worldwide. Although the global burden has decreased from an estimated 24.5 million cases in 2000 to 7.5 million cases in 2018, P. vivax remains a challenging parasite to control and eliminate due to its biology, notably its ability to relapse from dormant liver-stage hypnozoites [START_REF] White | Relapse[END_REF], and its high transmission potential caused by rapid production of gametocytes and short development time in the mosquito vector (Mueller et al. 2009a).

In tropical regions, most P. vivax relapses occur within 9 months of the initial mosquito bite, with longer intervals observed in temperate regions and parts of the sub-tropics [START_REF] White | Relapse[END_REF]. Except in India, relapses typically account for greater than 80% of all P. vivax blood-stage infections [START_REF] Robinson | Strategies for understanding and reducing the Plasmodium vivax and Plasmodium ovale hypnozoite reservoir in Papua New Guinean children: a randomised placebocontrolled trial and mathematical model[END_REF].

Moreover, in cross-sectional surveys 90-100% of infections are asymptomatic [START_REF] Anstey | Plasmodium vivax: clinical spectrum, risk factors and pathogenesis[END_REF]) and up to 67% of infections are not detected by conventional screening tools such as light microscopy [START_REF] Cheng | Systematic review of sub-microscopic P. vivax infections: prevalence and determining factors[END_REF] because of low parasite density [START_REF] Lin | Differential patterns of infection and disease with P. falciparum and P. vivax in young Papua New Guinean children[END_REF], Mueller et al. 2013). Although the role of asymptomatic infections is not well understood, all P. vivax blood-stage infections produce gametocytes and may contribute to maintaining transmission [START_REF] Wampfler | Strategies for detection of Plasmodium species gametocytes[END_REF].

As the number of malaria cases in a region decreases and transmission becomes more heterogeneous, monitoring and mapping of residual transmission pockets becomes increasingly important. Many countries have declared the ambitious goal of eliminating malaria by 2030, and thus, a tool able to identify residual transmission or document the absence of recent transmission is urgently needed. This tool would ideally detect the entire P. vivax infectious reservoir composed of individuals with asymptomatic blood-stage infections as well as silent hypnozoite carriers.

Light microscopy (LM) and quantitative Polymerase chain reaction (qPCR), although informative, are imperfect tools due to the poor sensitivity for detecting low density blood-stage infections (LM) and the inability to detect hypnozoite carriers (LM and qPCR). The antibody responses mounted to P. vivax can last weeks to several years after exposure [START_REF] Wipasa | Long-lived antibody and B Cell memory responses to the human malaria parasites, Plasmodium falciparum and Plasmodium vivax[END_REF][START_REF] Kochayoo | The persistence of naturally acquired antibodies and memory B cells specific to rhoptry proteins of Plasmodium vivax in patients from areas of low malaria transmission[END_REF], making them an ideal tool for assessing transmission history. Thus, by exploiting antibody longevity to assess recent exposure, we can use antibody levels to estimate the time since previous exposure and potentially detect hypnozoite carriers (Longley et al. 2020).

Serology has been used to estimate malaria risk and endemicity at the population level, detecting temporal and spatial variation in malaria exposure [START_REF] Drakeley | Estimating medium-and long-term trends in malaria transmission by using serological markers of malaria exposure[END_REF], and evaluating malaria control efforts in areas where transmission has decreased to low levels [START_REF] Drakeley | Chapter 5. Potential contribution of sero-epidemiological analysis for monitoring malaria control and elimination: historical and current perspectives[END_REF]. Therefore, serological markers are an appealing tool in the context of malaria elimination because population-level serological signatures can provide insights of both current and recent exposure to malaria when parasite prevalence surveys are no longer (cost-)efficient because of the small proportion of individuals with detectable blood-stage infection.

The lack of standardized methods and antigens has hindered the implementation of serology as a tool for malaria surveillance. Recently, novel serological exposure markers (SEM) to P. vivax have been developed

and validated in low transmission settings showing a promising application in detecting likely hypnozoite carriers (Longley et al. 2020). Their performance at higher transmission levels is yet to be clarified. It is particularly important to understand what factors cause variation in SEM's performance before their application at a population level. Here, using a sero-epidemiological approach, we compare the performance of SEMs to detect recent exposure to P. vivax in three different transmission settings.

Epidemiological factors such as age, number of detected blood-stage infections and time since the previous infection were evaluated to understand how they affect SEM performance.

Methods

Ethics

The 2020a). This cohort initially enrolled 1029 participants that were all followed up with monthly population (community) screening (mPS), weekly active case detection (wACD) and continuous passive case detection (PCD) for 12 months. A subsample of 456 individuals was followed up with monthly mPS for another 24 months. Passive case detection relied on care-seeking behavior of individuals with malaria-compatible symptoms at health posts, where axillary temperature and microscopy-directed treatment was done. The mPS were conducted in the first week of each calendar month and involved finger prick-blood sampling for microscopy and dried blood spots. The home-to-home screenings relied on wACDs in the remaining weeks of each month and collected blood samples for microscopy from participants who had malaria-compatible symptoms within the past 7 days. Only participants with microscopically confirmed infections during PCD or ACD were treated with Chloroquine for 3 days (10 mg/g on days 1 and 2, and 5 mg/kg on day 3) plus primaquine for 7 days (0.5 mg/kg per day). Treatment adherence was monitored by trained staff in health posts when in PCD. In contrast to PCD patients, ACD patients self-reported a full treatment adherence (Rosas-Aguirre et al. 2020a). In our study, we selected those participants with serum sample collected at the end of year 3, and at least complete qPCR data for the last 12 months of follow-up (n = 590). This subset included 456 participants with at least 35 qPCR results in the three-year follow-up and 244 participants with at least 12 qPCRs results in the last year of follow-up (37 active case detection visits in total) (Appendix

Fig).

The participants with at least 12 qPCRs results were participants with incomplete blood samples in the first two years of follow-up. The PCR prevalence at the beginning of the cohort was 16% for P.

vivax and 2% for P. falciparum [START_REF] Carrasco-Escobar | High prevalence of very-low Plasmodium falciparum and Plasmodium vivax parasitaemia carriers in the Peruvian Amazon: insights into local and occupational mobility-related transmission[END_REF].

Data from the Thai and Brazilian cohorts had been reported previously (Longley et al. 2020). Briefly, yearlong observational cohort studies were conducted over 2013-2014 in Kanchanaburi/Ratchaburi provinces, Thailand [START_REF] Nguitragool | Highly heterogeneous residual malaria risk in western Thailand[END_REF], and in Brasileirinho, Ipiranga and Puraquequara, three peri-urban communities in Manaus province, Amazonas State [START_REF] Monteiro | Prevalence and force of Plasmodium vivax and Plasmodium falciparum blood stage infection and associated clinical malaria burden in the Brazilian Amazon[END_REF]. 999 volunteers of all ages were enrolled from Thailand and sampled every month over the year-long cohort, with 14 active case detection visits performed in total. 826 volunteers attended the final visit (Appendix Figure 3.1). From 2400 inhabitants surveyed in November 2012 in the three communities of Manaus, a total of 1274 residents of all ages were enrolled in the study in April 2013, and sampled every month over the year-long period, with 13 active case detection visits performed in total. 928 volunteers attended the final visit with plasma from 925 available (Appendix Figure 3.1). At the end of the follow-up, the PCR prevalence of P. vivax was 3.0%

in Thailand, and 4.2% in Brazil (Appendix Table 3.1), while the cumulative PCR prevalence of P. vivax was 11.7% and 25.43% in Thailand and Brazil, respectively. All cohort participants provided written informed consent for both participation in the study and future use of samples in studies of antimalarial antibody responses. Parental written consent and written assent were obtained in case of child participants.

Sample collection and molecular diagnosis

In the Peruvian cohort, blood samples were collected by finger prick onto filter paper and left to dry at room temperature. Dried blood samples were stored at -20 °C prior to molecular diagnosis. In the last visit, whole blood samples were collected and serum was separated by centrifugation and kept at -80°C until processing. DNA was isolated from dried blood samples using the E.Z.

Multiplex serological evaluation

Serum samples from the Peruvian cohort were evaluated following the protocol reported by Longley et al (Longley et al. 2017a). Briefly, proteins were coupled to non-magnetic microspheres. Protein-coupled microspheres were incubated with plasma (1/100 dilution in phosphate-buffered saline containing 1% bovine serum albumin and 0.05% (v/v) Tween-20, denoted as PBT) for 30 min at room temperature. After washing, microspheres were incubated for 15 minutes at room temperature with an R-Phycoerythrin (R-PE) -conjugated Donkey Anti-Human IgG antibody (cat#709-116-098; JacksonImmunoResearch, UK) to detect total IgG (1/100 dilution). Final washing was followed by resuspension of microspheres in 100 μL of PBT. This protocol was previously validated in other cohorts as well as in this cohort by testing a small group of samples in duplicate [START_REF] Franca | Identification of highly-protective combinations of Plasmodium vivax recombinant proteins for vaccine development[END_REF], Longley et al. 2017a, Longley et al. 2020). Therefore, serum samples were evaluated in singlicate format (one well per sample).

34 expressed full-length proteins were tested (See Appendix Table 3.2 for details). They were downselected from a larger panel as previously reported (Longley et al. 2020). 30 of the expressed proteins corresponded to erythrocytic stages, one to pre-erythrocytic stages, one to sexual stages, and one to a putative protein.

Plasma samples from healthy donors from non-malaria endemic regions (Melbourne, Australia, and Bangkok, Thailand) were used as negative controls (n = 274) (Longley et al. 2020) (noting that the control set from Rio, Brazil, was not included in the current study). A standard curve made of pooled plasma from hyper-immune Papua New Guinea adults (serial dilutions ranging from 1:50 to 1:51200) was used in each run for quality control and normalization purposes. The antibody measurements were performed in a Milliplex platform based on Luminex technology. Antibody levels were converted from median fluorescence intensity to relative antibody unit (RAU) using a 5-parameter logistic model written in R.

Published serological data from the Thai and Brazilian cohorts were compared to Peruvian antibody profiles (Longley et al. 2020).

Statistical analysis

All epidemiological data was obtained from the questionnaires or laboratory tests recorded in the databases from the cohorts in Peru, Thailand and Brazil. This study generated new monthly qPCR data and serological data from the final time point from the Peruvian cohort; however, we only used qPCR data of the 13 months preceding the last time-point. We characterized the association between antibody responses and PCR positive blood-stage infections in the Peruvian cohort by fitting multivariate linear regression models adjusted for confounders. The number of qPCR positive results or blood-stage detections was counted in both the previous six months before end of follow up, and in the first six months of follow up. A Bayesian Information Criterion (BIC) was used to select the model with the best explanatory variables. A retrospective analysis was performed taking into account the last time-point of follow up as a point of reference.

To test the performance of the SEMs to identify recent P. vivax exposure, individuals in the cohorts were categorized according to their history of blood-stage infection. Individuals with at least one qPCR positive P. vivax sample in the previous 9 months were classified as "recently exposed", whereas those with old infections (> 9 months) or no infections in the time of follow up (13 months for Peru and Brazil, 14 months for Thailand), were classified as "not recently exposed". Nine months was used because this cut-off takes into account that all P. vivax strain (with the possible exception of the hybernans strain now restricted to Korea) will experience their first relapse within no more than 9 months following the primary mosquitobite derived infection [START_REF] White | Relapse[END_REF]). Thus, a 9 months recent exposure period allows to capture almost all people carrying hyponzoites and thus at risk of experiencing a relapse. Receiver operating characteristic (ROC) curves were used to assess the trade-off between sensitivity and specificity of using single SEMs to identify recent infections. Sensitivity was defined as the proportion of people with recent infections with antibody titers higher than a given cut-off value. Specificity was defined as the proportion of subjects without recent infections with antibody titers lowers than the same cut-off value. We used the area under the ROC curve (AUC) value for comparing the test performance given by an antibody response between the three study sites.

Linear discriminant analysis (LDA) was performed to identify the best combinations of antibody responses for classifying recent infections. The algorithm for searching and validating the best antigen combination was described elsewhere (Longley et al. 2020). The AUC was calculated for each combination.

To determine the factors which drive variation in antibody responses, multivariate linear regression models were fitted using: (i) log10 (age) (a proxy of lifetime exposure); (ii) number of detected blood-stage infections during 13 months (Peru, Brazil) or 14 months (Thailand) of follow up; (iii) time since previous blood-stage infection; and (iv) an error term accounting for additional unexplained variation. Data on individuals with at least one blood-stage detection during 13 months was utilized for this purpose. The total variance of each marker was normalized to 1 and was the sum of variance given by both explained and unexplained variance, respectively. The contribution of each factor to the total variance was estimated using the "relaimpo" (https://CRAN.R-project.org/package=relaimpo) and "car" (https://CRAN.Rproject.org/package=car) R packages [START_REF] Grömping | Relative Importance for Linear Regression in R: The Package relaimpo[END_REF][START_REF] Fox | An {R} Companion to Applied Regression[END_REF]).

Statistical analysis was done using R Antibody responses were strongly correlated where antigens were located in the same parasite location,

showing a similar degree of immunogenicity and pattern of reactivity (Appendix Figure 3.3). For instance, antibody responses to invasion-related proteins (microneme proteins: PVX_110810A (PvDBP region II Sal1 strain), AAY34130.1 (PvDBP region II AH strain), PVX_095055 (PvRiPR), PVX_090240 (P. vivax Cysteine-Rich

Protective Antigen, PvCyRPA), and KMZ83376.1 (PvEBP region II); and rhoptry proteins: PVX_094255 (PvRBP2b), and PVX_098585 (PvRBP1a)) showed 50 to 98% correlation to each other, whereas, lower correlations were found with antigens of surface proteins (29 -66%).

To identify epidemiological factors that influence the response to SEMs, linear regression models were fitted using demographic and epidemiological data (Appendix Table 3 3.2, Appendix Table 3.9). Across all populations, the top ranking markers were the antibody responses to PVX_094255 (PvRBP2b), PVX_121920 (PvRBP2a), PVX_087885 (PvRAMA), PVX_099980

(PvMSP119) and PVX_097715 (hypothetical PvMSP3) (Appendix Table 3.2). Although the cohort studies were carried out in co-endemic sites for P. vivax and P. falciparum, we did not find an association between the antibody levels to the top five proteins and recent P. falciparum infection (Appendix Figure 3 With each cohort contributing a single data point, it is challenging to assess the significance of these relationships. Nonetheless, these exploratory findings lead us to think that these P. vivax SEMs are most

Discussion

The application of serology to malaria control and elimination programs has been hampered by the lack of standardized techniques, well-developed antigenic markers, and validation in various transmission scenarios. With the potential for malaria elimination in many endemic countries, a tool able to both identify pockets of residual transmission and certify the malaria free status (in elimination settings) is urgently needed. Here, we evaluated the antibody responses to 34 novel Plasmodium vivax antigens in participants from a longitudinal cohort implemented in a high transmission setting in Peru, and made comparisons to data from low to moderate transmission settings in Thailand and Brazil (Longley et al. 2020).

In the three cohorts, individuals with blood-stage infections detected in the previous 9 months had significantly higher antibody titers than those with older infections; however, the performance of SEM to classify recent exposure was lower in Peru than in Thailand and in Brazil. The effect of malaria transmission on antibody responses is still poorly understood [START_REF] Drakeley | Estimating medium-and long-term trends in malaria transmission by using serological markers of malaria exposure[END_REF], our data showed that antibody responses to serological markers of recent exposure are influenced by the transmission intensity of the studied population. Whilst the geometric mean titer of most antibody responses was positively correlated with the cumulative PCR prevalence of each study site, the differences of antibody titers between positive PCR and negative PCR groups were highly significant in Thailand and Brazil but less pronounced in Peru, suggesting a slow decay of antibody responses in currently non-infected people in high transmission settings. This is also evidenced by the comparatively high antibody responses in Peruvians with an absence of blood-stage infections during one year of follow-up. Furthermore, our results supported the fact that the acquisition rate of antibody responses is faster in high transmission intensity sites, a phenomenon previously described. For example, King et al. showed that the antibody response repertoire to P. vivax and P. falciparum antigens was broader with corresponding higher antibody levels in subjects residing in high vs low transmission conditions [START_REF] King | Biosignatures of Exposure/Transmission and Immunity[END_REF]. Anti-malaria antibodies are mostly short-lived [START_REF] Ndungu | Memory B cells are a more reliable archive for historical antimalarial responses than plasma antibodies in no-longer exposed children[END_REF]), but their longevity is antigen specific [START_REF] Akpogheneta | Duration of naturally acquired antibody responses to blood-stage Plasmodium falciparum is age dependent and antigen specific[END_REF]) and may vary according to the background immunity and transmission intensity of the studied population [START_REF] Elliott | Research priorities for the development and implementation of serological tools for malaria surveillance[END_REF], Longley et al. 2017b). In the cases of P. vivax Merozoite Surface Protein-1 (PvMSP1), antibody responses can last upto 30 years in sites where there has been sufficient levels of past exposure [START_REF] Lim | Humoral responses against the C-terminal region of merozoite surface protein 1 can be remembered for more than 30 years in persons exposed to Plasmodium vivax[END_REF]). The antibody longevity is maintained by memory B cells that upon re-exposure rapidly proliferate and differentiate into antibody secretory cells, greatly boosting antibody levels [START_REF] Good | Resting human memory B cells are intrinsically programmed for enhanced survival and responsiveness to diverse stimuli compared to naive B cells[END_REF]. In settings of low transmission, P. vivax infections appear to induce long-lasting B cell memory but with corresponding relatively shortlived antibody responses [START_REF] Wipasa | Long-lived antibody and B Cell memory responses to the human malaria parasites, Plasmodium falciparum and Plasmodium vivax[END_REF][START_REF] Kochayoo | The persistence of naturally acquired antibodies and memory B cells specific to rhoptry proteins of Plasmodium vivax in patients from areas of low malaria transmission[END_REF], although long-lived antibody responses have also been reported (Longley et al. 2016a). Although it has not been rigorously investigated, in high transmission settings both memory and antibody responses appear to be long-lived [START_REF] Figueiredo | T follicular helper cells regulate the activation of B lymphocytes and antibody production during Plasmodium vivax infection[END_REF]. A plausible explanation of the high antibody levels in the Peruvian cohort is that frequent new infections and relapses would boost the antibody response baseline in the Peruvian population and that would result in a slow decay of these responses [START_REF] Chuquiyauri | Genome-Scale Protein Microarray Comparison of Human Antibody Responses in Plasmodium vivax Relapse and Reinfection[END_REF]. In light of these broad associations between transmission intensity and natural acquisition of immunity, we thus hypothesize that the differential performance of SEMs in the three study sites are likely due to the differences in malaria transmission intensity. In Thailand and Brazil, where the prevalence is low, it is likely that the immune repertoire is smaller and antibody responses are more short-lived. In Peru, the high antibody titers in the cohort individuals suggests a consistently high exposure to P. vivax, which is expected to elicit a broad repertoire of long-lived antibodies.

In Besides previous life-time exposure, the immunogenicity of markers and their interplay with transmission intensity were factors that affected the SEM performance. For example, the levels of antibody responses to PvRBP2b were positively correlated with the number of blood-stage detections and time since previous infection in the three cohorts, indicating that multiple antigen exposure is needed in order to boost an antibody response, and thus showcasing the moderate immunogenicity of this antigen. PvRBP2b belongs to PvRBP family of membrane proteins involved in the irreversible adhesion to reticulocytes [START_REF] Galinski | A reticulocyte-binding protein complex of Plasmodium vivax merozoites[END_REF]). Reports on naturally acquired immunity against PvRBP2b have shown that antibody responses correlated with age, infection status and clinical protection [START_REF] Franca | Plasmodium vivax Reticulocyte Binding Proteins Are Key Targets of Naturally AcqCQCCWuired Immunity in Young Papua New Guinean Children[END_REF], Franca et al. 2017, He et al. 2019a). Previous reports have shown that antibodies responses against other members of the PvRBP family (i.e. PvRBP1a, PvRBP2a, PvRBP2c and PvRBP2-P2) also correlated with intensity of exposure and increased at a slower rate than antibodies against highly immunogenic antigens such as anti-PvDBPII [START_REF] Tran | Comparison of IgG reactivities to Plasmodium vivax merozoite invasion antigens in a Brazilian Amazon population[END_REF] or PvCSP (Longley et al. 2017a). Recently, longitudinal studies have provided estimates of the longevity of antibody response against PvRBP2b, estimating a mean half-life of ~ 3.8 months in cohorts from Thailand and Brazil (Longley et al. 2020), indicating the moderate longevity of antibodies even in low transmission settings. Importantly, Longley et al. showed that the antibody response to PvRBP2b had low reactivity in the malaria-naïve negative control group, which likely also has a strong impact on classification performance (Longley et al. 2020). These characteristics make anti-PvRBP2b antibodies the best performing SEMs for classifying recent exposure in all three cohorts.

The use of highly immunogenic antigens such as PvMSP119 in serological tools has been advised in areas of low transmission, where the long-term persistence of antibody levels may be suitable for estimating changes in transmission intensity [START_REF] Drakeley | Estimating medium-and long-term trends in malaria transmission by using serological markers of malaria exposure[END_REF]. Lowly immunogenic markers with shorter half-life antibodies like PvCyRPA would be suitable for estimating the time of the last infection in high endemicity scenarios. Our antigen panel consists of constructs whose antibody responses longevity spanned up to 6 months in the absence of detectable recurrent infections in longitudinal cohorts in two low transmission sites (Longley et al. 2017b) and whose performance has been validated to detect recent exposure in the context of low transmission intensity (Longley et al. 2020). Our new findings suggest that the reactivity to these markers may not necessarily reflect recent exposure in high-transmission scenarios like Peru, and that the constant, long-term exposure to P. vivax may result in the long-lived IgG profiles in the absence of ongoing P. vivax infection in that cohort. Measurement of antibodies in longitudinal samples from the Peruvian cohort in the future would confirm our results and shed light on the antibody kinetics to P. vivax in a higher transmission scenario.

Although our work did not address the parasite genetic diversity on these cohorts, we reckon that parasite genetics could play a role in the variation of antibody responses between cohorts [START_REF] Hupalo | Population genomics studies identify signatures of global dispersal and drug resistance in Plasmodium vivax[END_REF] or Tafenoquine in elimination campaigns (Longley et al. 2020). Such an intervention could reduce P.

vivax cases with efficacy similar to mass drug administration (MDA), but with the benefit of lower rates of overtreatment.

Finally, our study highlights the importance of an in-depth immunological dissection of the antibody signatures in diverse transmission contexts of P. vivax. Such an exploration will allow us to understand the antibody kinetics in a context of ongoing transmission and to select suitable markers of recent exposure in high transmission settings. Field studies have shown that IgG subclass profiles to P. falciparum [START_REF] Tongren | Target antigen, age, and duration of antigen exposure independently regulate immunoglobulin G subclass switching in malaria[END_REF]) and P. vivax [START_REF] Morais | Antibodies to Plasmodium vivax apical membrane antigen 1: persistence and correlation with malaria transmission intensity[END_REF][START_REF] Fernandez-Becerra | Naturally-acquired humoral immune responses against the N-and Ctermini of the Plasmodium vivax MSP1 protein in endemic regions of Brazil and Papua New Guinea using a multiplex assay[END_REF], He et al. 2019b) differed among malaria antigens and that the subclass predominance is influenced by age and increasing exposure to infection. High IgG1 and IgG3 levels have been associated with long-term exposure to P. vivax [START_REF] Morais | Antibodies to Plasmodium vivax apical membrane antigen 1: persistence and correlation with malaria transmission intensity[END_REF][START_REF] Franca | Plasmodium vivax Reticulocyte Binding Proteins Are Key Targets of Naturally AcqCQCCWuired Immunity in Young Papua New Guinean Children[END_REF]). To improve performance of SEMs, further information could potentially be gained by testing IgG subclass responses and their ability to discriminate recent from distant exposure to P. vivax in areas with history of high past transmission.

In summary, we have shown that the antibody responses to SEMs reflect exposure in the previous 9 months in areas of low transmission, whereas the responses will mirror a combination of both recent and cumulative exposure in areas of high transmission. By combining both highly and less immunogenic antigens, our panel is able to detect recent exposure in low transmission or pre-elimination settings, providing a suitable tool for population-level surveillance.

Chapter 4 : Malaria transmission structure in the Peruvian Amazon through antibody signatures to Plasmodium vivax

Authors and Affiliations

Jason Rosado 1, 2 , Gabriel Carrasco-Escobar 3,4 , Oscar Nolasco 5,6 , Katherine Garro 6 , Hugo Rodriguez-Ferruci 7 , Mitchel Guzman-Guzman 8 , Alejandro Llanos-Cuentas 5 , Joseph M. Vinetz 5,8,9 , Narimane Nekkab 1 , Michael T. White 1 , Ivo Mueller 1,10,11 , Dionicia Gamboa

*Correspondence

Jason Rosado, e-mail: javier.rosado@pasteur.fr This chapter will be submitted for publication after further revision.

Abstract

Background

The landscape of malaria transmission in the Peruvian Amazon is temporally and spatially heterogeneous, presenting different micro-geographies with particular epidemiologies. Most cases are asymptomatic and escape routine malaria surveillance based on light microscopy (LM). Following the implementation of control programs in this region, new approaches to stratify transmission and direct efforts at an individual and community level are needed. Antibody responses to serological exposure markers (SEM) to P. vivax have proven diagnostic performance to identify people exposed in the previous 9 months.

Methodology

We measured antibody responses against 8 SEM to identify recently exposed people and determine the transmission dynamics of P. vivax in peri-urban (Iquitos) and riverine (Mazán) communities of Loreto, two communities that have seen significant recent reductions in transmission. Socio-demographic, georeference, LM and PCR diagnosis data were collected from cross-sectional surveys. Spatial and multilevel analyses were implemented to describe the distribution of seropositive cases and the risk factors associated with exposure to P. vivax.

Principal findings

Low local transmission was detected by PCR in both Iquitos (5.3%) and Mazán (2.7%); however, seroprevalence indicated a higher level of (past) exposure to P. vivax in Mazan (56.5%) than Iquitos (38.2%).

Age and being male were factors associated with high odds of being seropositive in both sites. High antibody levels were found in particular in >15 years old population of these communities. The persistence of long-lived antibodies in these individuals could overestimate the detection of recent exposure. Antibody levels in <15 years population could be a better indicator of recent exposure to P. vivax.

Introduction

Peru is a country with low transmission of malaria, with 24,000 cases reported in 2019 (Ministerio de Salud Therefore, detecting transmission changes using LM or PCR data may overlook P. vivax very-low-density infections and hypnozoite carriers, thus underestimating transmission levels.

New uses of malaria serological tools have been proposed to inform the impact of such control measures [START_REF] Greenhouse | Priority use cases for antibodydetecting assays of recent malaria exposure as tools to achieve and sustain malaria elimination[END_REF]. Ideally, the detection of short-lived antibody responses in a given population's inhabitants will indicate that recent exposure has happened. Recently, new serological markers of exposure to P. vivax have been developed and validated for their use in low transmission settings (Longley et al. 2020). Although with lower performance than in low endemic settings, these serological markers of exposure were able to detect recent exposure in participants from longitudinal cohorts of road and riverine communities with moderate-to-high transmission of the Peruvian Amazon [START_REF] Rosado | Heterogeneity in response to serological exposure markers of recent Plasmodium vivax infections in contrasting epidemiological contexts[END_REF].

Given

Study design

In April 2018, 933 individuals were enrolled to participate in the study Circles of Research on Arboviruses and Malaria (CAM) executed in the 3 communities of Iquitos mentioned above. In this study, convenience sampling was implemented to the household members presented at the moment of survey. These communities were selected due to their high incidence of P. vivax according to the MINSA reports of 2017, and due to ease of sample transport to the reference laboratory installed in Iquitos city. A total of 358 households were surveyed. From the enrolled participants, 895 individuals consented to provide a blood A population survey and geo-referencing of households were implemented in communities from both areas, and data were collected through surveys on mobile devices. Axillary temperature was recorded.

Microscopy and molecular diagnosis

Whole blood samples (5 mL) were collected by a venous puncture in citrate tubes. Serum was separated by centrifugation and kept at -80°C until processing. Red blood cell pellets were stored at -20°C prior to molecular diagnosis.

Thick and thin blood smears were stained for 10 min with a 10% Giemsa solution. A slide was declared negative if no malaria parasite was found after examining 100 microscopy fields (Ministerio de Salud del Perú 2003). Slides were examined by a trained technician at the reference laboratory in Iquitos. All positive slides and 10% of negative slides were confirmed with a second reading by a proficient technician at the reference laboratory in Iquitos.

DNA was isolated from red blood cell pellets using the E.Z.N.A. Blood DNA Mini Kit (Omega Bio-tek, Inc., Norcross, GA, US), according to the manufacturer's instructions. Subsequent amplification was performed by a real-time quantitative PCR (qPCR) method using PerfeCTa SYBR Green FastMix 1250 (Quanta bio, Beverly, MA, US) as previously described (Rosas-Aguirre et al. 2017).

Antibody measurements

IgG antibody responses to 8 serological exposure markers (SEM) to P. vivax were measured using a Luminex® platform, as described elsewhere (Longley et al. 2017a). The 8 SEM panel has been previously validated (Longley et al. 2020) and consisted of the following proteins: PVX_099980 (19 kDa C-terminal region of merozoite surface protein 1, PvMSP119), PVX_096995 (tryptophan-rich antigen, Pv-fam-a), PVX_112670, PVX_097625 (merozoite surface protein 8, putative, PvMSP8), PVX_097720 (merozoite surface protein 3, PvMSP3.10), PVX_087885 (rhoptry-associated membrane antigen, putative, PvRAMA), PVX_094255 (reticulocyte binding protein 2b, PvRBP2b) and KMZ83376.1d (erythrocyte-binding protein II, PvEBPII). Additionally, antibodies to four more proteins were screened: PvCyRPA, PvDBPSal1, Pvs16 and PvMSP7.

To normalise and minimise inter-plate variation, a standard curve was prepared using a plasma pool from hyper-immune adults from Papua New Guinea. Relative Antibody Units (RAU) or dilutions were obtained extrapolating the Median Fluorescence Intensity (MFI) in a standard curve by a 5-parameter logistic model written in R.

Participants were classified as seropositive or seronegative by a seropositivity classifier basedon the combined antibody responses to SEM. The seropositivity classifier is a Random Forests based classification algorithm previously validated in low P. vivax transmission contexts (Longley et al. 2020). The algorithm code is available on https://github.com/MWhite-InstitutPasteur/Pvivax_sero_dx.

Data analysis

Demographic data was collected from the databases of the CAM study and the baseline survey of the cohort executed by the Amazonian ICEMR. Epidemiological characteristics between areas were compared using the χ 2 test or one-way ANOVA test.

The proportion seropositive for P. vivax SEM was estimated as the proportion of individuals classified as seropositive stratified by age. The seropositive rate of individuals per household was estimated to visualise the spatial distribution of seroprevalence to P. vivax. For the spatial data processing and visualisation, QGIS 2.16 (QGIS Development Team, 2016. QGIS Geographic Information System. Open Source Geospatial Foundation Project) was used.

To understand the epidemiological factors associated with P. vivax exposure and P. vivax parasitaemia by qPCR in these communities, generalised linear mixed effect models (multilevel multivariate) were fitted for each area. The following potential risk factors were assessed as fixed effects: age, sex, fever, travels in the last month, outdoor occupation, education level, livestock inside dwelling, indoor spray, and housing type. Factors in final models were selected by manual backward elimination guided by the minimisation of the Akaike information criterion. Three levels were included in these models: individuals within households, households within communities and communities. Intra-class correlation (ICC) was estimated as a measure of correlation and homogeneity at each level [START_REF] Merlo | A brief conceptual tutorial of multilevel analysis in social epidemiology: linking the statistical concept of clustering to the idea of contextual phenomenon[END_REF]. The ICC quantifies the degree of homogeneity of the outcome within clusters. If ICC=1, there is no within-class variation. If ICC=0, the observations do not depend on cluster membership. The lme4 R package was utilised for this analysis [START_REF] Bates | Fitting Linear Mixed-Effects Models Using lme4[END_REF]. When exploring the prevalence of P. vivax by community, we found high heterogeneity within study sites (Figure 4.2). Both LM and qPCR prevalence had similar spatial patterns in both study sites (Figure 4.2 A-B).

Results

Socio-demographic characteristics

For better interpretation of ongoing infections in this study, we refer only to the qPCR data from now on.

In Iquitos, Rumococha showed higher PCR prevalence (9.83%, 29/295) Seropositivity rate was 0.93 in participants aged 15-29.9 years from the village of Libertad (0.93, 95CI: 0.82, 1.00), and 42% in Santo Tomás (0.42, 95CI: 0.31-0.53) (Figure 4.3). In Mazán, the risk of being seropositive to P. vivax increased with increasing age (OR5-14. There was no association with fever symptom and seropositivity, as well with the travel history in the last month and the type of house (Appendix Table 4.3). The high ICC value for community, in contrast to households, indicated that seropositivity of participants was mostly affected by community factors or that there was homogeneity within communities, e.g. closeness to water bodies. This possibly indicates a more uniform and stable local transmission pattern (Appendix Table 4.4).

In the peri-urban area of Iquitos, the risk to P. vivax seropositivity increased with increasing age (OR5-14.9 There was an association between being male and being seropositive to P. vivax (OR = 1.49; 95CI =1.03-2.16; p-value = 0.03). Having a high education level (either secondary school or higher) was a protective factor (OR = 0.61; 95CI = 0.40-0.93; p-value = 0.02). History of travel in the past month was no associated to seropositivity. The ICC values indicated that the household variable explains considerably more variance of seropositivity to P. vivax. This possibly indicates a heterogeneous individual exposure that could be related to job-related habits rather than to community factors (Appendix Table 4.4).

No factors were significantly associated with high or low risk of parasitaemia in Mazán and Iquitos (Appendix Table 4.5). On the other hand, the ICC showed that households within community and community levels contributed equally to the variance for P. vivax parasitaemia in Mazán. In contrast, households within the community explained more of the variance for P. vivax parasitaemia in Iquitos (Appendix Table 4.6).

Discussion

This study presents measures of P. vivax seroprevalence and their determinants in two different epidemiological contexts of the Peruvian Amazon based on antibody responses to 8 markers of recent exposure (SEM). Individuals were classified into seropositive or seronegative by a machine-learning algorithm using the combined antibody responses to SEM. The high seroprevalence in Iquitos and Mazan suggested that there was intense exposure in both communities in the recent past [START_REF] Chuquiyauri | Socio-demographics and the development of malaria elimination strategies in the low transmission setting[END_REF][START_REF] Saavedra | Higher risk of malaria transmission outdoors than indoors by Nyssorhynchus darlingi in riverine communities in the Peruvian Amazon[END_REF], Rosas-Aguirre et al. 2021).

However, it was challenging to tell whether the estimated seroprevalence was due to recent or lifetime exposure. In high transmission settings, the detection of recently exposed individuals is challenging due to long-lived antibodies linked to lifetime exposure [START_REF] Rosado | Heterogeneity in response to serological exposure markers of recent Plasmodium vivax infections in contrasting epidemiological contexts[END_REF]. Antibody titers to SEM are boosted by recent infections even in moderate and high transmission, and therefore, high titres do provide evidence for recent exposure. In particular, high antibody levels in young people could indicate recent exposure, as their lifetime exposure is lower than adults [START_REF] Rosado | Heterogeneity in response to serological exposure markers of recent Plasmodium vivax infections in contrasting epidemiological contexts[END_REF]). To better understand recent exposure in these settings, we could rely on the antibody levels in the younger groups.

The choice of tools to detect exposure to P. vivax could have a significant impact on the control measures implemented by health authorities. For instance, in population screening studies in Mazán, only 23% of the infections were detected by LM-based active case detection, compared to PCR [START_REF] Moreno-Gutierrez | Effectiveness of a Malaria Surveillance Strategy Based on Active Case Detection during High Transmission Season in the Peruvian Amazon[END_REF]. Also, of the PCR positive infections, 57% were asymptomatic [START_REF] Moreno-Gutierrez | Effectiveness of a Malaria Surveillance Strategy Based on Active Case Detection during High Transmission Season in the Peruvian Amazon[END_REF]. In low transmission settings, where antibodies to SEMs represent exposure in a defined period of 9 months, SEMs outperform PCR to detect transmission patterns as it is both more sensitive and less susceptible to transmission seasonality than PCR [START_REF] Greenhouse | Priority use cases for antibodydetecting assays of recent malaria exposure as tools to achieve and sustain malaria elimination[END_REF]). In our study, compared to PCR, SEM detected up to 4 and 11 times more of the <15 years participants recently exposed to P. vivax in Iquitos and Mazán, respectively. However, our study showed that in areas where after long periods of moderate-to-high transmission that then rapidly goes to low transmission, antibodies remain at high levels for several years after transmission has been reduced. As a consequence, they are not well suited to measure short-term changes in transmission in areas where populations have established high levels of immunity. This could impact the use of antibodies to measure the impact of interventions [START_REF] Greenhouse | Priority use cases for antibodydetecting assays of recent malaria exposure as tools to achieve and sustain malaria elimination[END_REF]. We thus recommended that in areas where transmission has shifted from high-moderate to low levels like Iquitos and Mazán, SEMs are best suited to detect recent exposure in young people.

A limitation of this study is the lack of information about recent malaria episodes in the study survey to explain the communities' transmission patterns better. The low proportion of participants sampled per household in Iquitos could have overestimated the seropositive rate per household and thus the differences between households detected by our multilevel logistic model. This sampling bias may have also affected the distribution of seropositive cases or maps of seropositivity.

Overall, this study confirms that malaria transmission in the Peruvian Amazon is heterogeneous and that geographical, socio-demographic and human behaviour are important factors for the maintenance of disease burden in this region. Our findings have implications for designing appropriate interventions against malaria in the region; these novel serological markers could be deployed as a tool to target individual and community interventions, but care needs to be taken with their use in areas where transmission has been reduced rapidly from high to low transmission.

Abstract

Background

Infection with SARS-CoV-2 induces an antibody response targeting multiple antigens that changes over time. This study aims to take advantage of this complexity to develop more accurate serological diagnostics. A mathematical model of antibody kinetics informed by prior information from other coronaviruses was used to estimate time-varying antibody responses and assess the sensitivity and classification performance of serological diagnostics during the first year following symptom onset. A statistical estimator is presented that can provide estimates of seroprevalence in very low transmission settings.

Methods

Findings

IgG antibody responses to trimeric Spike protein identified individuals with previous RT-qPCR confirmed SARS-CoV-2 infection with 91.6% sensitivity (95confidence interval (CI); 87.5%, 94.5%) and 99.1% specificity (95% CI; 97.4%, 99.7%). Using a serological signature of IgG and IgM to multiple antigens, it was possible to identify infected individuals with 98.8% sensitivity (95% CI; 96.5%, 99.6%) and 99.3% specificity (95% CI; 97.6%, 99.8%). Informed by prior data from other coronaviruses, we estimate that one year following infection, a monoplex assay with optimal anti-S tri IgG cutoff has 88.7% sensitivity (95% CI: 63.4%, 97.4%),

and that a multiplex assay can increase sensitivity to 96.4% (95% CI: 80.9%, 100.0%). When applied to population-level serological surveys, statistical analysis of multiplex data allows estimation of seroprevalence levels less than 1%, below the false positivity rate of many other assays.

Introduction

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing coronavirus disease 2019 (COVID-19) emerged in Wuhan, China in December 2019. Since then, it has spread rapidly, with confirmed cases being recorded in nearly every country in the world. The presence of viral infection can be directly detected via reverse transcriptase quantitative PCR (RT-qPCR) on samples from nasopharyngeal or throat swabs [START_REF] Siordia | Epidemiology and clinical features of COVID-19: A review of current literature[END_REF], To et al. 2020). In many countries neither mild cases nor asymptomatic cases will be tested by RT-qPCR (unless they are direct contacts of known cases), and even among tested individuals many may be viremia negative at time of testing due to low viral load. While not suitable for diagnosis of clinical cases, serology is a promising tool for identifying individuals with previous infection by detecting antibodies generated in response to SARS-CoV-2. However, the utility of serological testing depends on the kinetics of the anti-SARS-CoV-2 antibody response during and after infection.

An individual is seropositive to a pathogen if they have detectable antibodies specific for that pathogen. In practice, serological assays are used to measure antibody responses in blood samples. However, individuals who have never been infected with the target pathogen may have non-zero antibody responses due to cross-reactivity with other pathogens or background assay noise. To account for this, defining seropositivity is equivalent to determining whether the measured antibody responses is greater or lower than some defined cutoff value.

The most fundamental measure of antibody level is via concentration in a sample (e.g. in units of μg/mL), however a measurement in terms of molecular mass per volume is usually impossible to obtain. Instead, a range of assays can provide measurements that are positively associated with the true antibody concentration, e.g. an optical density from an enzyme-linked immunosorbent assay (ELISA), or a median fluorescent intensity (MFI) from a Luminex® microsphere assay. In contrast to the continuous measurement of antibody response provided by laboratory-based research assays, most point-of-care serological tests provide a binary outcome: seronegative or seropositive. There are several commercially available tests for detecting SARS-CoV-2 antibody responses, which are being catalogued by FIND Diagnostics (FIND 2020). These tests are typically based on lateral flow assays mounted in plastic cartridges which detect antibodies in small volume blood samples. A key feature of many rapid tests is that they are dependent on the choice of seropositivity cutoff, and there may be substantial misclassification for antibody levels close to this cutoff.

Antibody levels are not constant, and change over time. The early kinetics of the antibody response to SARS-CoV-2 have been well documented with a rapid rise in antibody levels occurring 5-15 days after symptom onset leading to seroconversion (depending on the choice of cutoff) [START_REF] Borremans | Quantifying antibody kinetics and RNA detection during early-phase SARS-CoV-2 infection by time since symptom onset[END_REF], To et al. 2020). Assuming the long-term kinetics of the SARS-CoV-2 antibody response are similar to other pathogens (Andraud et al. 2012, White et al. 2014, Teunis et al. 2016), we expect to observe a bi-phasic pattern of decay, with rapid decay in the first 3-6 months after infection, followed by a slower rate of decay.

Notably, this decay pattern may lead to seroreversion whereby previously seropositive individuals revert to seronegative. If a serological test with an inappropriately high choice of cutoff is used for SARS-CoV-2 serological surveys, there is a major risk that seroreversion may lead to previously infected individuals testing seronegative [START_REF] Adams | Antibody testing for COVID-19: A report from the National COVID Scientific Advisory Panel[END_REF]).

The antibody response generated following SARS-CoV-2 infection is diverse, consisting of multiple isotypes targeting several proteins on the virus including Spike (and its receptor binding domain, RBD) and Nucleoprotein [START_REF] Hachim | ORF8 and ORF3b antibodies are accurate serological markers of early and late SARS-CoV-2 infection[END_REF]. This diversity of biomarkers provides both a challenge and an opportunity for diagnostics research. The challenge lies in selecting appropriate biomarkers and choosing between the increasing number of commercial assays, many of which have not been extensively validated and may produce conflicting results. The opportunity is that with multiple biomarkers, it is possible to generate a serological signature of infection that is robust to how antibody levels change over time [START_REF] Helb | Novel serologic biomarkers provide accurate estimates of recent Plasmodium falciparum exposure for individuals and communities[END_REF][START_REF] Perraut | Serological signatures of declining exposure following intensification of integrated malaria control in two rural Senegalese communities[END_REF][START_REF] Azman | Estimating cholera incidence with cross-sectional serology[END_REF], Longley et al. 2020), rather than relying on classification of seropositive individuals using a single cutoff antibody level.

In this analysis, we apply mathematical models of antibody kinetics and machine learning classifiers to identify serological signatures of SARS-CoV-2 infection generated using multiplex assays, which can provide accurate serological diagnosis that is robust to the waning of antibody levels over time.

Methods

Samples

We analysed 97 serum samples from 53 patients admitted to hospitals in Paris, and 162 serum samples from healthcare workers in hospitals in Strasbourg, with SARS-CoV-2 infection confirmed by RT-qPCR and mostly mild symptoms (Fafi-Kremer et al. 2020[START_REF] Hadjadj | Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients[END_REF][START_REF] Lescure | Clinical and virological data of the first cases of COVID-19 in Europe: a case series[END_REF]) (Table 5.1).

Negative controls samples collected before December 2019 were selected from panels representative of the target population (177 serum samples from French blood donors), plus geographically diverse panels (68 plasma samples from the Thai Red Cross, 90 serum samples from Peruvian healthy donors). All samples underwent a viral inactivation protocol by heating at 56 °C for 30 minutes. The potential effect of the viral inactivation protocol on the measurement of antibody levels was assessed using serum positive for antimalaria antibodies. IgG antibody levels were measured in matched samples before and after the inactivation protocol. The viral inactivation protocol did not affect measured IgG levels (Appendix Figure Positive control serum samples are from patients with RT-qPCR confirmed SARS-CoV-2 infection. Negative control samples are from panels of pre-epidemic cohorts with ethical approval for broad antibody testing. Age is presented as median and range.

Antibody responses to multiple antigens can be combined to identify individuals with previous SARS-CoV-2 infection using statistical classification or machine learning algorithms (Longley et al. 2020). A random forests algorithm was selected due to its superior classification performance over statistical classifiers such as logistic regression (Appendix Figure 5.3). Classification algorithms were implemented in R version 3.4.3.

Uncertainty in sensitivity and specificity is quantified in three ways: (i) binomial confidence intervals calculated using Wilson's method; (ii) 1000-fold repeat cross-validation with a training set comprising 2/3 of the data and a disjoint testing set comprising 1/3 of the data; (iii) cross-panel validation with algorithms trained and tested on disjoint panels of data (Appendix Figure 5.4).

Mathematical model of antibody kinetics

SARS-CoV-2 antibody kinetics are described using a previously published mathematical model of the immunological processes underlying the generation and waning of antibody responses following infection (White et al. 2014). The existing model is adapted to account for the frequent data available in the first weeks of infection. Statistical inference was implemented within a mixed-effects framework allowing for characterisation of the kinetics within each individual while also describing the population-level patterns. On the population level, both the mean and variation in antibody kinetics are accounted for. Models were fitted in a Bayesian framework using Markov chain Monte Carlo methods with priors informed by estimates from long-term studies of antibody kinetics following infection with other coronaviruses (Appendix Table 5.4).

𝑑𝐵

Serological surveillance

A ROC curve obtained from a training data set consisting of positive and negative samples is described by a sequence of sensitivities and specificities {𝑠𝑒 𝑖 ̂, 𝑠𝑝 𝑖 ̂}. N-fold cross-validation generates samples of sensitivity {𝑠𝑒 𝑖1 , … , 𝑠𝑒 𝑖𝑁 } for each 𝑠𝑝 𝑖 ̂ and samples of specificity {𝑠𝑝 𝑖1 , … , 𝑠𝑝 𝑖𝑁 } for each 𝑠𝑒 𝑖 ̂. Following a previously outlined approach [START_REF] Diggle | Estimating Prevalence Using an Imperfect Test[END_REF][START_REF] Gelman | Bayesian analysis of tests with unknown specificity and sensitivity[END_REF], for each pair i of sensitivity and specificity, we obtain N estimates of the measured seroprevalence Min in a scenario with true seroprevalence T as follows:

𝑀 𝑖𝑛 = 𝑇𝑠𝑒 𝑖𝑛 + (1 -𝑇)(1 -𝑠𝑝 𝑖𝑛 )
The point estimates of sensitivity and specificity can be used to calculate an adjusted estimate of true seroprevalence:

𝑇 𝑖𝑛 ̂= 𝑀 𝑖𝑛 + 𝑠𝑒 𝑖 ̂-1 𝑠𝑒 𝑖 ̂+ 𝑠𝑝 𝑖 ̂-1 with 𝑇 𝑖𝑛 ̂= 0 if 𝑀 𝑖𝑛 < 1 -𝑠𝑒 𝑖 ̂.
Both 𝑀 𝑖 and 𝑇 𝑖 ̂ are summarized as medians with 95% ranges. We calculate the expected relative error as:

1 𝑁 ∑ |𝑇 𝑖𝑛 ̂-𝑇| 𝑇 𝑁 𝑛=1
5.4. Results

Single biomarker classification

For all 14 SARS-CoV-2 biomarkers (seven antigens, IgG and IgM for both), measured responses were significantly higher in samples with RT-qPCR confirmed infection than in negative control samples (Figure 5.1A-B; P value < 1 x 10 -7 ; 2 sided t-test).

The trade-off between sensitivity and specificity obtained by varying the cutoff for seropositivity was investigated using a ROC curve (Figure 5.1C-D). Depending on the characteristics of the desired diagnostic test, different targets for sensitivity and specificity can be considered. The results of three targets are summarized in Appendix -8 ). There was significant correlation between antibody responses against all SARS-CoV-2

antigens, but no significant correlation between antibody responses to SARS-CoV-2 and the seasonal coronaviruses 229E and NL63 (Figure 5.1F). The classification performance with random forests algorithms of multiplex combinations of antibody responses is shown with the ROC curves in Figure 5.2B. Including data from additional biomarkers leads to significant improvements in classification performance (Appendix Table 5.3), albeit with diminishing returns (Figure 5.2c). For example, for the high specificity target, with a single biomarker (anti-S tri IgG) we can achieve 91•6% sensitivity (95% CI: 87.5%, 94.5%). Including anti-RBDv2 IgG increases sensitivity to 95.6% sensitivity (95% CI: 92.3%, 97.5%). Combinations of size five to six provide 98•8% sensitivity (95% CI: 96.5%, 99.6%) and 99.3% sensitivity (95% CI: 97.6%, 99.8%).

SARS-CoV-2 antibody kinetics

For individuals with RT-qPCR SARS-CoV-2 infection, Appendix Figure 5.5 shows the model predicted IgG antibody response to SARS-CoV-2. For all antigens, we predict a bi-phasic pattern of waning with a first rapid phase between one and three months after symptom onset, followed by a slower rate of waning.

The percentage reduction in antibody level after one year was mostly determined by prior information and estimated to be 47% (95% CrI: 18%, 90%) for anti-S tri IgG antibodies, with comparable estimates for other antigens (Appendix Table 5.6). Sensitivity was assessed using the seropositivity cutoff based on a high specificity target (>99%). For all antigens considered, we predict that there will be a reduction in sensitivity over time, although there is a large degree of uncertainty (Appendix Figure 5.5).

We predict that multiplex diagnostic tests will have higher classification performance for longer durations of time than single antigen tests. In particular, the sensitivity based on anti-S tri IgG antibody responses after one year will be 88.7% (95% CrI: 63.4%, 97.4%); and that the sensitivity of a four antigen multiplex classifier after six months will be 96.4% (95% CrI: 80.9%, 100%) (Figure 5.3).

Multiplex assays for seroprevalence surveys

For serological diagnosis of individual samples, the target pursued is typically to optimize sensitivity whilst enforcing high specificity (>99%). A serological assay that accurately classifies individual samples will also perform well at estimating seroprevalence in populations. However, an assay optimized for individuallevel classification is not necessarily optimal for population-level surveillance where the target is to obtain accurate estimates of true seroprevalence. Figure 5.4A presents ROC curves for a monoplex anti-S tri IgG assay and a multiplex assay using six biomarkers (Appendix Table 5.3) with quantification of uncertainty via repeat cross-validation. In an epidemiological scenario with true seroprevalence = 5%, the measured seroprevalence will depend on the assay sensitivity and false positive rate (= 1 -specificity) (Figure 5.4B).

For high false positive rate, the measured seroprevalence overestimates the true seroprevalence.

Discussion

Infection with SARS-CoV-2 induces antibodies of multiple isotypes (IgG, IgM, IgA) targeting various epitopes, including Spike and Nucleoprotein (Grzelak et al. 2020). These biomarkers may exhibit distinct kinetics leading to variation in their diagnostic performance. By measuring multiple biomarkers in large numbers of individuals, it is possible to create a serological signature of previous infection [START_REF] Helb | Novel serologic biomarkers provide accurate estimates of recent Plasmodium falciparum exposure for individuals and communities[END_REF][START_REF] Perraut | Serological signatures of declining exposure following intensification of integrated malaria control in two rural Senegalese communities[END_REF][START_REF] Azman | Estimating cholera incidence with cross-sectional serology[END_REF], Longley et al. 2020), leading to more accurate serological classification of individuals recently infected with SARS-CoV-2 [START_REF] Becker | Going beyond clinical routine in SARS-CoV-2 antibody testing -A multiplex corona virus antibody test for the evaluation of cross-reactivity to endemic coronavirus antigens[END_REF][START_REF] Iyer | Dynamics and significance of the antibody response to SARS-CoV-2 infection[END_REF].

Although necessarily more complex than a single measured antibody response, such an approach has the potential of providing more accurate classification and being more stable over time.

IgG antibody levels to a single antigen (trimeric Spike) can classify samples from individuals previously infected with SARS-CoV-2 with 91.6% sensitivity (95% CI: 87.5%, 94.5%) and 99.1% specificity (95% CI:

97.4%, 99.7%). Measuring additional biomarkers with a multiplex assay can improve classification performance to 98.8% sensitivity (95% CI: 96.5%, 99.6%) and 99•3% specificity (95% CI: 97.6%, 99.8%). A similar phenomenon is observed for serological diagnosis of HIV where combining multiple assays can lead to improved accuracy (World Health Organisation 2011). Multiplex assays provide some of the benefits of combining separate assays, but are subject to the risk that multiple biomarkers measured on the same assay are often correlated.

The reported accuracy of serological tests depends on multiple factors, most notably the validation samples used. Specificity is typically determined by pre-epidemic negative control samples, with the inclusion of greater numbers of samples providing more robust characterization of specificity. Rather than taking large numbers of samples from a homogeneous population, we encourage the utilization of multiple negative control panels that are epidemiologically diverse with respect to age and location.

Sensitivity is determined by positive control samples. It may be trivial to record high sensitivity when validating with samples from small numbers of individuals with severe symptoms [START_REF] Takahashi | Are Seroprevalence Estimates for Severe Acute Respiratory Syndrome Coronavirus 2 Biased?[END_REF].

We encourage the use of multiple panels of positive control panels that are epidemiologically diverse with respect to factors such as age, COVID-19 symptom severity, and time since symptoms. When comparing the performance of different assays, the ideal approach is to use common serum samples. In the majority of situations where common serum samples are not available, including epidemiological information on validation samples can facilitate more effective comparison between assays.

Analysis of samples from individuals collected up to five months after the onset of COVID-19 symptoms indicates that antibody levels are maintained but with significant waning [START_REF] Gudbjartsson | Humoral Immune Response to SARS-CoV-2 in Iceland[END_REF][START_REF] Wheatley | Evolution of immunity to SARS-CoV-2[END_REF]. The long-term kinetics of the antibody response to SARS-CoV-2 will not be definitively quantified until infected individuals are followed longitudinally for months and even years after RT-qPCR confirmed infection. While these samples are being collected, mathematical models can provide important insights into how SARS-CoV-2 antibody levels may change over time. Modelling beyond the timeframe for which we have data has its limitations, however our Bayesian approach benefits from robust quantification of uncertainty accounting for a wide range of future scenarios. Furthermore, this modelling approach provides falsifiable predictions which will allow models to be updated as new data is generated.

The simulations presented here predict that following SARS-CoV-2 infection, antibody responses will increase rapidly 1-2 weeks after symptom onset, with antibody responses peaking within 2-4 weeks. After this peak, antibody responses are predicted to decline according to a bi-phasic pattern, with rapid decay in the first three to six months followed by a slower rate of decay. Model predictions of the rise and peak of antibody response are informed by, and are consistent with, many sources of data (Andraud et al. 2012, White et al. 2014, Teunis et al. 2016[START_REF] Gudbjartsson | Humoral Immune Response to SARS-CoV-2 in Iceland[END_REF][START_REF] Wheatley | Evolution of immunity to SARS-CoV-2[END_REF]. Model predictions of the decay of antibody responses are strongly determined by prior information on longitudinal followup of individuals infected with other coronaviruses (Callow et al. 1990). Under the scenario that the decay of SARS-CoV-2 antibody responses is similar to that of SARS-CoV-1, we would expect substantial reductions in antibody levels within the first year after infection. For the seropositivity cutoffs highlighted here, this could cause approximately 50% -90% of individuals to test seronegative after one year, depending on the exact choice of biomarker and seropositivity cut-off.

This presents a potential problem for SARS-CoV-2 serological diagnostics. Most commercially available diagnostic tests compare antibody responses to a fixed seropositivity cutoff. Where these cutoffs have been validated, it is typically by comparison of serum from negative control samples collected preepidemic with serum from hospitalized patients in the first weeks of infection (i.e. when antibody responses are likely to be at their highest) [START_REF] Long | Antibody responses to SARS-CoV-2 in COVID-19 patients: the perspective application of serological tests in clinical practice[END_REF]). If we fail to account for antibody kinetics, we risk incorrectly classifying individuals with old infections (e.g. >6 months) as seronegative. This is particularly important for point-of-care rapid serological tests with fixed cutoffs, limited dynamic range and visual evaluation. If inappropriate tests are used in seroprevalence surveys, there is a risk of substantial under-estimation of the proportion of previously infected individuals.

An advantage of continuous multiplex data is that different algorithms can be applied to the same data for different epidemiological applications. Appendix Table 5.3 assesses classification performance against three targets. We selected multiplex combination of antigens to optimize classification of individual samples against a target of maximizing sensitivity given a minimum specificity of 99%. However, a test that is optimal for individual-level classification is not necessarily optimal for population-level use. A recommended target for serological assays for sero-surveillance surveys is to minimize the expected error in estimated seroprevalence. For scenarios where we expect low true seroprevalence (<10%) assays with high specificity (>99%) are optimal (Figure 4). Notably this provides a potential solution to the challenge of implementing sero-surveillance studies in regions where seroprevalence is expected to be lower than commonly reported false positive rates [START_REF] Bryant | Serology for SARS-CoV-2: Apprehensions, opportunities, and the path forward[END_REF]. This is possible because our assay allows 100% specificity to be achieved with an accompanying reduction in sensitivity that can be statistically accounted for. In low seroprevalence settings there are additional challenges in collecting sufficient numbers of samples to ensure statistically robust estimates [START_REF] Larremore | Estimating SARS-CoV-2 seroprevalence and epidemiological parameters with uncertainty from serological surveys[END_REF]).

The analysis presented here is based on limited data, and the predictions may subsequently be contradicted as more data become available. However, the concepts outlined here of serological sensitivity and 99.3% specificity. This study also brought insights into how eventual antibody decay could affect the diagnostic performance of serological tools.

In the following sections, the main findings are discussed and put into context to address the research questions established by this thesis.

Longevity of antibody responses to Plasmodium vivax in regions with moderate and high transmission of vivax malaria

Antibody levels mirror the transmission of vivax malaria in the population. In the longitudinal study (Chapter 3), we found that participants from Cahuide and Lupuna had higher geometric mean antibody titers than those from low transmission sites of Thailand and Brazil. We also detected differences in immunity levels between Amazonian communities: higher antibody levels in participants from Lupuna than Cahuide, and higher antibody levels in participants from Mazán than peri-urban Iquitos (Chapter 4).

Both longitudinal and cross-sectional studies showed that this difference might be related to the history of transmission. Higher antibody levels were found in communities with historical intense community transmission. This result was expected since both Lupuna and communities from Mazán were riverine villages with high entomological indices during the study period [START_REF] Prussing | Decreasing proportion of Anopheles darlingi biting outdoors between long-lasting insecticidal net distributions in peri-Iquitos, Amazonian Peru[END_REF][START_REF] Saavedra | Higher risk of malaria transmission outdoors than indoors by Nyssorhynchus darlingi in riverine communities in the Peruvian Amazon[END_REF]. Our results corroborated those published by King and Pires et al. regarding the antibody levels and their relationship with transmission level. King and collaborators showed that inhabitants from sites with high endemicity had a broader antibody repertoire with corresponding higher antibody reactivity than inhabitants from low endemicity sites [START_REF] King | Biosignatures of Exposure/Transmission and Immunity[END_REF]. On the other hand, Pires et al. reported higher antibody levels to P. vivax in individuals from riverine populations than in individuals from non-riverine populations in the Brazilian Amazon [START_REF] Pires | Blood-stage Plasmodium vivax antibody dynamics in a low transmission setting: A nine year follow-up study in the Amazon region[END_REF].

While anti-malaria antibodies are mostly short-lived [START_REF] Ndungu | Memory B cells are a more reliable archive for historical antimalarial responses than plasma antibodies in no-longer exposed children[END_REF], their longevity is antigenspecific [START_REF] Akpogheneta | Duration of naturally acquired antibody responses to blood-stage Plasmodium falciparum is age dependent and antigen specific[END_REF]) and may vary according to the background immunity and transmission intensity of the studied population (Longley et al. 2017b). Our work showed that in settings with moderate/high transmission of vivax malaria, antibodies could persist longer after P. vivax infection.

Individuals with no PCR detectable infection in Cahuide and Lupuna had considerably higher antibody levels than PCR negative individuals from low transmission sites. In low transmission settings, P. vivax infections appear to induce long-lasting B cell memory but with corresponding relatively short-lived antibody responses [START_REF] Kochayoo | The persistence of naturally acquired antibodies and memory B cells specific to rhoptry proteins of Plasmodium vivax in patients from areas of low malaria transmission[END_REF]. In high transmission settings, both memory and antibody responses appear to be long-lived [START_REF] Figueiredo | T follicular helper cells regulate the activation of B lymphocytes and antibody production during Plasmodium vivax infection[END_REF], Longley et al. 2017b). Our findings indicated that in high transmission settings like Cahuide and Lupuna, and in populations that historically have been exposed to P. vivax like Iquitos and Mazán, the antibody decay is slower than in populations with low or no transmission. A plausible explanation of the high antibody levels and long-lived antibodies in these Peruvian communities is that frequent new infections and hypnozoite relapses would boost the antibody responses in those communities, resulting in a slow decay of these responses [START_REF] Chuquiyauri | Genome-Scale Protein Microarray Comparison of Human Antibody Responses in Plasmodium vivax Relapse and Reinfection[END_REF]).

Longevity of antibody responses to Plasmodium vivax in regions with moderate and high transmission of vivax malaria

In the first study of this thesis, a novel serological assay for identifying hypnozoite carriers was validated in populations of the Peruvian Amazon. Following the rationale that first relapses mainly occur within the first nine months after primary P. vivax infections, the novel serological assay developed by our team was able to identify potential hypnozoite carriers in populations with low malaria transmission (AUC = 0.8) (Longley et al. 2020). Unlike in low transmission settings, this antibody assay showed a lower performance to detect individuals exposed in the last nine months in the communities of Cahuide and Lupuna (AUC = 0.7).

The monthly PCR data and the measured antibodies were crucial to identify individuals exposed to P. vivax in the last year of follow-up. We found that antibody responses were positively correlated with increasing age and the number of detected blood-stage infections in the preceding year. Antibody levels were negatively correlated to the time elapsed since the last infection, i.e. individuals with old infections had lower levels than individuals with recent infections. Thus, our results provide evidence that even in high transmission contexts, antibody responses to P. vivax decay with time, although at a slower rate than in low transmission contexts.

Our multivariate linear regression models showed that age (an indicator of lifetime exposure) was the strongest explanatory factor for antibodies to 32 out of 34 P. vivax antigens in these Peruvian communities. This result was expected as increasing antibody levels with age indicates the cumulated immunity in populations historically exposed to P. vivax [START_REF] Mueller | Natural acquisition of immunity to Plasmodium vivax: epidemiological observations and potential targets[END_REF][START_REF] Rosas-Aguirre | Hotspots of Malaria Transmission in the Peruvian Amazon: Rapid Assessment through a Parasitological and Serological Survey[END_REF].

However, this association could also reflect the nature of recent transmission in this population. The identification of recently exposed people in these sites is challenging due to the cumulative nature of anti-P. vivax immunity in heavily exposed populations. Setting cut-off antibody levels specific for age groups could be an approach worth trying; i.e., setting a cut-off adapted to population <15 years and another cut-off to >15 years could help identify recently exposed people and deploy intervention measures to targeted population. Nevertheless, as our findings showed, in a population with intense historical transmission like these communities in the Peruvian Amazon, the presence of long-lived antibodies in all age groups may negatively impact the identification of people suitable for hypnozoiticidal treatment. burden accounted for by hypnozoite relapses (Longley et al. 2020).

Moving from control to elimination requires national programs to implement detection strategies of both symptomatic and asymptomatic cases in the community. In populations with high immunity levels, P.

vivax infections with very low parasitaemias are frequently seen in periods of low transmission [START_REF] Mueller | Natural acquisition of immunity to Plasmodium vivax: epidemiological observations and potential targets[END_REF][START_REF] Rosas-Aguirre | Micro-heterogeneity of malaria transmission in the Peruvian Amazon: a baseline assessment underlying a population-based cohort study[END_REF], Rosas-Aguirre et al. 2020a). If we implemented a PCR-based activecase detection, we could miss the detection of recent transmission in sites like Mazán. Alternatively, suppose we implemented a serology-based active-case detection. In that case, we could target areas where low parasitaemia infections are undetectable by PCR, and antibodies show that transmission is still ongoing [START_REF] Greenhouse | Priority use cases for antibodydetecting assays of recent malaria exposure as tools to achieve and sustain malaria elimination[END_REF]. Certainly, serological surveillance would be a supplementary measure that will accompany other activities such as vector control, entomological surveillance, health education and targeted drug administration [START_REF] Greenhouse | Priority use cases for antibodydetecting assays of recent malaria exposure as tools to achieve and sustain malaria elimination[END_REF].

Unlike parasite prevalence by PCR, serology is less sensitive to transmission seasonality, i.e. it is a tool that can be applied in surveys during or after transmission season with less effect on estimates of population exposure [START_REF] Cook | Sero-epidemiological evaluation of changes in Plasmodium falciparum and Plasmodium vivax transmission patterns over the rainy season in Cambodia[END_REF], Kerkhof et al. 2016a, Wang et al. 2016[START_REF] Greenhouse | Taking Sharper Pictures of Malaria with CAMERAs: Combined Antibodies to Measure Exposure Recency Assays[END_REF][START_REF] Greenhouse | Priority use cases for antibodydetecting assays of recent malaria exposure as tools to achieve and sustain malaria elimination[END_REF]. In our second study (Chapter 4), we found a higher PCR prevalence in the peri-urban area of Iquitos than in the rural area of Mazán. The high seroprevalence and antibody levels in Mazán, on the other hand, showed the high magnitude of exposure in these communities in contrast to Iquitos. This high PCR prevalence in Iquitos could be attributable to the timing of sampling. The survey in Iquitos was conducted during transmission season (May 2018); whereas, the survey in Mazán was done at the end of transmission season (June 2018). There is no study of the change in antibody responses over the transmission season in the Peruvian Amazon; however, a couple of studies carried out in populations with forest-related transmission in Cambodia did not find substantial changes during and after the transmission peak. For instance, Cook and collaborators showed that seroprevalence to PvMSP119 did not significantly change between the middle and the end of transmission season (17% vs 12%) in Ratanakiri and Pailin, Cambodia. Also, Kerkhof et al. did not find variation in antibody levels to PvMSP119 and PvAMA1 measured over low transmission season in two consecutive years across communities of Ratanakiri, Cambodia (Kerkhof et al. 2016a). Measuring seroprevalence with highly immunogenic antigens such as PvMSP119 and PvAMA1 over the transmission season may not show significant changes, but simultaneously measured antibody levels to several less immunogenic antigens may better reflect changes linked to seasonality. Whilst we have not evaluated the variation of antibody responses to these novel P. vivax SEM during and after transmission season, we hypothesised that little changes between peak and low seasons could be detected, likely due to relapsing infections.

In the first study (Chapter 3), we showed that antibody levels could be used to detect exposure to P. vivax in the last nine months. We only needed a transversal serum sample to reconstruct the history of exposure of the studied cohort. Otherwise, if we had opted to use molecular tests to do so, we would have needed several consecutive molecular tests with narrow sampling timeframes to detect all blood-stage infections.

Serology is, therefore, a potentially cost-effective tool that requires less expensive resources than molecular tests. As described above, the age distribution of antibody levels at the population level reflects the temporality of exposure (recent or past) and the transmission nature (local or occupational). Our findings showed a scenario of current local low transmission in the villages of Iquitos and Mazán. The prevalence of antibodies to SEM in <15 years old male and female participants from Iquitos indicated that P. vivax exposure might be related to peri-domestic infections. In Mazán, < 15 years old participants had higher antibody levels than children from Iquitos, thus indicating intense local transmission in this site. These data support earlier publications on the P. vivax transmission in these communities [START_REF] Chuquiyauri | Socio-demographics and the development of malaria elimination strategies in the low transmission setting[END_REF][START_REF] Rosas-Aguirre | Hotspots of Malaria Transmission in the Peruvian Amazon: Rapid Assessment through a Parasitological and Serological Survey[END_REF], Carrasco-Escobar et al. 2017[START_REF] Saavedra | Higher risk of malaria transmission outdoors than indoors by Nyssorhynchus darlingi in riverine communities in the Peruvian Amazon[END_REF]).

In the past, the seroconversion rate (SCR), based on seropositivity by age to a single Plasmodium immunogenic antigen, was proposed as a surrogate of traditional malaria metrics such as entomological inoculation rate and parasite rate [START_REF] Greenhouse | Taking Sharper Pictures of Malaria with CAMERAs: Combined Antibodies to Measure Exposure Recency Assays[END_REF]. However, the use of SCR would be limited if we used high immunogenic antigens in regions with historical high transmission intensity, such as Lupuna or Mazán. In high transmission sites, the changes in antibody levels to high immunogenic antigens over time would be minor as they will induce long-lived antibodies [START_REF] Wipasa | Long-lived antibody and B Cell memory responses to the human malaria parasites, Plasmodium falciparum and Plasmodium vivax[END_REF][START_REF] Yman | Antibody responses to merozoite antigens after natural Plasmodium falciparum infection: kinetics and longevity in absence of re-exposure[END_REF]. In a recent study carried out in the Gambia, no change in seroconversion to antigens associated with longlived antibody responses (PfMSP119, PfAMA1, PfGLURP.R2) was detected between the start and the end of the transmission season, and pre-and post-mass drug administration (MDA) campaigns (Wu et al. 2020b). Nonetheless, Wu and collaborators showed that age-stratified antibody levels of shorter-lived antibody responses detected subtle changes of parasite exposure during transmission season (Wu et al. 2020b) and can also detect changes post-MDA campaigns [START_REF] Wu | Serological evaluation of a cluster randomised trial on the use of reactive focal mass drug administration and reactive vector control to reduce malaria transmission in Zambezi Region, Namibia[END_REF]. Measuring multiple antibody levels to less immunogenic antigens has the advantage of better estimate recent exposure through shorter-lived antibody responses.

Unlike previous studies on malaria serology, our approach estimates the antibody acquisition rate to a combined group of markers associated with short-lived antibody responses in low transmission settings.

Our study showed that in areas that transit rapidly from long periods of moderate-to-high transmission rapidly to low transmission, antibodies remain at high levels for several years after transmission has been reduced. As a consequence, SEMs are not well suited to measure short-term changes in transmission in areas where populations have established high levels of immunity. This could impact the use of antibodies to measure the impact of interventions [START_REF] Greenhouse | Priority use cases for antibodydetecting assays of recent malaria exposure as tools to achieve and sustain malaria elimination[END_REF]. We thus recommended that in areas where transmission has shifted from high-moderate to low levels like Iquitos and Mazán, SEMs are best suited to detect recent exposure in young people.

Lessons for SARS-CoV-2 serology

The immune response to SARS-CoV-2 consists of multiple isotypes targeting several proteins on the virus, including Spike (and its receptor binding domain, RBD) and Nucleocapsid [START_REF] Hachim | ORF8 and ORF3b antibodies are accurate serological markers of early and late SARS-CoV-2 infection[END_REF]. Antibodies mounted against SARS-CoV-2 increase rapidly between the 5-15 days after symptom onset [START_REF] Borremans | Quantifying antibody kinetics and RNA detection during early-phase SARS-CoV-2 infection by time since symptom onset[END_REF], To et al. 2020); and decay during the subsequent months [START_REF] Pelleau | Serological reconstruction of COVID-19 epidemics through analysis of antibody kinetics to SARS-CoV-2 proteins[END_REF]. Anti-SARS-CoV-2

antibodies follow a similar kinetic to other pathogens, like Plasmodium (White et al. 2014), with a bi-phasic decay pattern, with a rapid decay in the first 3-6 months after infection, followed by a slower rate decay.

Our third study (Chapter 5) provided a multiplex antibody assay and classification algorithm to detect exposure to SARS-CoV-2, following the lessons learnt from vivax malaria serology. This tool showed great potential to investigate antibody kinetics following SARS-CoV-2 infections.

The inclusion of a single antigen (Trimeric Spike) was able to detect individuals exposed to SARS-CoV-2 with 91.6% sensitivity and 99.1% specificity. Adding more antigens in our multiplex assay improved detection to 98.8% sensitivity and 99.3% specificity. Although adding more antigens provides more accurate classification, there is a risk that some antibody responses are correlated.

Contrary to the P. vivax multiplex antibody assay, the high sensitivity and specificity of the SARS-CoV-2 antibody assay are related to the lack of background immunity to SARS-CoV-2 in pre-pandemic negative controls. However, when the pandemic has passed and the world population has reached enough immunity to this virus, most infections will be undetectable by qPCR or antigenic surveillance tools. The background immunity to SARS-CoV-2 will challenge the detection of recent exposure in post-pandemic time. Our multiplex tool, maybe with more and better biomarkers, and algorithms calibrated with pandemic data, will be crucial to detect populations with recent exposure and implement case tracking to stop further spreading.

Analysis of samples from individuals collected up to five months after the onset of COVID-19 symptoms indicates that antibody levels are maintained but with significant waning [START_REF] Gudbjartsson | Humoral Immune Response to SARS-CoV-2 in Iceland[END_REF][START_REF] Wheatley | Evolution of immune responses to SARS-CoV-2 in mild-moderate COVID-19[END_REF]. We risk incorrectly classifying individuals with old infections (e.g. >6 months) as seronegative if we ignored antibody kinetics. Antibody waning is a threat from the diagnostic point of view because it could affect the sensitivity of detection of seropositive people after one year of infection, i.e. 50-90% of individuals would turn seronegative, depending on the choice of serological test.

Continuous multiplex antibody data has the advantage that different epidemiological applications can be addressed with different algorithms, such as population seroprevalence adjusted by sensitivity and specificity targeted.

The addition of other isotypes and a panel of antigens from other human coronaviruses to our antibody assay has now allowed investigation of the kinetics of anti-SARS-CoV-2 antibodies in longitudinal studies executed in Paris, Strasbourg and Dublin. For instance, one year after symptoms, we found that 36% of anti-S IgG remains, 31% of anti-RBD IgG remains, and 7% of anti-NP IgG remains [START_REF] Pelleau | Serological reconstruction of COVID-19 epidemics through analysis of antibody kinetics to SARS-CoV-2 proteins[END_REF]). IgM and Immunoglobulin A (IgA) waned more rapidly, with 9% of anti-RBD IgM and 10% of anti-RBD IgA remaining, respectively [START_REF] Pelleau | Serological reconstruction of COVID-19 epidemics through analysis of antibody kinetics to SARS-CoV-2 proteins[END_REF]. This updated antibody assay also allowed classification of time since infection into intervals of 0 -3 months, 3 -6 months, and 6 -12 months [START_REF] Pelleau | Serological reconstruction of COVID-19 epidemics through analysis of antibody kinetics to SARS-CoV-2 proteins[END_REF]).

The validation panel was essential to estimate the sensitivity and specificity of this tool. We encourage the use of multiple panels of positive control panels that are epidemiologically diverse concerning factors such as age, COVID-19 symptom severity, and time since symptoms. Regarding the negative control panel, we recommend using multiple negative control panels that are epidemiologically diverse with respect to age and location.

The expression system of recombinant antigens can affect the quality of responses detected in antibody assays. We found that proteins expressed in mammal cells systems had less background noise than proteins expressed in Escherichia coli systems; this may be due to the folding of proteins expressed in mammal cells or cross-reaction with some E. coli proteins. Although E. coli is a cost-effective system with huge yields compared to other systems, we encourage the use of proteins expressed in mammal cells system.

Knowing the time since the last infection with SARS-CoV-2 remains important because it will better estimate the lethality rate caused by this virus and provide more accurate estimates of reproduction number, detect people that are naturally immune, determine areas with active virus circulation, and identify correlates of protection. The knowledge obtained from these type of studies will be essential to face future pandemics.

Conclusions

Serology is an old tool but has more uses now more than ever. While its application to P. vivax epidemiology has many facets, this thesis gave clear examples of the data gained in terms of estimation of the level, the time and characterisation of vivax malaria transmission.

At the population level, antibody responses to novel SEM to P. vivax mirror transmission intensity (Figure

6.1.A, B)

. At the individual level, antibody responses to SEM provide a unique signature shaped by the previous exposure to the parasite. The two studies of this thesis conducted in the Peruvian Amazon showed that in sites like Lupuna and Mazán, where transmission was high and community-wide, antibody levels are higher than in communities like Cahuide and Iquitos, with a low level of transmission.

In high vivax transmission sites, antibody responses will last longer than in low-transmission sites and could reflect both recent and lifetime exposure (Figure 6

Perspectives

The findings of this thesis have motivated further research of novel markers to detect recent exposure to P. vivax in high transmission settings by our team in Institut Pasteur and colleagues in the Walter & Eliza Hall Institute in Melbourne, Australia.

In order to contribute to the control and elimination of malaria in the Peruvian Amazon and based on this thesis finding, we recommend the following approaches:

 The longevity of antibody responses plays an important role in determining who was exposed to P. vivax. The preferences for short-lived antibodies and less immunogenic antigens should be the rule, especially in settings with high transmission.

 When the Peruvian Amazon achieves zero transmission of malaria, detecting recent exposure to P. vivax via antibody responses to SEM will be a priority for targeted interventions [START_REF] Greenhouse | Priority use cases for antibodydetecting assays of recent malaria exposure as tools to achieve and sustain malaria elimination[END_REF]. It would be interesting to measure the immunity level in the communities covered by this thesis and determine how long it takes for long-lived antibodies to reach undetectable levels after decreasing transmission.

 As in other infectious diseases such as HIV, measuring the anti-P. vivax avidity index could help to determine more accurately recent exposure (< 9 months) from old exposure (> 9 months) in settings with high transmission of vivax malaria [START_REF] Suligoi | Avidity Index for anti-HIV antibodies: comparison between third-and fourthgeneration automated immunoassays[END_REF]. Antibody avidity reflects the overall strength of interaction between the antibody and antigen complex and correlates with protection in naturally acquired and vaccine-induced immunity to viral and bacterial pathogens.

This approach has been explored in P. falciparum, and the authors found that avidity to two P.

falciparum antigens was lower in areas of high transmission intensity than areas with lower transmission [START_REF] Ssewanyana | Avidity of anti-malarial antibodies inversely related to transmission intensity at three sites in Uganda[END_REF].

 High IgG1 and IgG3 levels have been associated with long-term exposure to P. vivax [START_REF] Morais | Antibodies to Plasmodium vivax apical membrane antigen 1: persistence and correlation with malaria transmission intensity[END_REF][START_REF] Franca | Plasmodium vivax Reticulocyte Binding Proteins Are Key Targets of Naturally AcqCQCCWuired Immunity in Young Papua New Guinean Children[END_REF]). To improve performance of SEMs, further information could potentially be gained by testing IgG subclass responses and their ability to discriminate recent from distant exposure to P. vivax in areas with a history of high past transmission.

 Designing longitudinal studies in high transmission areas with consecutive serum sampling is necessary to accurately define the longevity of antibody responses to P. vivax in those sites.

Concomitant genotyping of recurrent infections detected in these longitudinal studies could help to determine the contribution of new infections and relapses to the background immunity and antibody longevity. Moreover, integrating parasite genetic data into this study would help better understand if the humoral response to P. vivax is strain-specific or strain-transcending during peak and low transmission season.

 Measuring the longevity of antibody responses in people who have lived in high transmission sites but moved to places where there is no transmission of P. vivax. It would be interesting also to know how long it takes for antibodies to diminish after hypnozoite treatment in these individuals.

 With the ongoing need for SARS-CoV-2 serological surveillance, we recommend our approach to detect recent exposure in populations with low transmission. Farmer/Fishing a,c 33.17 14.55 38.32 Infection by qPCR at D0 ⱡ P. vivax (%) NL63 and 229E, and three antigens from other viruses (Influenza A H1N1, adenovirus type 40, rubella) for which a large part of the population is expected to be seropositive due to vaccination or natural infection and hence serve as internal controls (Appendix Table 5.1).

The assay was performed in black, 96 well, non-binding microtiter plate (cat#655090; Greiner Bio-One, Germany). Briefly 50 µL of protein-conjugated magnetic beads (500/region/µL) and 50 µL of diluted serum were mixed and incubated for 30 min at room temperature on a plate shaker. All dilutions were made in phosphate buffered saline containing 1% bovine serum albumin and 0•05% (v/v) Tween-20 (denoted as PBT), and all samples were run in singlicate. Following incubation, the magnetic beads were separated using magnetic plate separator (Luminex®) for 60 seconds and washed three times with 100 μL of PBT.

The washed magnetic beads were incubated for 15 minutes with detector secondary antibody at room temperature on a plate shaker. The magnetic beads were separated and washed three times with 100 μL of PBT and finally resuspended in 100 μL of PBT. Two separate assays were used for measuring IgG and IgM antibodies. For IgM measurements, serum samples were diluted 1/200, and R-Phycoerythrin (R-PE)conjugated Donkey Anti-Human IgM (cat#709-116-073; JacksonImmunoResearch, UK) antibody was used as secondary antibody at 1/400 dilution. For IgG, serum samples were diluted 1/100, and R-Phycoerythrin (R-PE) -conjugated Donkey Anti-Human IgG (cat#709-116-098; JacksonImmunoResearch, UK) antibody was used as secondary antibody at 1/120 dilution.

On each plate, two blanks (only beads, no serum) were included as well as a standard curve prepared from two-fold serial dilutions (1:50 to 1:25600) of a pool of positive controls. Plates were read using a

Luminex® MAGPIX® system and the median fluorescence intensity (MFI) was used for analysis. A 5parameter logistic curve was used to convert MFI to antibody dilution, relative to the standard curve performed on the same plate to account for inter-assay variations. The multiplex immunoassay was validated by checking that the MFI obtained were well correlated with those obtained in monoplex (only one conjugated bead type per well). For non-SARS-CoV-2 antigens, MFI data was used for the analysis.

The antigens included in the multiplex assay were selected with the aim of providing multiple measurements of the antibody response directed towards the Spike protein and the Nucleoprotein.

Factors affecting antigen selection included availability and timeliness. Thus for example, at the time when this assay was being developed our team had access to the nucleoprotein of two seasonal alphacoronaviruses (NL63, 229E), but we did not yet have access to antigens from the two seasonal betacoronaviruses (OC43, HKU1).

Selection of classification algorithms

In previous work on the analysis of data from a multiplex assay of Plasmodium vivax malaria antibody responses, we tested the performance of a number of different classification algorithms including logistic regression, linear discriminant analyses, quadratic discriminant analyses, decision trees, and random forests (Longley et al. 2020). This analysis found random forests algorithms to have the highest classification performance of the tested algorithms. On this basis, we selected random forests algorithms to classify previous SARS-CoV-2 infection using multiplex serological data.

To further test this, we compared the classification performance of a random forests algorithm with a logistic regression classifier. The data from positive and negative samples as detailed in

Uncertainty in classification performance

Uncertainty in sensitivity and specificity can be quantified in three ways: (i) binomial confidence intervals calculated using Wilson's method; (ii) 1000-fold repeat cross-validation with a training set comprising 2/3 of the data and a disjoint testing set comprising 1/3 of the data; (iii) cross-panel validation with algorithms trained and tested on disjoint panels of data.

For a single antigen (S tri ) IgG assay, and a six antigen multiplex assay with random forests classifier (Stri IgG + RBDv2 IgG + NPv1 IgG + S2 IgG + RBDv1 IgM + NPv1 IgM), Appendix Figure 3

Mathematical model of antibody kinetics

SARS-CoV-2 antibody kinetics are described using a previously published mathematical model of the immunological processes underlying the generation and waning of antibody responses following infection or vaccination (White et al. 2014a). The existing model is adapted to account for the frequent data available in the first weeks of infection. δ is the time after symptom onset when antibody levels start to increase. As g and B0 are not both identifiable, we estimate 𝛽 = 𝑔𝐵 0 . If r is the decay rate of antibody molecules, then we define 𝑑 𝑎 =

Methodology for statistical inference

The model was fitted to longitudinal antibody level measurements from all participants. Mixed effects methods were used to capture the natural variation in antibody kinetics between individual participants, whilst estimating the average value and variance of the immune parameters across the entire population of individuals. The models were fitted in a Bayesian framework using Markov Chain Monte Carlo (MCMC) methods. Mixed effects methods allow individual-level parameters to be estimated for each participant separately, with these individual-level (or mixed effects) parameters being drawn from global distributions. For example, for each participant n the half-life of the short-lived ASCs may be estimated as 𝑑 𝑠 𝑛 (an individual-level parameter). These N estimates of the local parameters 𝑑 𝑠 𝑛 will be drawn from a probability distribution. A log-Normal distribution is suitable as it has positive support on [0, ∞). Thus we have 𝑙𝑜𝑔(𝑑 𝑠 𝑛 )~𝑁(𝜇 𝑠 , 𝜎 𝑠 2 ). The mean ds and the variance Σ 𝑠 2 of the estimates of 𝑑 𝑠 𝑛 are given by 𝑑 𝑠 = 𝑒 𝜇 𝑠 + 𝜎 𝑠 2 2 and Σ 𝑠 2 = (𝑒 𝜎 𝑠 2 -1)𝑒 2𝜇 𝑠 +𝜎 𝑠 2 .

Model likelihood

For individual n we have data on observed antibody levels 𝐴 𝑛 = {𝑎 As the proportion of the ASCs that are long-lived must be bounded by 0 and 1, the individual-level parameters ρ n are assumed to be drawn from logit-Normal distributions.

Total model likelihood

Denote 𝐷 = {𝐷 1 , … , 𝐷 𝑁 } to be the vector of data for all N participants. We denote 𝜃 𝑛 = (𝜇 𝐴 , 𝜎 𝐴 , 𝜇 𝛿 , 𝜎 𝛿 , 𝜇 𝛽 , 𝜎 𝛽 , 𝜇 𝑏 , 𝜎 𝑏 , 𝜇 𝑠 , 𝜎 𝑠 , 𝜇 𝑙 , 𝜎 𝑙 , 𝜇 𝑎 , 𝜎 𝑎 , 𝜇 𝜌 , 𝜎 𝜌 , 𝜃 1 , … , 𝜃 𝑁 ) to be the combined vector of population-level parameters and individual-level parameters to be estimated. The total likelihood is obtained by multiplying the likelihood for each participant )

where 𝜇 𝜌,0 and 𝜏 𝜌,0 parameterise the Normal prior distribution on the mean, and k and θ parameterise the Gamma prior distribution on the precision (inverse standard deviation).

Observational variance parameter Metropolis-Hastings Update.

 Update the observational variance parameter 𝜎 𝑜𝑏𝑠 ′  Calculate updated total likelihood 𝐿 𝑡𝑜𝑡𝑎𝑙 (𝜃′|𝐷)and the updated prior probability density

𝑃(𝜃′)

 Accept the parameter update with probability 𝑚𝑖𝑛 (1, 𝐿 𝑡𝑜𝑡𝑎𝑙 (𝜃′|𝐷)𝑃(𝜃′) 𝐿 𝑡𝑜𝑡𝑎𝑙 (𝜃|𝐷)𝑃(𝜃) )

The MCMC algorithm was implemented in C++ complied in Microsoft Visual Studio. The covariance of the multivariate-Normal proposal distributions for Metropolis-Hastings updates were adaptively tuned using the estimated posterior distributions during a burn-in phase of 1 million MCMC iterations. The magnitude of the proposed step size was calibrated using a Robbins-Munro algorithm to ensure an acceptance rate of approximately 23%. The total number of MCMC iterations was 10,000,000. The effective number of iterations was calculated using the effectiveSize routine in the R library coda and the effective size was checked to be > 1,000 for all parameters.

  Figure 1.1. Temporal trends in Plasmodium vivax incidence and case counts from 2010 to 2019. .............. 3 Figure 1.2. Global distribution of predicted all-age clinical case count of Plasmodium vivax malaria for 2010 and 2019. ............................................................................................................................................... 4 Figure 1.3. Reported malaria cases in Peru from 2000 to 2019. .................................................................... 6 Figure 1.4. Cumulated incidence of P. vivax malaria in Peru at district level in 2020. ................................... 7 Figure 1.5. Life cycle of Plasmodium vivax and some targets of immune response. ...................................10 Figure 2.1. Cellular and humoral immune response during Plasmodium vivax infections. .........................19 Figure 2.2. Antibody signatures to P. vivax are shaped by lifetime exposure. .............................................25 Figure 2.3. Established and next-generation methods for evaluating malaria transmission via antibodies provide higher resolution than parasite prevalence. ...................................................................................27 Figure 2.4. Priority applications of serological markers of exposure as tools to achieve and sustain malaria elimination. ...................................................................................................................................................29 Figure 3.1.Prevalence of P. vivax infection in the Peruvian cohort. .............................................................46 Figure 3.2. Heatmap of antibody responses to P. vivax serological exposure markers in the Peruvian cohort. ...........................................................................................................................................................47 Figure 3.3. Association of antibody responses to PvMSP119, PvRBP2b and PvCyRPA with epidemiological features. ........................................................................................................................................................50 Figure 3.4. Diagnostic performance for classifying recent exposure using antibody responses. .................52 Figure 3.5. Contribution of recent and lifetime exposure to the variance of antibody responses to PvMSP119, PvRBP2b and PvCyRPA across the study sites. ............................................................................53 Figure 3.6. Association of cumulative PCR prevalence and antibody responses to PvMSP119, PvRBP2b and PvCyRPA. .......................................................................................................................................................54 Figure 4.1. Map of the studied sites. ............................................................................................................69 Figure 4.2. Prevalence of Plasmodium vivax measured by three tools in communities of Iquitos and Mazán............................................................................................................................................................72 Figure 4.3. Age trend of seropositivity to P. vivax in the studied communities. ..........................................74 Figure 4.4. Distribution of seropositives cases. Map of exposure to P. vivax in the study sites. .................76 Figure 4.5. Visual summary of estimates of multilevel regression models of P. vivax seropositivity and parasitaemia in Mazán and Iquitos. ..............................................................................................................78 Figure 5.1. Anti-SARS-CoV-2 antibody responses. ........................................................................................90 Figure 5.2. Serological signatures of SARS-CoV-2 infection. .........................................................................92 Figure 5.3. Model predicted sensitivity over time. .......................................................................................93 Figure 5.4. Implementation of seroprevalence surveys. ..............................................................................95 Figure 6.1. Main findings of the thesis. .......................................................................................................111

1 . 3 Figure 1 . 2 . 4 Figure 1 . 3 . 6 Figure 1 . 4 . 7 Figure 1 . 5 . 10 Figure 2 . 1 . 19 Figure 2 . 2 .Figure 2 . 3 .Figure 3 . 3 .Figure 3 . 4 .Figure 3 . 5 .Figure 3 . 6 .Figure 4 . 1 .Figure 4 . 2 .Figure 5 . 2 .Figure 5 . 3 .Figure 5 . 4 .Figure 6 . 1 .

 1312413614715102119222333343536414252535461 Figure 1.1. Temporal trends in Plasmodium vivax incidence and case counts from 2010 to 2019. .............. 3 Figure 1.2. Global distribution of predicted all-age clinical case count of Plasmodium vivax malaria for 2010 and 2019. ............................................................................................................................................... 4 Figure 1.3. Reported malaria cases in Peru from 2000 to 2019. .................................................................... 6 Figure 1.4. Cumulated incidence of P. vivax malaria in Peru at district level in 2020. ................................... 7 Figure 1.5. Life cycle of Plasmodium vivax and some targets of immune response. ...................................10 Figure 2.1. Cellular and humoral immune response during Plasmodium vivax infections. .........................19 Figure 2.2. Antibody signatures to P. vivax are shaped by lifetime exposure. .............................................25 Figure 2.3. Established and next-generation methods for evaluating malaria transmission via antibodies provide higher resolution than parasite prevalence. ...................................................................................27 Figure 2.4. Priority applications of serological markers of exposure as tools to achieve and sustain malaria elimination. ...................................................................................................................................................29 Figure 3.1.Prevalence of P. vivax infection in the Peruvian cohort. .............................................................46 Figure 3.2. Heatmap of antibody responses to P. vivax serological exposure markers in the Peruvian cohort. ...........................................................................................................................................................47 Figure 3.3. Association of antibody responses to PvMSP119, PvRBP2b and PvCyRPA with epidemiological features. ........................................................................................................................................................50 Figure 3.4. Diagnostic performance for classifying recent exposure using antibody responses. .................52 Figure 3.5. Contribution of recent and lifetime exposure to the variance of antibody responses to PvMSP119, PvRBP2b and PvCyRPA across the study sites. ............................................................................53 Figure 3.6. Association of cumulative PCR prevalence and antibody responses to PvMSP119, PvRBP2b and PvCyRPA. .......................................................................................................................................................54 Figure 4.1. Map of the studied sites. ............................................................................................................69 Figure 4.2. Prevalence of Plasmodium vivax measured by three tools in communities of Iquitos and Mazán............................................................................................................................................................72 Figure 4.3. Age trend of seropositivity to P. vivax in the studied communities. ..........................................74 Figure 4.4. Distribution of seropositives cases. Map of exposure to P. vivax in the study sites. .................76 Figure 4.5. Visual summary of estimates of multilevel regression models of P. vivax seropositivity and parasitaemia in Mazán and Iquitos. ..............................................................................................................78 Figure 5.1. Anti-SARS-CoV-2 antibody responses. ........................................................................................90 Figure 5.2. Serological signatures of SARS-CoV-2 infection. .........................................................................92 Figure 5.3. Model predicted sensitivity over time. .......................................................................................93 Figure 5.4. Implementation of seroprevalence surveys. ..............................................................................95 Figure 6.1. Main findings of the thesis. .......................................................................................................111

Figure 1 . 1 .

 11 Figure 1.1. Temporal trends in Plasmodium vivax incidence and case counts from 2010 to 2019. These plots show: A) predicted global incidence rate (clinical cases per 1,000 population per annum), and B) predicted global incidence counts (clinical cases) of Plasmodium vivax malaria. 95% uncertainty intervals are shown via the corresponding coloured bands behind the mean lines. Data extracted from Malaria Atlas Project (https://malariaatlas.org/)

Figure 1 . 2 .

 12 Figure 1.2. Global distribution of predicted all-age clinical case count of Plasmodium vivax malaria for 2010 and 2019.The number of cases predicted are shown on a spectrum of light blue to red. Areas where P. vivax is known to be endemic, but there was not sufficient information to generate a prediction are shown in light grey. These maps were created by Malaria Atlas Project (MAP) (https://malariaatlas.org/).

  epidemic in Loreto, and the historic Loreto flood of 2011-2012 that inundated and damaged many riverine communities (Rosas-Aguirre et al. 2016). With the implementation of the national malaria program "Malaria Cero" by the Minister of Health in 2017, the numbers have descended to 19 600 cases in 2019 (Ministerio de Salud Perú 2020).

Figure 1 . 3 .

 13 Figure 1.3. Reported malaria cases in Peru from 2000 to 2019. Grey line represents total malaria cases; red line represents vivax malaria cases; and blue line represents falciparum malaria cases. Data sources: World Malaria Report 2010 (World Health Organization 2010) and World Malaria Report 2019 (World Health Organization 2020).

  villages near the Iquitos-Nauta highway. The transmission among these villages is heterogeneous as shown by diverse entomologic inoculation rate (EIR) estimates comparable to those from holoendemic areas of Africa (Parker et al. 2013), and by parasitological and serological tests that confirm the existence of hotspots of high malaria transmission intensity (Rosas-Aguirre et al. 2015, Rosas-Aguirre et al. 2017).

Figure 1 . 4 .

 14 Figure 1.4. Cumulated incidence of P. vivax malaria in Peru at district level in 2020. CIR: Cumulative incidence rate estimated in 1000 inhabitants. Adapted from Centro Nacional de Epidemiologia, Prevención y Control de Enfermedades-MINSA (*) Hasta la SE 52 -2020 (Ministerio de Salud Perú 2020).

Figure 1 . 5 .

 15 Figure 1.5. Life cycle of Plasmodium vivax and some targets of immune response.Two types of sporozoites are injected into the host: those that will differentiate into merozoites by active mitotic division right after hepatocyte invasion (1), and those that will stay in a quiescent stage within the hepatocyte, hypnozoites(2). Created with BioRender.com.

Figure 2 . 1 .

 21 Figure 2.1. Cellular and humoral immune response during Plasmodium vivax infections. Follicular helper T cells (Tfh) interact with B cells in the germinal centre and promote B cells to differentiate into memory and plasma cells that secrete antibodies against P. vivax. Activated B cells are associated with the production of low-affinity antibodies. The differentiation of Tfh cells is stimulated by the interaction between CD4+ T cells and dendritic cells. The B cells and Tfh interactions are regulated by various surface molecules on Tfh and their counterparts on naïve B cells. Continuous malaria episodes corresponds with a greater expansion of Tfh cells and classical memory B cells. Reproduced from Antonelli et al. (Antonelli et al. 2020).

Figure 2 . 2 .

 22 Figure 2.2. Antibody signatures to P. vivax are shaped by lifetime exposure. A) Kinetic of short-(black), moderate-(red) and long-lived (blue) antibody (Ab) responses boosted by hypnozoite relapses in a time period of 6 months. Short-lived antibodies can be detected up to 20-30 days after infection; whereas moderate-lived Ab can be detected up to 45-98 days after infection(Longley et al. 2017b). Ideally, moderate-and short-lived antibodies would be suitable to detect recent exposure in low transmission settings, while short-lived antibodies would be suitable to detect recent exposure in high transmission settings. B) Antibody kinetics of a shortlived antibody response in low-and high-transmission setting. Antibody response can be boosted by reinfections or relapses in high transmission settings, causing a slow decay and a remaining tail response. Created with BioRender.com.

Figure 2 .

 2 3 A). This binary data (seronegative or seropositive) is obtained by typically long-lived antibody responses to immunogenic Plasmodium proteins (Greenhouse et al. 2018). This strategy has been validated in various epidemiological settings and shows the correlation with other malaria metrics such as parasite rate (PR) (Figure 2.3 B).

Figure 2 . 3 .

 23 Figure 2.3. Established and next-generation methods for evaluating malaria transmission via antibodies provide higher resolution than parasite prevalence.A) The SCR for a population can be calculated from age-stratified prevalence of antibody responses. Data shown here are responses to AMA1 from three cross-sectional surveys in Uganda. B) SCR (using MSP-1) is strongly associated with transmission, as measured by the annual entomologic inoculation rate (EIR) and parasite prevalence or parasite rate (PR) (data from multiple sites). (C) Using six antibodies identified as informative about recent exposure, predictions of P. falciparum exposure in a community can be obtained from relatively small surveys, in contrast to PR data obtained from the same surveys. Adapted from[START_REF] Greenhouse | Taking Sharper Pictures of Malaria with CAMERAs: Combined Antibodies to Measure Exposure Recency Assays[END_REF][START_REF] Greenhouse | Taking Sharper Pictures of Malaria with CAMERAs: Combined Antibodies to Measure Exposure Recency Assays[END_REF]).

Figure 2 . 4 .

 24 Figure 2.4. Priority applications of serological markers of exposure as tools to achieve and sustain malaria elimination.Adapted from[START_REF] Greenhouse | Priority use cases for antibodydetecting assays of recent malaria exposure as tools to achieve and sustain malaria elimination[END_REF][START_REF] Greenhouse | Priority use cases for antibodydetecting assays of recent malaria exposure as tools to achieve and sustain malaria elimination[END_REF]

  Antibody titers reflect recent infection and cumulative exposure. Generally, individuals born after the start of a successful elimination program should not have antibodies (with the potential exception of maternallytransferred antibodies in children less than 6 months old). The presence of antibodies in young children indicates recent transmission. Adults will remain seropositive for a time due to previous exposure. This use-case would determine the impact of interventions, either new or established.

2. 4 .

 4 Serology in the time of pandemics: the case of SARS-CoV-2In December 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan, China, causing the Coronavirus disease (COVID19). Since then, it has been reported in almost every country in the world. The sequencing of the complete SARS-CoV-2 genome facilitated the rapid development of molecular and serological tests for diagnostic purposes(Wu et al. 2020a).The detection of active COVID19 cases was based almost exclusively on Reverse transcription quantitativePolymerase chain reaction (RT-qPCR)(To et al. 2020). Molecular tests are crucial for detecting people with ongoing SARS-CoV-2 infections, initiating isolation procedures, and cutting the transmission chain[START_REF] Wang | Combination of RT-qPCR testing and clinical features for diagnosis of COVID-19 facilitates management of SARS-CoV-2 outbreak[END_REF]). However, many mild and asymptomatic cases would have been unnoticed as they may never have sought diagnosis or if they had many might have had low viral load infections that are not detectable by RT-qPCR at the time of diagnostic sample collection. On the other hand, serological tests have limited use for clinical diagnosis but could be promising epidemiological tools to identity individuals who have been previously exposed to SARS-CoV-2.

Chapter 3 :

 3 Heterogeneity in response to serological exposure markers of recent Plasmodium vivax infections in contrasting epidemiological contexts Short title: Antibody responses to P. vivax in contrasting epidemiology contexts

Figure 3 . 1 .

 31 Figure 3.1.Prevalence of P. vivax infection in the Peruvian cohort. (A) Longitudinal prevalence of P. vivax infection by light microscopy or PCR in Cahuide and Lupuna. Retrospective analyses were performed using infection history over the last year of follow-up, which is indicated by the orange shadow. The purple arrow indicates when antibodies were measured. (B) Proportion of individuals with PCRdetectable infection in the previous "x" days the end of follow up. Shaded areas represent the respective 95% confidence intervals.

Figure 3 . 3 .

 33 Figure 3.3. Association of antibody responses to PvMSP119, PvRBP2b and PvCyRPA with epidemiological features. (A) Antibody titers and time since previous infection during follow up. (B) Antibody titers and number of blood stage infections detected by qPCR. (C) Antibody titers across age groups. Note the controls are grouped together regardless of age. Mo: number of months since previous infection.

  .7). exposure in the previous year accounted for 12% of the explained variance in Thailand, 5% in Brazil, and 10% in Peru. The studied factors explained substantially less of the variance in anti-PvMSP119 antibody responses. This is most likely due to the high immunogenicity of PvMSP119 causing the generation of strong antibody responses after few infections. The explained variance of the antibody response to PvCyRPA represented 35% of the total variance in Peru, 22% in Thailand and only 1.5% in Brazil. Age was the most important driver of the antibody response to PvCyRPA in Peru (0.35) followed by Thailand (0.13), and Brazil (0.01).

Figure 3 . 5 .

 35 Figure 3.5. Contribution of recent and lifetime exposure to the variance of antibody responses to PvMSP119, PvRBP2b and PvCyRPA across the study sites. Panels represent the variance estimated for each marker. The area of each colored bar is proportional to the variance explained by known and unknown factors. Red bars: variance explained by individual's age. Blue bars: variance explained by number of blood stage detections by qPCR during 13 months of follow up. Green bars: variance explained by time since previous infection (days). Grey bars: unexplained variance.

  our study, antibody titers increased with individuals' age. There are two possible explanations. Antibody levels may increase with age reflecting the cumulative nature of the immune response to P. vivax, and highlighting age as a surrogate marker of life-time exposure. This relationship has been exploited to assess malaria transmission intensity in endemic settings through modelling seroconversion rates, which are closely correlated with the entomological inoculation rate[START_REF] Drakeley | Estimating medium-and long-term trends in malaria transmission by using serological markers of malaria exposure[END_REF][START_REF] Stewart | Rapid assessment of malaria transmission using age-specific sero-conversion rates[END_REF]). On the other hand, the association of antibody responses and age may also reflect the epidemiological characteristics of each population. The transmission of P. vivax in Thailand is mostly related to forest jobs in adults(Longley et al. 2016a), whereas in Brazil and Peru, the transmission is both peri-domestic and work-related[START_REF] Lana | The top 1% -quantifying the unequal distribution of malaria: the case of Brazil[END_REF], Rosas-Aguirre et al. 2020a). Our linear regression model showed that the time since previous infection and the intensity of exposure in the last year were important drivers of the antibody response to the top markers, PvRBP2b and PvMSP119, in Thailand and Brazil, whereas the cumulative exposure, as indicated by individuals' age, explained most of the variance in antibody responses in Peru. In a previous study on the baseline of the Peruvian cohort, Rosas-Aguirre et al. reported the association of antibody responses to PvMSP10 with P. vivax infections in the previous 6 months and individual's age (Rosas-Aguirre et al. 2020b). PvMSP10 was not included in our panel of SEMs; however, the four PvMSP markers that ranked within the top 8 SEMs for classifying recent exposure in Peru(PvMSP3.3; PvMSP3.10; and two PvMSP7 markers) also showed association to individuals' age. Our results confirm the previous finding of a strong influence of age on PvMSP antigens' performance as a marker of recent exposure to P. vivax. Thus, the previous life-time exposure resulting from sustained transmission would influence SEMs performance in high-transmission settings.

  Perú 2020). Most of the malaria cases in Peru are concentrated in Loreto (96% of cases in 2020) (Ministerio de Salud del Perú 2020). Even within Loreto, transmission is heterogeneous, i.e., there are spatial and temporal transmission hot-spots surrounded by extensive areas with little or no local transmission. These scenarios with distinct malaria transmission dynamics owe their particularity to environmental factors such as human settlements close to water bodies[START_REF] Rosas-Aguirre | Hotspots of Malaria Transmission in the Peruvian Amazon: Rapid Assessment through a Parasitological and Serological Survey[END_REF] and the creation of mosquitoes breeding sites (such as fish ponds, road constructions and mining pools) by deforestation activities[START_REF] Vittor | The effect of deforestation on the human-biting rate of Anopheles darlingi, the primary vector of Falciparum malaria in the Peruvian Amazon[END_REF], as well as occupational factors such as forest-related work[START_REF] Chuquiyauri | Socio-demographics and the development of malaria elimination strategies in the low transmission setting[END_REF] or high human mobility between communities (Rosas-Aguirre et al. 2017). Over the last 20 years, the case incidence of malaria in Peru has been fluctuating, influenced largely by the prevailing intervention programs: After the cessation of the Global Fund Malaria Project "PAMAFRO" in 2010, there was a noteworthy increase in the number of malaria cases (Rosas-Aguirre et al. 2016), with a steady rise from 25 005 cases in 2011 to 66 609 cases in 2015. Since then, numbers are descending again due to the Malaria Zero program implemented in 2017 by the Peruvian Minister of Health (MINSA) (Ministerio de Salud Peru (MINSA) 2017). In three years of running Malaria Zero program, the number of malaria cases in Loreto decreased by 75% between 2017 and 2020 (Ministerio de Salud del Perú 2021a). In its first phase, this program has strengthened prevention activities in the population by distributing longlasting insecticide-treated nets and indoor spraying and implemented active test-and-treatment of all detected cases (Ministerio de Salud del Perú 2021b). P. vivax causes the majority of malaria cases in Peru. The detection of cases is based on light microscopy (LM) diagnosis; this may substantially underestimate prevalence as greater than 50% of infections that are detectable by qPCR may be missed by microscopy (Rosas-Aguirre et al. 2017). Being based on the detection of blood-stage parasites, LM and qPCR do not detect individuals with only liver-stage hypnozoite infections.

  sample for qPCR diagnosis, and 880 individuals accepted donating a plasma sample for serology purpose (Appendix Figure 4.1). In the community of Mazán, an area of more intense malaria transmission, a representative populationbased cross-sectional survey was conducted in July 2018, where 1127 individuals were enrolled to participate in the baseline survey by the Amazonian International Centres of Excellence for Malaria Research (ICEMR) team (Rosas-Aguirre et al. 2017). A total of 261 households were sampled in the 6 communities of Mazán described above. Blood samples were collected for light microscopy (n = 1127) and qPCR diagnosis (n = 1127). Plasma samples were collected from 1024 individuals for serological investigation (Appendix Figure 4.1).

Figure 4 . 1 .

 41 Figure 4.1. Map of the studied sites. Mazán and Iquitos are located within the Maynas province, in the department of Loreto. Seven communities from the Mazán community were surveyed: Gaminatacocha (GC), Libertad (LI), Primero de Enero (PE), Puerto Alegre (PA), Salvador (SA), Urco Miraño (UM) and Lago Yuracyacu (LY). Three communities from Iquitos were surveyed: Rumococha (RC), Santo Tomás (ST) and Quistococha (QC). Each circle represents a household location.

Figure 4 . 2 .

 42 Figure 4.2. Prevalence of Plasmodium vivax measured by three tools in communities of Iquitos and Mazán A) Prevalence of Pv by light microscopy (LM). B) Prevalence of Pv by PCR and C) Prevalence of Pv by antibody responses to SEM. Dots denote the mean and lines denote the 95% confidence intervals.

Figure 4 . 3 .

 43 Figure 4.3. Age trend of seropositivity to P. vivax in the studied communities. Each panel indicates the age seroprevalence detected by antibody responses to SEM in the villages of Iquitos: Rumococha (RC), Santo Tomás (ST) and Quistococha (QC), and the villages of Mazán: Gaminatacocha (GC), Libertad (LI), Primero de Enero (PE), Puerto Alegre (PA), Salvador (SA), Urco Miraño (UM) and Lago Yuracyacu (LY).. Dots denote the proportion mean and 95 % confidence interval of seroprevalence per age-group.

Figure 4 . 4 .

 44 Figure 4.4. Distribution of seropositives cases. Map of exposure to P. vivax in the study sites. The seropositive rate was calculated as the proportion of seropositive individuals per households in the villages of Iquitos: Rumococha (RC), Santo Tomás (ST) and Quistococha (QC), and the villages of Mazán: Gaminatacocha (GC), Libertad (LI), Primero de Enero (PE), Puerto Alegre (PA), Salvador (SA), Urco Miraño (UM) and Lago Yuracyacu (LY). Each point indicates a household location. Households are colored accordingly to their seropositive rate.

  9 years = 3.20; 95CI =1.66-6.16; p-value = 0.00; OR15-29 years = 12.8; 95CI = 5.54-29.59; p-value = 0.00; OR30-49.9 years = 19.57; 95CI = 7.88-48.60; p-value = 0.00; OR≥50 years = 27.67; 95CI = 10.79-70.93; p-value = 0.00), being male (OR = 1.69; 95CI =1.16-2.46; p-value = 0.01) and working outside (OR = 2.59; 95CI = 1.48-4.51; p-value =0.00). Having domestic animals was associated with a low risk of being seropositive (OR = 0.51; 95CI =0.29-0.89; p-value = 0.02).

years = 3 .

 3 94; 95CI = 1.23-12.59; p-value = 0.02; OR15-29 years = 11.46; 95CI = 3.40-38.70; p-value = 0.00; OR30-49.9 years = 25.95; 95CI = 7.67-86.78; p-value = 0.00; OR≥50 years = 29.81; 95CI = 8.99-98.88; p-value = 0.00).

Figure 4 . 5 .

 45 Figure 4.5. Visual summary of estimates of multilevel regression models of P. vivax seropositivity and parasitaemia in Mazán and Iquitos. A) Fixed effects for seropositivity to P. vivax. B) Fixed effects for P. vivax parasitaemia; no factors were significantly associated. Adjusted Odds Ratios and the 95% CI are represented by blue (Mazán) and orange (Iquitos) lines. Ref = Reference; *: p < 0.05.

A

  multiplex serological assay was developed to measure IgG and IgM antibody responses to seven SARS-CoV-2 spike or nucleoprotein antigens, two antigens for the nucleoproteins of the 229E and NL63 seasonal coronaviruses, and three non-coronavirus antigens. Antibodies were measured in serum samples from individuals in French hospitals with RT-qPCR confirmed SARS-CoV-2 infection (n = 259), and negative control serum samples collected before the start of the SARS-CoV-2 epidemic (n = 335). Machine learning classifiers were trained with the multiplex data to classify individuals with previous SARS-CoV-2 infection.

  obtained from consenting individuals through the CORSER study (NCT04325646, Etude séro-épidémiologique du virus SARS-CoV-2 en France : constitution d'une collection d'échantillons biologiques humains) directed by Institut Pasteur and approved by the Comité de Protection des Personnes Ile de France III, and the French COVID cohort (NCT04262921, sponsored by Inserm and approved by the Comité de Protection des Personnes Ile de France VI). Sample collection in Hôpital Cochin was approved by the Research Ethics Commission of Necker-Cochin Hospital. Use of the Peruvian negative controls was approved by the Institutional Ethics Committee from the Universidad Peruana Cayetano Heredia (SIDISI 100873).

  + 𝑔𝑃 𝑙 -𝑟𝐴where B denotes B lymphocytes, b is the rate of differentiation of B lymphocytes into antibody secreting plasma cells, Ps denotes short-lived plasma cells, Pl denotes long-lived plasma cells, ρ is the proportion of plasma cells that are short-lived, g is the rate of generation of antibodies (IgG or IgM) from plasma cells, and r is the rate of decay of antibody molecules.

Figure 5 . 1 .

 51 Figure 5.1. Anti-SARS-CoV-2 antibody responses. (A) Measured IgG antibody dilutions or medium fluorescence intensity (MFI) in serum samples with previously confirmed RT-qPCR infection from patients in Hôpital Bichat (n = 34), health care workers from Strasbourg (n = 162), and Hôpital Cochin (n = 63). Negative control samples from Thailand (n = 68), Peru (n = 90), and French donors (n = 177) were also tested. (B) Measured IgM antibody dilutions or MFI in serum or plasma samples. (C) Receiver Operating Characteristic (ROC) curve for IgG antibodies obtained by varying the cutoff for seropositivity. Colours correspond to those shown in part A. (D) ROC curve for IgM antibodies obtained by varying the cutoff for seropositivity. (E) Area under the ROC curve for individual biomarkers. (F) Spearman correlation between measured antibody responses

Figure 5 . 2 .

 52 Figure 5.2. Serological signatures of SARS-CoV-2 infection. (A) Pairwise combinations of antibody responses. Each point denotes a measured antibody response from a sample from Hôpital Bichat (n = 34), Nouvel Hôpital Civil & Hôpital de Haute Pierre in Strasbourg (n = 162), and Hôpital Cochin (n = 63). Negative control samples are included from Thailand (n = 68), Peru (n = 90) and French blood donors (n =177). (B) ROC curves for multiple biomarker classifiers generated using a Random Forests algorithm. (C) For a high specificity target (>99%), sensitivity increases with additional biomarkers. Sensitivity was estimated using a Random Forests classifier. Points and whiskers denote the median and 95% confidence intervals from repeat crossvalidation.

Figure 5 . 3 .

 53 Figure 5.3. Model predicted sensitivity over time. Proportion of n = 215 individuals with qRT-PCR infection testing seropositive over time. A Random Forests algorithm was used for classification of multiple antigen multiplex data. The grey shaded region shows the 95% credible interval (CrI) for the four antigen multiplex classifier.

Figure 5 . 4 .

 54 Figure 5.4. Implementation of seroprevalence surveys. (A) Receiver Operating Characteristic (ROC) analysis with cross-validated uncertainty. Solid lines represent median ROC curves and shaded regions represent 95% uncertainty intervals for specificity. (B) In a scenario with true seroprevalence = 5%, the measured seroprevalence depends on the false positive rate (= 1 -specificity). Results for the monoplex anti-S tri IgG assay are shown on the left, and results for the multiplex assay are shown on the right. (C) In a scenario with true seroprevalence = 5%, adjusted seroprevalence estimates are obtained by accounting for assay sensitivity and specificity. (D) Across a range of true seroprevalence, optimal values of sensitivity and specificity can be selected to minimize the expected relative error in seroprevalence surveys. (E) The expected relative error for optimal values of sensitivity and specificity.

  Males aged >15 years old are more exposed to P. vivax than males of <15 years old because they start working in forest-related jobs where occupational infections happen[START_REF] Carrasco-Escobar | High prevalence of very-low Plasmodium falciparum and Plasmodium vivax parasitaemia carriers in the Peruvian Amazon: insights into local and occupational mobility-related transmission[END_REF]. While the presence of antibodies in older age groups indicates either recent or historical exposure, the presence of antibodies in <15 years population, especially children <10 years old, indicated that exposure is recent and P. vivax is transmitted at the community level. These findings are in line with reports showing the resurgence of malaria in those communities after the outbreaks following the floods in Loreto (2012-2013) (Rosas-Aguirre et al. 2020a). Our second study (Chapter 4) also supports the premise that antibodies in young groups indicated that exposure is recent and transmission is local. In contrast, the higher antibody titers in >15 years old participants indicated recent or past exposure and local or occupational transmission.

. 1 .

 1 C-E). The performance of a combination of 5 top SEM to identify exposure in the last nine months to P. vivax was lower in high transmission settings of the Peruvian Amazon (AUC = 0.7) compared to low transmission settings of Thailand and Brazil (AUC = 0.8) (Figure6.1.F). The background immunity of communities such as Cahuide and Lupuna undermines the utility of P. vivax SEM to detect individuals with recent exposure in settings with historically intense transmission.We have provided evidence that estimates of exposure derived from molecular tests give a single snapshot of the transmission in the survey time, while serology projects images of the exposure landscape over a span of time longer than the survey time (Figure6.1.G). Also, we have shown that the challenges of characterising recent exposure in settings like the Peruvian Amazon could be overcome by inspecting the distribution of antibody levels in young participants of the surveyed population (Figure6.1.H, I). This distribution will permit describing the type of transmission, local or occupational, in the surveyed population. As the transmission of malaria in Loreto is diminishing thanks to the Malaria Zero program implemented in 2017 (75% of cases diminished between 2017 and 2020) (Ministerio de Salud del Perú 2021a), this thesis provides sufficient evidence on the population immunity in contrasting epidemiological contexts of the Peruvian Amazon before and during the implementation of such programs. When malaria cases reach zero transmission, measuring recent exposure by SEM will play an essential role in evaluating the impact of that or other intervention programs.Finally, we have demonstrated that vivax malaria serology sets precedents to design better antibody assays to other pathogens like SARS-CoV-2. Multiplex antibody measurements will allow us to study antibody kinetics to SARS-CoV-2 and get ready for future challenges that we could face in SARS-CoV-2 serological surveillance.

Figure 6 . 1 .

 61 Figure 6.1. Main findings of the thesis. A) Longitudinal follow-up studies analysed in the third chapter. B) Antibody levels and transmission intensity. C) Antibody levels and time since last infection. D) Antibody levels and blood-stage detections. E) Antibody levels and age. F) Antibody levels and diagnostic performance by ROC curves. G) Cross-sectional studies analysed in the fourth chapter. H) Seroprevalence stratified by age. I) Antibody levels stratified by age. CAH: Cahuide; LUP: Lupuna. Created with BioRender.com.

A

  clinical episode was defined by having a temperature of ≥38.0°C at the time of sample collection or having fever in the past 48 hours and parasite density >500 parasites/uL. ¥ : Number of P. vivax positive qPCR results within the last six months of following up. Concurrent P. vivax infections were included in the variable Number of P. vivax positive qPCR results within the last six months of following up. ¶ : Number of P. vivax positive qPCR results in the initial seven months of following up. 95% CI: 95% confidence interval. * p<0.05; **p<0.01; ***p<0.001.

  + 𝑔𝑃 𝑙 -𝑟𝐴 where B denotes B lymphocytes, b > 0 is the rate of differentiation of B lymphocytes into antibody secreting plasma cells, Ps denotes short-lived plasma cells, Pl denotes long-lived plasma cells, ρ (0 ≤ 𝜌 ≤ 1) is the proportion of plasma cells that are short-lived, g > 0 is the rate of generation of antibodies (IgG or IgM) from plasma cells, and r > 0 is the rate of decay of antibody molecules. Assuming B(0) = B0, Ps(0) = Pl(0) = 0 and A(0) = Abg, these equations can be solved analytically to give: 𝐴(𝑡) = 𝐴 𝑏𝑔 + 𝑔𝐵 0 ( (𝜌𝑐 𝑙 + (1 -𝜌)𝑐 𝑠 -𝛿)𝑒 -𝑏(𝑡-𝛿) (𝑐 𝑠 -𝛿)(𝑐 𝑙 -𝛿)(𝑟 -𝑏) + (𝜌𝑐 𝑙 + (1 -𝜌)𝑐 𝑠 -𝑟)𝑒 -𝑟(𝑡-𝛿) (𝑐 𝑠 -𝑟)(𝑐 𝑙 -𝑟)(𝑏 -𝑟) + 𝜌𝑒 -𝑐 𝑠 (𝑡-𝛿) (𝑐 𝑠 -𝑟)(𝑐 𝑠 -𝑏) +(1 -𝜌)𝑒 -𝑐 𝑙 (𝑡-𝛿) (𝑐 𝑙 -𝑟)(𝑐 𝑙 -𝑏) )

log ( 2

 2 ) 𝑟 to be the half-life of antibody molecules. Similarly, we define 𝑑 𝑏

  The model was fitted to the data using Markov Chain Monte Carlo (MCMC) methods. A three stage parameter update regimen was utilised with a Metropolis-within-Gibbs sampler with sequential updating of individual-level parameters, population-level parameters, and observational variance parameters. A′ indicates an attempted update.

Table 3 .

 3 1. Epidemiologic characteristics of the newly reported Peruvian study sites and participants. .....45 Table 4.1. Epidemiological characteristics of study sites. .............................................................................70 Table 4.2. Prevalence of P. vivax and P. falciparum .....................................................................................71 Table 5.1. Panels of samples. ........................................................................................................................85

1 Chapter 1 : General Introduction 2

  the antibody responses to 856 P. falciparum proteins via protein array in 186 Ugandan children, for whom detailed P.

  Antibody responses as serological markers of Plasmodium vivax infection have been shown to correlate with exposure, but little is known about the other factors that affect antibody responses in naturally infected people from endemic settings. To address this question, we studied IgG responses to novel serological exposure markers (SEMs) of P. vivax in three settings with different transmission intensity.

	3.1. Abstract
	3.1.1. Background
	1,3,4*

  Peruvian cohort was approved by the Institutional Ethics Committee from the Universidad Peruana Cayetano Heredia (UPCH) (SIDISI 57395/2013) and from the University of California San Diego Human Subjects Protection Program (Project # 100765). UPCH also approved the use of the Peruvian serum samples in the Walter and Eliza Hall Institute of Medical Research (WEHI) (SIDISI 100873/2017). The Thai cohort was approved by the Ethics Committee of the Faculty of Tropical Medicine, Mahidol University, The Peruvian cohort was conducted in two Amazonian villages in the Loreto Region: San José de Lupuna,

	3.3.2. Field Studies
	and Cahuide (Rosas-Aguirre et al. 2017). Lupuna is located 10 km from Iquitos district (03°44.591′S,
	73°19.615′W), a forested area only accessible by river. Cahuide (04°13.785′ S, 73°276′ W) is located 60 km
	from Iquitos city on the Iquitos-Nauta road. Villagers work mainly in agriculture, fishing and occasional
	hunting. In 2016, malaria cases in Peru represented 14.3% of all cases in South America, from which 96%
	were reported from Loreto Region (MINSA 2016, WHO 2017). Malaria cases have been steadily increasing
	since 2010-2011, after the cessation of the international financial support program "PAMAFRO", and
	worsening due to the Loreto flood between 2012-2013 that inundated and damaged many riverine
	Thailand (MUTM 2013-027-01). The Brazilian study was approved by the FMT-HVD (51536/2012) and by
	the Brazilian National Committee of Ethics (CONEP) (349.211/2013). The Human Research Ethics
	Committee (HREC) at WEHI approved samples for use in Melbourne (#14/02).

communities

[START_REF] Rosas-Aguirre | Epidemiology of Plasmodium vivax Malaria in Peru[END_REF]

. Transmission is stable in both Lupuna and Cahuide, with a peak season from November to May

[START_REF] Rosas-Aguirre | Micro-heterogeneity of malaria transmission in the Peruvian Amazon: a baseline assessment underlying a population-based cohort study[END_REF]

.

A three year-long observational cohort study was conducted over December 2012 -December 2015 in Loreto, Peru. Using home-to-home and community-based screening, volunteers ≥ 3 years old were invited to participate in this cohort. Participants excluded from the cohort had the following characteristics: being younger than three years old and not giving informed consent to use a blood sample. In July -August 2012, a census was administered to 1007 and 1440 people in Lupuna and Cahuide, respectively. In the enrollment visit, 2197 people were scheduled for follow up from January 2013 to December 2015. The sample size estimation assumed the following: 20% residents had at least 1 microscopically confirmed malaria infection annually; 2% precision; 25% loss to follow-up; 80% power; and 5% significance level (Rosas-Aguirre et al.

  Escobar et al. 2017). Almost 15% (n = 89) of participants had recently settled in the communities (<2 years), with higher proportions in Cahuide (26%) than Lupuna (3%) (p < 0.0001, χ 2 test).December 2014 to December 2015, a total of 7,612 blood samples were collected during follow-up. Of the collected blood samples, 14.2% (1083/7612) were positive for P. vivax by qPCR. Of these qPCR positive samples 11.8% (128/1083) were positive by microscopy, and 2.8% (30/1083) exhibited symptoms. In the last 9 months of follow up (the last year of the cohort), 65.6% of individuals experienced at least one bloodstage P. vivax infection. There was a significant difference in cumulative PCR prevalence between Cahuide

	(66.1%) and Lupuna (84.1%) (p < 0.0001, χ 2 test) (Figure 3.1B).
	At the last day of follow up, the prevalence of P. vivax (Pv) infections detected by qPCR was 21.2% (LUP:
	23.6%, CAH: 18.7%) (Figure 3.1A), of which only 7% were detected by LM (9 patent infections). Age has
	been described as a risk factor for P. vivax exposure at the baseline of this cohort (Rosas-Aguirre et al.

3.43 (https://www.r-project.org/) and the R packages MASS, ROCR, rpart, randomForest, and Stata version 12 (StataCorp. 2011. Stata Statistical Software: Release 12. College Station, TX: StataCorp LP.)

3.4. Results

3.4.1. Epidemiological characteristics of the Peruvian longitudinal cohort

Epidemiological characteristics of the Peruvian study population are summarized in Table

3

.1. A total of 590 individuals had a serum sample at the end of the cohort. Of these individuals, 289 lived in Cahuide (CAH, 48.9%) and 301 lived in Lupuna (LUP, 51.1%). The average age of participants was 29.7 years (range: 3.8 -85 years old). Individuals from Cahuide were younger than those from Lupuna (p < 0.01). The overall female/male ratio was 1.39, with similar proportions in both sites (CAH:

1.44, LUP: 1.35

). The short duration of residency in the community had previously been identified as a risk factor for P. vivax infection in these communities

(Carrasco-2017)

; therefore, we compared the Pv prevalence by qPCR among three age groups at the last time point of follow up. There were no significant differences in prevalence among three defined age groups (p > 0.05). The median parasite density by qPCR was 1.55 parasites/µL (IQR: 0.74 -8.38). In the preceding year,

Table 3 .1. Epidemiologic characteristics of the newly reported Peruvian study sites and participants.

 3

	Characteristics	Cahuide n = 289	Lupuna n = 301	Total n = 590
	Age (median, range)**	21.9 (3.80 -81.88)	29.1 (4.44 -85.57)	26.0 ( 3.80 -85.57)
	<15 years old (%)	40.48	31.56	35.93
	15-39 years old (%)	33.56	31.56	32.54
	≥40 years old (%)	25.95	36.88	31.53
	Female sex (%)	59.16	57.47	58.30
	Time in community, (%) ****
	<5 years	26.29	3.32	14.58
	≥5 years	73.7	96.68	85.42
	Forest-related occupation (>18 years old) (%)
	Fishing	0.62	2.67	1.73
	Woodcutter	6.83	4.81	5.76
	Farmer*	25.46	35.29	30.83
	Education (%)
	None*	21.45	14.95	18.13
	Primary school	57.10	52.16	54.58
	Secondary school**	21.45	32.89	27.29
	Infection by LM at the last time-point
	P. vivax (%)	2.08	1.00	1.52
	P. falciparum (%)	0	0	0.00
	Infection by qPCR at the last time-point a
	P. vivax (%)	18.68	23.58	21.19
	P. falciparum (%)	2.77	2.66	2.71
	Months of follow-up, n (%)
	13 months****	164 (56.74)	76 (25.24)	240 (40.68)
	37 months****	125 (43.25)	225 (74.75)	350 (59.32)
	P. vivax density by qPCR at the last time-point	1.60	1.39	1.55
	(median, IQR)	(0.92 -21.61)	(0.65 -4.96)	(0.74 -8.38)
	Total clinical P. vivax infections, n (%)
	0	279 (96.54)	283 (94.01)	562 (95.25)
	1	10 (3.46)	16 (5.31)	26 (4.41)
	≥2	0 (0)	2 (0.66)	2 (0.34)

a Prevalence includes single -and mixed-species infections The last time-point= day of blood and serum sampling for qPCR diagnosis and Luminex. Statistical differences between Cahuide and Lupuna, * p<0.05; ** p<0.01; *** p<0.001; **** p<0.0001.

  .5). Age was the strongest explanatory factor for 32 out of 34 proteins (coefficient range: 0.146 -1.127, p < 0.01). For instance, a doubling in age will give a 2.18 (2^1.127) increase in antibody levels to PVX_082650 (PvMSP7). Living in Lupuna and being male were associated with high antibody titers to 20 SEMs (coefficient range: 0.096 -0.538, p < 0.01) and 15 SEMs (coefficient range: 0.105 -0.202, p < 0.01), respectively, indicating the high At the final time point of follow-up, Peru had the highest prevalence of P. vivax by qPCR (21.2%, p< 0.001), followed by Brazil (4.2%) and Thailand (3.0%) (Appendix Table3.1). During the preceding year of follow up, 75.3% of Peruvian individuals experienced at least one infection of Pv, followed by Brazil and Thailand where 25.4% and 11.7% of participants had at least one infection. The average antibody titer to all 34 SEMs combined in the Peruvian cohort was 1.64 times higher than Brazil (p < 0.001, t-test) and 2.43 times higher Thailand (2.25 fold, p < 0.001, t-test) is also consistent with higher P. vivax transmission and a more rapid acquisition of antibodies in this population.Antibody levels to 34 Pv antigens negatively correlated to the time since previous infection (p < 0.001, Kruskal-Wallis test, Appendix Table3.7) and positively correlated to the number of blood-stage detections during follow up in all three cohorts (p < 0.001, Kruskal-Wallis test, Appendix Table3.8).[START_REF] Franca | Identification of highly-protective combinations of Plasmodium vivax recombinant proteins for vaccine development[END_REF], Longley et al. 2017a, He et al. 2019a). Figure3.3 shows the association between the antibody response measured at the final time point and (i) time since previous infection; (ii) number of qPCR positive blood-stage infections detected; and (iii) age. Similar patterns are seen across antibodies to all antigens in all regions (Appendix Figure 3.4-Appendix Figure 3.6).Antibody responses decrease with time since previous infection; increase with the number of blood-stage infections detected during one year of follow-up; and increase with age. Anti-CyRPA antibody responses in Brazil provide a notable exception, not exhibiting any significant association with the studied factors.

	We selected three
	antigens (PVX_094255B (PvRBP2b), PVX_090240 (PvCyRPA) and PVX_099980 (PvMPS119)) as examples due
	to their documented immunogenicity (
	PvMSP119), PVX_096995 (Pv-fam-a), PVX_094255 (PvRBP2b161-1454 and PvRBP2b1986-2653), PVX_095055
	(PvRiPR) and PVX_087885 were positively associated with the incidence of clinical episodes during the
	previous 6 months of follow up (coefficient: 0.384 -0.505, p < 0.01).

exposure in this community. The number of qPCR infections detected in the previous 6 months was an important predictor for 28 antibody responses (coefficient range: 0.042 -0.194 / infection, p < 0.01), indicating that the level of antibody response to these antigens depends upon the intensity of recent exposure. The antibody responses to PVX_099980 (P. vivax Merozoite Surface Protein1-fragment 19 3.4.3. Transmission intensity and average antibody titers to SEM were higher in Peru than Brazil and Thailand than Thailand (p < 0.001, t-test), confirming the existing high exposure burden in the communities from the Peruvian amazon (See more details in Appendix

Table 3.6)

. The higher antibody titers in the Peruvian children (< 15 years old) compared with the same age group from Brazil (1.70 fold, p < 0.001, t-test) and

  , de[START_REF] De Oliveira | Genome-wide diversity and differentiation in New World populations of the human malaria parasite Plasmodium vivax[END_REF]), e.g. antibody responses to PvCyRPA changed between countries. In the future, it would worth assessing the genetic variation of the proposed SEMs and their impact in diagnosis in different endemic settings. Our approach's advantage is that we use many antigens that produce redundancy, i.e. if one antigen does not work in a particular site, other antigens could still provide information. Another limitation of our work is the fixed cohort design in Peru. Migration affected the cohort's design with loss of follow up and entry of new residents (who were not included in the study). Migration may have affected the incidence and prevalence either by introducing new strains or by the introgression of malaria-naïve individuals[START_REF] Carrasco-Escobar | High prevalence of very-low Plasmodium falciparum and Plasmodium vivax parasitaemia carriers in the Peruvian Amazon: insights into local and occupational mobility-related transmission[END_REF]. Unlike Lupuna, Cahuide is vulnerable to imported malaria due to work related-human mobility and social interactions within and between other endemic communities

(Rosas-Aguirre et al. 2020a); however, the transmission dynamics in the Peruvian amazon goes beyond our work aims. There are a number of potential applications for SEMs in malaria control and elimination programs. At a population level, SEMs can be useful to identify pockets of residual transmission in geographic regions with heterogeneous or focalized transmission and measure the impact of interventions

[START_REF] Rosas-Aguirre | Hotspots of Malaria Transmission in the Peruvian Amazon: Rapid Assessment through a Parasitological and Serological Survey[END_REF][START_REF] Greenhouse | Priority use cases for antibodydetecting assays of recent malaria exposure as tools to achieve and sustain malaria elimination[END_REF][START_REF] Surendra | Using health facility-based serological surveillance to predict receptive areas at risk of malaria outbreaks in elimination areas[END_REF]

). These use cases can be applied in contexts of medium to very low transmission levels. At an individual level, SEMs could be implemented in serological test and treat (PvSeroTAT) strategies for preventative treatment of P. vivax with Primaquine

  5,8,12 12. Unit of Malaria: Parasites and hosts, Institut Pasteur, Paris, France 13. Sorbonne Université, ED 393, F-75005 Paris, France 14. School of Public Health, University of California San Diego, La Jolla, CA, USA 15. Health Innovation Laboratory, Instituto de Medicina Tropical Alexander von Humboldt, Universidad Peruana Cayetano Heredia, Peru 16. Instituto de Medicina Tropical Alexander von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru 17. Laboratorio de Malaria, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru 18. Universidad Nacional de la Amazonía Peruana, Loreto, Peru 19. Laboratorio ICEMR-Amazonia, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru 20. Section of Infectious Diseases, Department of Internal Medicine, Yale School of Medicine, New

	Haven, CT, USA
	21. Department of Medical Biology, University of Melbourne, Australia
	22. Population Health and Immunity Division, Walter and Eliza Hall Institute of Medical Research,
	Melbourne, Australia
	23. Departamento de Ciencias Celulares y Moleculares, Facultad de Ciencias y Filosofía, Universidad
	Peruana Cayetano Heredia, Lima, Peru

  that the Malaria Zero program has been implemented in the Peruvian Amazon region, new tools are needed to capture transmission patterns, detecting hot-spots and individuals likely contributing to transmission. In 2018, cross-sectional surveys were executed in two contrasting geographical areas of Loreto: Iquitos (peri-urban communities) and Mazán (rural, riverine communities) to determine the exposure level to Plasmodium vivax through antibody responses to serological markers in riverine and periurban communities of the Peruvian Amazon. Whereas previous studies in the Peruvian Amazon only focused on the analyses of antibodies to individual protein such as PvAMA1 (Rosas-Aguirre et al. 2015),Iquitos is the capital of Loreto and is the most populated metropolitan area. It is accessed only by air or by river and the majority of the population are mestizo (very mixed-race identity). Three peri-urban communities of the district of Iquitos were surveyed for this study: Rumococha (RC), Santo Tomás (ST) and

	Quistococha (QC). RC is located 8 km from Iquitos city; ST is 11km from Iquitos city and surrounded by the
	Nanay River (Figure 4.1). QC is located 12km from Iquitos city and close to the Nauta-Iquitos road. The
	most common occupations are commerce, mototaxi and housewife (Chuquiyauri et al. 2012).
	Mazán is a district of Maynas province located 55 -60 km northeast of Iquitos. It is comprised of many
	small communities, and the community of Mazán is surrounded by the Mazán and the Napo Rivers (3.503°
	S, 73.094° W). The only access is by speedboat (1 hour from Iquitos). Gamitanacocha (GC), Libertad (LI),
	PvMSP119 (Rosas-Aguirre et al. 2015), PvMSP8 (Villasís et al. 2021) or PvMSP10 (Rosas-Aguirre et al. 2017),
	the advantage of our study is the simultaneous measurement of 8 markers of recent exposure to P. vivax:
	PvMSP119, PvTRAg_2, PvTRAg_28, PvMSP8, PvMSP3.10, PvRAMA, PvRBP2b and PvEBPII.
	4.3. Methods
	4.3.1. Ethics
	Samples were collected from consenting individuals through the Circles of Research on Arboviruses and
	Malaria (CAM) study (SIDISI 101645/2017) and the Amazonia International Center of Excellence in Malaria
	Research (ICEMR) study (SIDISI 101518/2018); both studies were approved by the Institutional Ethics
	Committee from the Universidad Peruana Cayetano Heredia (UPCH). UPCH also approved the use of the
	serum samples collected in those studies and the use of Peruvian negative controls in the Institut Pasteur
	(SIDISI 100873/2017). All participants provided written informed consent for both participation in the study
	and future use of samples in studies of antimalarial antibody responses. Parental written consent and
	written assent were obtained in the case of child participants.
	4.3.2. Study area

Two cross-sectional studies were carried out in two districts of Maynas, department of Loreto in 2018: Iquitos and Mazán. Primero de Enero (PE) and Puerto Alegre (PA) are located on the Mazán River. Salvador (SA), Lago Yuracyacu (LY) and Urco Miraño (UM) are located on the Napo River. The main economic activities are farming, timber extraction and fishing (Rosas-Aguirre et al. 2017).

Malaria transmission in Maynas is perennial, with a peak between February and July

[START_REF] Branch | Clustered local transmission and asymptomatic Plasmodium falciparum and Plasmodium vivax malaria infections in a recently emerged, hypoendemic Peruvian Amazon community[END_REF]

.

While in 2017, Iquitos and Mazán were considered medium-and high-risk districts for malaria transmission, respectively (Ministerio de Salud del Perú 2017), in Mazán, intensified control during 2017 significantly reduced transmission and parasite prevalence in Mazán villages (Rosas-Aguirre et al. 2021).

  Of the 933 surveyed inhabitants in the CAM study, 880 individuals from 358 households (HH) were included in the analysis. In the CAM study in Iquitos there were 4.70 individuals per HH (95% CI: 3.00-6.00) and 52.3% of these HH members were enrolled (2.45 enrolled individuals per HH, 95% CI: 2.00-2.89). Of the 1127 surveyed inhabitants in the ICEMR study in Mazán, 1024 individuals from 263 HH were included in the analysis. There were 4.89 individuals per HH (95% CI: 3.00-6.00), and 79.6% of HH members were enrolled (3.89 enrolled individuals per HH, 95% CI: 3.30-4.47). In the peri-urban area of Iquitos, 35.5% of Iquitos with no significant differences. Open houses have been associated as a high risk factor for P. vivax in Mazán[START_REF] Rosas-Aguirre | Micro-heterogeneity of malaria transmission in the Peruvian Amazon: a baseline assessment underlying a population-based cohort study[END_REF], Rosas-Aguirre et al. 2021) and in other studies in the Amazon[START_REF] Corder | Statistical modeling of surveillance data to identify correlates of urban malaria risk: A population-based study in the Amazon Basin[END_REF]). 16% of HH in Iquitos and 8% of HH in Mazán were open houses (0-3 walls) (p < 0.05). Having livestock inside dwelling was seen in 24.6% of the HH from Iquitos and 26.6% from Mazán (p > 0.05).

individuals were under 15 years old, and 59 % were women (Table

4

.1). In contrast, in the district of Mazán, 46.3% of individuals were under 15 years old and 51% were women. Almost half of participants of Iquitos had secondary school or higher education level (47.2 %), whereas only a fifth of participants of Mazán (18.1 %) had this education level. Outdoor occupations such as logger, farmer and fisher were more common in Mazán (40.9%) than in Iquitos (8.9%). Farmer and housewife were the most common economical occupation in adults older than 18 years old in Mazán (38.1%) and in Iquitos (30.8%), respectively. Only 2.5% of participants from Iquitos and 1.2% from Mazán had fever (axillar temperature >37.5 °C) at the moment of survey (p < 0.05).

Crowded households (≥ 5 individuals per HH) were slightly more frequently reported in Mazán than in

Table 4 .1. Epidemiological characteristics of study sites.

 4

		Iquitos	Mazán	p-value
		(n = 880)	(n = 1024)
	Communities, n (%)
	Rumococha 295 (33.5%)
	Santo Tomás 298 (33.9%)
	Quistococha 287 (32.6%)
	Gamitanacocha		48 (4.7%)
	Libertad		184 (18.0%)
	Primero de Enero		58 (5.7%)
	Salvador		278 (27.1%)
	Lago Yuracyacu		97 (9.5%)
	Puerto Alegre		169 (16.5%)
	Urco Miraño		190 (18.6%)
	Individual level variables
	Age, mean (min -max)	30.22 (0-117) 27.67 (0-87) 0.011
	Age groups, n (%)			<0.001
	≤ 4.9	56 (6.4%)	127 (12.4%) <0.001
	5-14.9	256 (29.1%)	347 (33.9%) 0.024
	15-29.9	199 (22.6%)	134 (13.1%) <0.001
	30-49.9	199 (22.6%)	218 (21.3%)
	≥ 50	170 (19.3%)	198 (19.3%)
	Gender, n (%)			<0.001
	Female	519 (59.0%)	521 (50.9%)
	Male	361 (41.0%)	503 (49.1%)
	Education			<0.001
	None or primary	462 (52.8%)	749 (81.9%)
	Secondary or higher	413 (47.2%)	165 (18.1%)

Outdoor occupation (logger, fisher and farmer)

  .4.2. Antibody responses signature reveals transmission structure within study areas Although both Pv prevalence by LM and qPCR were higher in Iquitos than in Mazán (LM: 1.8 % vs 1.3%, and

	qPCR: 5.3% vs 2.7%, p < 0.05); Mazán (56.5%) showed higher seroprevalence than Iquitos (38.2%, p < 0.05)
	(Table 4.2). Only 14% and 5% of sero-positive individuals in Iquitos and Mazán respectively had concurrent,
	PCR-detectable infections. There were also significant differences in the antibody responses to 6 out of 8
	SEM between the two areas. Antibody responses to PvTRAg_2, PvEBPII, PvRBP2b, PvTRAg_28 and
	PvMSP3.10 were two times higher in residents from Mazán than Iquitos (p-value mean: <0.05). Participants
	from Iquitos had 1.2 times higher antibody levels to PvMSP119 than participants from Mazán (p <0.05).
				<0.001
	Yes	78 (8.9%)	419 (40.9%)
	No	802 (91.1%)	605 (59.1%)
	Fever, n (%)	22 (2.5%)	12 (1.2%)	0.045
	Household level variables
	Households, n	358	263
	Household size			0.09
	< 5 individuals	178 (49.7%)	113 (43.0%)
	≥ 5 individuals	180 (50.3%)	150 (57.0%)
	Housing type, n (%)			0.004
	Open house (0-3 walls) 56 (15.6%)	21 (7.9%)
	Complete 302 (84.4%)	242 (92.1%)
	Livestock inside dwelling
		Yes 88 (24.6%)	70 (26.6%)
		No 270 (75.4%)	193 (73.4%)

Frequencies were compared by χ 2 test and continous variables were compared by Oneway ANOVA test. Livestock inside dwelling: Dog, Cat, Chicken. 4

Table 4 .2. Prevalence of P. vivax and P. falciparum

 4 95% confidence interval. *Antibody level values are in relative antibody units (RAU) interpolated from standard curves using a 5PL logistic regression model. RAU were values going from 0.02 to 0.00000195. RAU values were multiplied by 1000 to ease reading. Frequencies were compared by χ2 test and continous variables were compared by One-way ANOVA test.

		Iquitos	Mazán	p-value
		(n = 880)	(n = 1024)
	Light microscopy, n (%)
	P. vivax	16 (1.8%)	13 (1.3%)
	P. falciparum	3 (0.3%)	5 (0.5%)
	PCR, n (%)
	P. vivax	47 (5.3%)	28 (2.7%)	0.005
	P. falciparum	12 (1.4%)	12 (1.2%)
	SEM, n (%)
	P. vivax seropositive	336 (38.2%)	579 (56.5%)	<0.001
	Antibody levels*, mean (95% CI)
	PvRAMA	0.56 (0.43-0.69) 0.65 (0.55-0.74)
	PvTRAg_2	0.90 (0.72-1.08) 1.73 (1.50-1.97)	<0.001
	PvEBPII	0.95 (0.74-1.16) 2.10 (1.83-2.36)	<0.001
	PvRBP2b	1.44 (1.21-1.66) 3.13 (2.84-3.43)	<0.001
	PvMSP119	3.58 (3.20-3.93) 2.98 (2.67-3.29)	0.013
	PvMSP8	0.46 (0.32-0.61) 0.56 (0.43-0.68)
	PvTRAg_28	2.09 (1.83-2.35) 3.66 (3.33-3.99)	<0.001
	PvMSP3.10	0.46 (0.35-0.57) 1.20 (1.02-1.38)	<0.001
	Abbreviations: 95% CI =

  Salvador and Primero de Enero did not have any positive qPCR sample in the survey (0%). On the other hand, Gamitanacocha was the community with higher seroprevalence (87.5%, 42/48), followed by Libertad with 75% (138/184) of seroprevalence, Primero de Enero (69%, 40/58), Urco Miraño (61.0%, 116/190), Puerto Alegre (48.5%, 82/169), Salvador (47.1%, 131/278) and Lago Yuracyacu (30.9%, 30/97).

	< 0.05 t-test) and Quistococha (2 %, 6/287) (p < 0.05 t-test). Conversely, seroprevalence was higher in Santo
	Tomás (49.3%, 147/298) than Rumococha (35.35%, 104/295) (p < 0.05 t-test) and Quistococha (29.6%,
	85/287) (p < 0.05 t-test).
	In Mazán, Libertad has the highest qPCR prevalence among all the communities (8.7%, 16/184), followed
	by Gamitanacocha (4.2%, 2/48), Puerto Alegre (2.9%, 5/169), Urco Miraño (2.1%, 4/190), and Lago
	Yuracyacu (1.0%, 1/97).
	than Santo Tomás (4%, 12/298) (p

Table 4 . 1 .

 41 Overall, seropositivity rate increased with age in both study sites(Appendix Figure 4.3 C). The proportion of seropositive individuals was significantly higher in participants from Mazán than Iquitos in all age groups (age group <15: 1.72 times higher; age group 15-29.9: 1.92 higher; age group 30-49.9: 1.54 higher; age

	group 50+: 1.39 higher; p-value mean <0.05 (Appendix Figure 4.3 C). There was a dramatic increase in the
	proportion seropositive people aged > 15 years in both study sites (Appendix Figure 4.3 C). This difference
	was more pronounced in Mazán, where the SEM seropositivity rate in the age group 15-29.9 (0.68, 95CI:
	0.60-0.76) was two times higher than in the age group 0-15 (0.31, 95CI: 0.27-0.35) (p<0.05) (Appendix
	Figure 4.3 C). Antibody titers to individual SEM also showed an age-trend in both sites for all antigens
	considered (Appendix Figure 4.4)
	Within each study area, villages had different seropositive rate-age patterns (Figure 4.3). The lowest
	seropositive rates in participants aged <15 years were observed in Lago Yuracyacu (0.04, 95CI: 0.00-0.11)
	in Mazán, and Quistococha (0.15, 95CI: 0.08-0.22) in Iquitos. The highest seropositive rates in participants
	aged <15 years were observed in Gamitanacocha (0.72, 95CI: 0.50-0.88) in Mazán, and Santo Tomás (0.21,

95CI: 0.13-0.31) in Iquitos. Lago Yuracyacu (0.21, 95CI: 0.00-0.43) in Mazán and Rumococha (0.28, 95CI: 0.18-0.4) in Iquitos had the lowest seropositive rate in participants aged 15-29.9 years, respectively.

Table 4 .

 4 3 and Appendix Table 4.5. The ICC values are presented in Appendix Table 4.4 and Appendix Table 4.6. The significant fixed effects for the multilevel multi-community models are graphically represented in Figure 4.5.

  The seroprevalence of P. vivax was higher in communities of Mazán than in Iquitos. Seroprevalence increased with age in both sites, with a significant increase in participants older than 15 years old, although the distribution by age was more heterogeneous among communities from Mazán. The distribution of seropositive cases did not show a spatial structure in communities of Iquitos but did it in Mazán. Age and being male were factors associated with high P. vivax seropositivity in both areas, while having higher education level and livestock inside dwelling were low-risk factors in Iquitos and Mazán, respectively. Outdoor occupation was a risk factor for P. vivax seropositivity in Mazán only. LM and PCR prevalence showed local transmission in both sites, but we did not find risk

factors associated with P. vivax parasitaemia, possibly due to low infection rates and thus limited statistical power.

Table 5 .1. Panels of samples.

 5

	Panel	RT-qPCR	N:	N: samples age		symptoms
		confirmed	participants		(years)	mild	severe
	Hôpital Bichat,	yes	4	34	39 (31, 80) 0	4
	Paris
	Hôpital Cochin,	yes	49	63	56 (26, 79) 27	22
	Paris
	Nouvel Hôpital Civil	yes	162	162	32 (20, 65) 160	2
	& Hôpital de Haute
	Pierre, Strasbourg
	Thai Red Cross	pre-epidemic	68	68	> 18
		negative
		controls
	Peru negative	pre-epidemic	90	90	> 18
	controls	negative
		controls
	France blood	pre-epidemic	177	177	> 18
	donors	negative
	(Établissement	controls
	Français du Sang)

Table 5

 5 .3: (i) high sensitivity target enforcing sensitivity > 99%; (ii) balanced sensitivity and specificity where both are approximately equal; and (iii) high specificity target enforcing specificity >99%. Focusing on the high specificity target, anti-S tri IgG was the best performing biomarker

	with 99•1% specificity (95% CI: 97•4%, 99•7%) and 91•6% sensitivity (95% CI: 87•5%, 94•5%). Anti-S tri IgG
	provided significantly better classification than all other biomarkers (Appendix Table 5.2; McNemar's test
	P value < 10

  signatures of SARS-CoV-2 infection generated by multiplex assays, and mathematical models of antibody kinetics, allow us to plan in advance for some of the future challenges that we may face in SARS-CoV-2 serological surveillance. considered medium-and high-risk districts for malaria transmission, respectively (Ministerio de Salud del Perú 2017), in Mazán, intensified control during 2017 significantly reduced transmission and parasite prevalence in Mazán villages[START_REF] Rosas-Aguirre | Integrating Parasitological and Entomological Observations to Understand Malaria Transmission in Riverine Villages in the Peruvian Amazon[END_REF].Both studies allowed us to understand P. vivax malaria transmission in geographically heterogeneous sites of the Peruvian Amazon. The longitudinal cohort study permitted the investigation of factors associated with antibody responses to markers of recent exposure to P. vivax. We found that participants from Lupuna had higher antibody levels than participants from Cahuide. Three factors were found to be associated with higher antibody levels: (i) time since previous infection; (ii) total number of blood-stage infections detected by qPCR during follow-up; and (iii) age. Comparing the antibody data measured in this study with those from longitudinal cohorts from low transmission sites (Thailand and Brazil) allowed us to weigh the importance of these three factors on the variation of antibody levels: number of infections, the time since previous infection and age (lifetime exposure). Although the time since previous infection better explained the antibody levels in low transmission settings, its contribution was smaller than age in the high transmission communities of Peru. Our study concluded that age was the most important determinant for the antibody responses in both Cahuide and Lupuna. The high levels of past exposure and probably long-lived antibodies in these communities reduces our ability to detect recently exposed individuals. The markers of recent exposure to P. vivax performed less well in moderate-high transmission settings than in low transmission sites.In the second study (Chapter 4), we measured eight antibody responses to detect recent exposure to P. vivax in a cross-sectional survey and characterise the transmission in peri-urban areas of Iquitos and rural communities of Mazán. Although this survey was located months after the beginning of the national malaria control and elimination program in Loreto, there was still a high number of reported malaria cases in 2018 (43 886 cases), a 27.8% reduction in comparison to 2013-2015 (average 60 708 cases) (Ministerio de Salud del Perú 2019). Our findings confirmed that transmission did not cease in Loreto. A proof of this is the PCR prevalence in inhabitants from the studied areas of both Iquitos and Mazán. Although the P. vivax PCR prevalence in Iquitos was higher than in Mazán, the seroprevalence demonstrated that vivax malaria transmission was more intense in Mazán than in Iquitos. The presence of anti-P. vivax antibodies in children (<15 years) confirmed that transmission is local in both study sites (as children from this population are substantially less mobile than adults). The probability of being exposed to P. vivax in both areas increased with age, whereas having P. vivax parasitaemia did not show any association. This study exemplified the benefits of serology over PCR to estimate exposure to P. vivax.In the third study, we developed and validated a multiplex antibody assay for measuring IgG and IgM responses to SARS-CoV-2. The inclusion of the most abundant proteins of SARS-CoV-2 was the rationale to design this antibody assay: five versions of spike protein and two constructs of nucleoprotein. We

	included one nucleoprotein for each seasonal coronaviruses NL63 and 229E, as controls to assess possible
	cross-reactivity with SARS-CoV-2; and three vaccine antigens from other viruses: influenza A [H1N1] virus,
	adenovirus type 40, and rubella virus, as internal controls. The assay was validated with positive serum
	samples: 215 adults in four French hospitals (53 patients and 162 health-care workers) with confirmed
	qPCR result for SARS-CoV-2, and pre-pandemic negative control serum samples collected from healthy
	adult blood donors (335 samples from France, Thailand, and Peru). The combination of multiplex assays
	and machine learning algorithms allowed us to identify previously infected individuals with 98.8%

  6.1.3. Serology outperforms PCR to estimate Plasmodium vivax exposureUp to 80% of P. vivax cases are caused by hypnozoite relapses[START_REF] Robinson | Strategies for understanding and reducing the Plasmodium vivax and Plasmodium ovale hypnozoite reservoir in Papua New Guinean children: a randomised placebocontrolled trial and mathematical model[END_REF]. To date, no tool is capable of directly detecting hypnozoites in P. vivax infected individuals because its hidden niches are not accessible to conventional diagnosis. The lack of such a tool means that we cannot target hypnozoiticidal treatment to carriers of dormant stages. PCR can estimate the malaria burden accounted for by detectable blood-stage infections only. Nonetheless, by using antibody responses informative of exposure in the last nine months, serology can detect potential hypnozoite carriers, giving better estimates of vivax malaria

Table 3 .4. Characteristic of evaluated constructs.

 3

	a, b

aPlasmoDB release 36 (http://plasmodb.org/plasmo/), bGenBank

  Table 1 for assessing classification performance. 1000-fold repeat cross-validation was implemented with splitting of the data into disjoint training sets (2/3 of the data) and testing sets (1/3 of the data). Appendix Figure 5.3 shows cross-validated Receiver Operating Characteristic (ROC) curves for Random Forests and Logistic Regression classifiers using six measurements of antibody responses (S tri IgG; RBDv2 IgG; NPv1 IgG; S2 IgG; RBDv1 IgM; NPv1 IgM). For all values of specificity, the random forests classifier provides equal or better sensitivity than the logistic regression classifier. At a fixed value of 99% specificity, the logistic regression classifier had 96•6% (95% CI: 88•2%, 100•0%) sensitivity, whereas the random forests classifier had 98•9%

	anti-NPv1	99•2%	5•5%	73•7%	73•8%	0%	100%
		(97•1%,	(3•6%, 8•5%)	(67•9%,	(68•8%,	(0%, 1•5%)	(98•9%,
		99•8%)		78•8%)	78•2%)		100%)
	Multiplex combinations
	S tri IgG + RBDv2 IgG 99•2%	95•8%	97•6%	97•6%	95•6%	99•1%
		(97•2%,	(93•1%,	(94•9%,	(95•4%,	(92•3%,	(97•4%,
		99•8%)	97•5%)	98•9%)	98•8%)	97•5%)	99•7%)
	S tri IgG + RBDv2 IgG	99•2%	96•6%	98•0%	98•1%	98•0%	99•1%
	+	(97•2%,	(94•1%,	(95•4%,	(96•1%,	(95•4%,	(97•4%,
	NPv1 IgG	99•8%)	98•1%)	99•1%)	99•1%)	99•1%)	99•7%)
	S tri IgG + RBDv2 IgG	99•2%	96•0%	98•8%	98•8%	98•4%	99•1%
	+	(97•2%,	(93•3%,	(96•5%,	(96•9%,	(96•0%,	(97•4%,
	NPv1 IgG + S2 IgG	99•8%)	97•6%)	99•6%)	99•5%)	99•4%)	99•7%)
	S tri IgG + RBDv2 IgG	99•2%	98•5%	98•8%	98•9%	98•8%	99•3%
	+ NPv1 IgG + S2 IgG	(97•2%,	(96•6%,	(96•5%,	(97•1%,	(96•5%,	(97•6%,
	+ RBDv1 IgM	99•8%)	99•4%)	99•6%)	99•6%)	99•6%)	99•8%)
	S tri IgG + RBDv2 IgG	99•2%	98•9%	98•8%	98•9%	98•8%	99•3%
	+ NPv1 IgG + S2 IgG	(97•2%,	(97•1%,	(96•5%,	(97•1%,	(96•5%,	(97•6%,
	+ RBDv1 IgM + NPv1	99•8%)	99•6%)	99•6%)	99•6%)	99•6%)	99•8%)
	IgM
	(95% cI: 97•1%, 99•6%) sensitivity.

  provides a detailed overview of the uncertainty in assay sensitivity and specificity.

												394	61%
	Nab	1034	1254	836	773	960	99	32	32			154	33%
	SARS-CoV; Liu et al. [9]
	time (days) 30	120	210	300	480	720
	IgG	185	201	115	125	65	32					254	49%
	SARS-CoV; Tang et al. [10]
	time (days) 24	120	210	300	480	720	900	1080	1600	2160
	IgG	305	252	128	170	66	31	36	33	6.9	6.0	400	57%
	seasonal coronavirus 229E; [11]
	time (days) 0	21	84	364
	IgG	2•45	3•18	2•62	2•51							191	91%
	IgA	2•61	3•04	2•80	2•66							150	87%
	Nab	1•43	9•84	5•46	2•19							116	91%
	MERS CoV; Choe et al. [12]
	time (days) 15	90	200	300	400
	IgG (S1)	1•39	2•53	1•63	1•56	1•47						915	50%

  1 , … , 𝑎 𝐽 } at times 𝑇 𝑛 = {𝑡 1 , … , 𝑡 𝐽 }. We denote 𝐷 𝑛 = (𝐴 𝑛 , 𝑇 𝑛 ) to be the vector of data for individual n. For individual n, the parameters 𝜃 𝑛 = (𝐴 𝑏𝑔 𝑛 , 𝛿 𝑛 , 𝐵 0 𝑛 , 𝛿 𝑛 , 𝑑 𝑠 𝑛 , 𝑑 𝑙 𝑛 , 𝑑 𝑎 𝑛 , 𝜌 𝑛 ) are estimated. The model predicted antibody levels will be {𝐴(𝑡 1 ), 𝐴(𝑡 2 ), … , 𝐴(𝑡 𝐽 )}. We assume log-Normally distributed measurement error such that the difference between 𝑙𝑜𝑔(𝑎 𝑗 ) and 𝑙𝑜𝑔 (𝐴(𝑡 𝑗 )) is Normally distributed with variance 𝜎 𝑜𝑏𝑠 2 . For model predicted antibody levels 𝐴(𝑡 𝑗 ) the data likelihood for individual n is given by As described above, for each individual there are eight parameters to be estimated. The mixed effects likelihood can be written as follows:

				𝑒	-(𝑙𝑜𝑔(𝑑 𝑙 𝑛 )-𝜇 𝑙 ) 2 2𝜎 𝑙 2 √2𝜋𝑑 𝑙 𝑛 𝜎 𝑙	( 𝑒	-	(𝑙𝑜𝑔(𝑑 𝑎 𝑛 )-𝜇 𝑎 ) 2 2𝜎 𝑎 2 √2𝜋𝑑 𝑎 𝑛 𝜎 𝑎	)		𝑒 √2𝜋𝜌 𝑛 (1 -𝜌 𝑛 )𝜎 𝜌 (𝑙𝑜𝑔( 2 𝜌 𝑛 1-𝜌 𝑛 )-𝜇 𝜌 ) -2 2𝜎 𝜌	𝐿 𝑚𝑜𝑑 𝑛	(𝜃 𝑛 |𝐷 𝑛 )
				(							)											(	)
																								2
											𝐿 𝑚𝑜𝑑 𝑛	(𝜃 𝑛 |𝐷 𝑛 ) = ∏	𝑒	-	(𝑙𝑜𝑔(𝑎 𝑗 )-𝑙𝑜𝑔(𝐴(𝑡 𝑗 ))) 2 2𝜎 𝑜𝑏𝑠 𝑎 𝑗 𝜎 𝑜𝑏𝑠 √2𝜋
	𝐿 𝑚𝑖𝑥 𝑛 (𝜃 𝑛 |𝐷 𝑛 )
	=	(	𝑒	-(𝑙𝑜𝑔(𝐴 𝑏𝑔 𝑛 )-𝜇 𝐴 ) 2𝜎 𝐴 2 √2𝜋𝐴 𝑏𝑔 𝑛 𝜎 𝐴	2	)	(	𝑒	-	(𝑙𝑜𝑔(𝛿 𝑛 )-𝜇 𝛿 ) 2 2𝜎 𝛿 2 √2𝜋𝛿 𝑛 𝜎 𝛿	) (	𝑒	-	(𝑙𝑜𝑔(𝛽 𝑛 )-𝜇 𝛽 ) 2 2𝜎 𝛽 2 √2𝜋𝛽 𝑛 𝜎 𝛽	) (	𝑒	-(𝑙𝑜𝑔(𝑑 𝑏 𝑛 )-𝜇 𝑏 ) 2 2𝜎 𝑏 2 √2𝜋𝑑 𝑏 𝑛 𝜎 𝑏	)	(	𝑒	-	(𝑙𝑜𝑔(𝑑 𝑠 𝑛 )-𝜇 𝑠 ) 2 2 2𝜎 𝑠 √2𝜋𝑑 𝑠 𝑛 𝜎 𝑠	)

𝑗∈𝐽

Mixed effects likelihood

1. Individual-level parameter Metropolis-Hastings update. For

  each participant n:  Update local parameters: 𝜃 𝑛 ′ = (𝐴 𝑏𝑔 𝑛 ′ , 𝛿 𝑛 ′, 𝛽 𝑛 ′, 𝑑 𝑏 𝑛 ′, 𝑑 𝑠 𝑛 ′, 𝑑 𝑙 𝑛 ′, 𝑑 𝑎 𝑛 ′, 𝜌 𝑛 ′)  Calculate updated mixed effects likelihood 𝐿 𝑚𝑖𝑥 𝑛 (𝜃 𝑛 ′|𝐷 𝑛 )  Accept the parameter update with probability 𝑚𝑖𝑛 (1, For each of the 𝑖 ∈ {𝐴, 𝛿, 𝛽, 𝑏, 𝑠, 𝑙, 𝑎} we obtain new estimates of the population level parameters 𝜇 𝑖 ′ and 𝜏 𝑖 ′ = where 𝜇 𝑖,0 and 𝜏 𝑖,0 parameterise the Normal prior distribution on the mean, and k and θ parameterise the Gamma prior distribution on the precision (inverse standard deviation). 𝑦 𝑖 𝑛 is the individual-level parameter i in individual n.  For ρ we obtain new estimates of the population level parameters 𝜇 𝜌 ′ and 𝜏 𝜌 ′ =

	𝜇 𝑖 ′ ~𝑁 (	𝜏 𝑖,0 𝜇 𝑖,0 + 𝜏 𝑖 ∑ 𝑙𝑜𝑔(𝑦 𝑖 𝑛 ) 𝑁 𝜏 𝑖,0 + 𝜏 𝑖 𝑁	,	1 √ 𝜏 𝑖,0 + 𝜏 𝑖 𝑁	)
	𝜏 𝑖 ′ ~Γ (	𝑁 2	+ 𝑘, ( 𝜃 1	+	1 2	∑ (𝑙𝑜𝑔(𝑦 𝑖 𝑁 𝑛 ) -𝜇 𝑖 ′) 2	-1 )	)
									1 𝜎 𝜌 ′ as
	follows:
	𝜇 𝜌 ′ ~𝑁 (	𝜏 𝜌,0 𝜇 𝜌,0 + 𝜏 𝜌 ∑ 𝑙𝑜𝑔𝑖𝑡(𝜌 𝑛 ) 𝑁 𝜏 𝜌,0 + 𝜏 𝜌 𝑁	,	1 √ 𝜏 𝜌,0 + 𝜏 𝜌 𝑁	)
	𝜏 𝜌 ′ ~Γ (	𝑁 2	+ 𝑘, ( 𝜃 1	+	1 2	𝑁 ∑ (𝑙𝑜𝑔𝑖𝑡(𝜌 𝑛 ) -𝜇 𝜌 ′) 2	) -1
									𝐿 𝑚𝑖𝑥 𝑛 (𝜃 𝑛 ′ |𝐷 𝑛 ) 𝐿 𝑚𝑖𝑥 𝑛 (𝜃 𝑛 |𝐷 𝑛 ) )
	2. Population-level parameter Gibbs update.
	 1 𝜎 𝑖 ′ as follows:
							196

Appendix Figure 3.3. Pearson correlation between antibody responses to 34 Serological markers of exposure in the Peruvian cohort.

Acknowledgements

, thank you so much for your always productive and fun teachings and discussions. Thank you for always giving me the push I needed for the presentations.

I am grateful to the PPU program for allowing me to achieve the dream of doing a PhD at the prestigious Institut Pasteur.

Conclusions

These findings highlight the opportunities and challenges of deploying SEMs as a tool to estimate recent transmission changes due to control programs in the Peruvian Amazon and may be generalised to other areas with similar endemicity and control history.

List of Abbreviations

Ab Antibody

Chapter 2 : Serological markers for vivax malaria elimination

Profiles of anti-P. vivax antibody responses in Peru

In Lupuna the geometric mean antibody response to 34 P. vivax proteins was 1.93 times higher than in Cahuide, although the distribution of antibody responses overlapped between both communities (0.0014 vs. 0.0007; p < 0.001, t-test, Appendix Figure 3.2). Henceforth, data from both communities were combined for subsequent analysis. Antibody responses to 26 out of 34 antigens were positively associated with concurrent P. vivax infections (Odds ratio range: 1.38 -1.99, p < 0.01, Appendix Table 3. 3). Most of the responses to the tested markers showed a positive correlation with the individual's age (spearman correlation factor range ρ = 0.143 -0.482; p <0.001, Figure 3.2, Appendix Table 3.4), indicating a broader antibody repertoire and higher magnitude of the responses in individuals older than 40 years old. Relative antibody units (RAU) were log10 transformed. Each vertical line represents a participant sorted by increasing age (3-85 years old). Immunogenicity of SEMs was defined as the geometric mean of log10 (RAU) across all participants in the Peruvian cohort. We used this quantitative metric to sort the antibody responses.

However, the number of individuals with at least one P. falciparum infection in the preceding 13 months of last time point was low: Thailand n = 9, Brazil n = 17, Peru n = 61.

The antibody levels to the two constructs of PvRBP2b were highly correlated (Pearson's r = 0.83, p < 0.001), so we excluded the PvRBP2b construct with the lower level of accuracy (PvRBP2b1986-2653. At a population level, the performance of antibody responses to PVX_094255 (PvRBP2b161-1454) for classifying recent infections was better in Thailand (AUC = 0.83) and Brazil (AUC = 0.79), than in Peru (AUC = 0.68) (Figure 3.4A). Moreover, there were markers such as PVX_099980 (PvMPS119) that showed good performance only in Thailand and Brazil, and others such as PVX_090240 (PvCyRPA) that performed well only in Peru.

Evaluation of antibody responses to PVX_094255B (PvRBP2b), PVX_090240 (PvCyRPA) and PVX_099980 (PvMPS119), indicates that they can be used to detect previous P. vivax infections within a wide time range of 1 -12 months, and not just the previously investigated 9 month target (Appendix Figure 3.8) (Longley et al. 2020).

Performance to detect recent exposure using combinations of antibody responses

While antibody responses to single antigens are informative for detecting recent exposure, a combination of antibody responses may lead to improved sensitivity and specificity in low-transmission regions (Longley et al. 2020). Using a linear discriminant analysis (LDA) classification algorithm, models including up to five antibody responses were tested for maximizing information of exposure in the previous 9 months. The combinations of antibody responses that were most informative of recent exposure were cross-validated, and performance plotted using ROC curves. The top combinations of antibody responses per country were the following: Thailand: PvRBP2b, PvRBP2a, Pvs16, histone-lysine N-methyltransferase SET10 and PvMSP119 (AUC = 0.87); Brazil: PvRBP2b, hypothetical PvMSP3, PvRAMA, PVX_091710 and PVX_094830

(AUC = 0.85); Peru: PvCyRPA, PvRBP1a, PVX_090970, and the members from the P. vivax Tryptophan Rich Antigens (PvTRAG or Pv-fam-a) family PVX_096995 and PVX_092995 (AUC = 0.72). The top combinations of antibody responses for the combined data was PvRBP2b, Pvs16, PVX_091710, PVX_084720 (PTEX150) and PVX_094830 (AUC = 0.86). As expected, the multi-antibody response models performed better than the single antibody ones; although to a less extent for Peru (Figure 3.4B). We further analysed the top combinations of antibody responses for each study site stratified by two age categories: 0-15 and 15+ years old (Appendix Figure 3.9). In the cohorts of Thailand and Brazil, the diagnostic performance was slightly higher in the group of 0 -15 years old (Thailand 0 -15 AUC: 0.90, Brazil 0 -15 AUC: 0.87) than the group of 15+ 52 years old (Thailand 15+ AUC: 0.83; Brazil 15+ AUC: 0.81). In both Cahuide and Lupuna, people older than 15 years old (Cahuide: 15+ AUC: 0.71, Lupuna: 15+ AUC: 0.66) were better classified than younger people (Cahuide: 0 -15 AUC: 0.51; Lupuna: 0 -15 AUC: 0.62).

Stratification of transmission was detected by seropositivity maps

In order to detect spatial patterns of exposure within communities, a seropositivity rate per household was estimated for both areas. 88% of the HH with 1.00 seropositive rate in Iquitos had 2 or less participants sampled compared to Mazan (65%) (Appendix Table 4.2).The household-level spatial distribution of individuals is represented in Figure 4.4.

In the peri-urban areas of Iquitos, Santo Tomás had the highest proportion of households with seropositive rate ≥0.75 (40%), in contrast to Rumococha (20%) and Quistococha (20%) (Appendix Figure 4.5). However, the distribution of households with high positive rate did not show spatial structure, i.e. there were households with ≥0.75 seropositive rate surrounded by households with <0.5 seropositivity rate, indicating thus an unstable transmission in Rumococha and Quistococha.

A more heterogeneous transmission landscape was found in the communities of Mazán, probably related to the geographical location of households. Mazán province is crossed by two rivers: Mazán River and Napo

River. The highest proportions of seropositive individuals at household level were detected in communities along the basin of Mazán River: Gamitanacocha (88%), Libertad (63%) and Primero de Enero (60%).

Although also located in the basin of Mazán River, only 31% of the households in Puerto Alegre had ≥0.75 seropositive rate. While the proportion of households with high seropositive rate in the communities of the basin of Napo River were the following: Urco Miraño (45%), Salvador (20%) and Lago Yuracyacu (4%) (Appendix Figure 4.5). The exposure map in Mazán indicates that transmission is stable and more frequent in households rounding the Mazán River than the Napo River.

Chapter 5 : Multiplex assays for the identification of serological signatures of SARS-CoV-2 infection: an antibody-based diagnostic and machine learning study

Authors and Affiliations

Jason Rosado ,8 , Benjamin Terrier 9,10 , Samira Fafi-Kremer 11,12 , Jerome de Seze 13 , Timothée Bruel 14,15 , François Dejardin 16 , Stéphane Petres 16 , Rhea Longley 17,18 , Arnaud Fontanet 19 , Marija Backovic 6 , Ivo Mueller 1,17,18 , Michael T White 1 *

Serological assays

We optimized a 12-plex assay for detecting IgG and IgM antibody responses against seven SARS-CoV-2 antigens (two Nucleoproteins constructs, five Spike), and one Nucleoprotein for each seasonal coronavirus NL63 and 229E. Three antigens from other viruses were included (Influenza A H1N1, adenovirus type 40, rubella), for which a large part of the population is expected to be seropositive due to vaccination or natural infection and hence serve as internal controls (Appendix Table 5.1). All proteins were coupled to magnetic beads as described elsewhere (Longley et al. 2017a). The mass of proteins coupled on beads were optimized to generate a log-linear standard curve with a pool of positive serum prepared from RT-qPCRconfirmed SARS-CoV-2 patients.

Two separate assays were used for measuring IgG and IgM antibodies. For each assay, samples were run on 96 well plates, containing two blanks (only beads, no serum) and a standard curve prepared from twofold serial dilutions (1:50 to 1:25600) of a pool of positive controls. Plates were read using a Luminex® MAGPIX® system and MFI was used for analysis. A 5-parameter logistic curve was used to convert MFI to antibody dilution, relative to the standard curve performed on the same plate to account for inter-assay variations. The multiplex immunoassay was validated by checking that the MFI obtained were well correlated with those obtained in monoplex (only one conjugated bead type per well). For non-SARS-CoV-2 antigens, MFI data was used for the analysis. Analytic validation of the assay was implemented by comparison with data from two other immunoassays on matched sample sets. Further details of assay validation are provided in Appendix Section 5.1.1.

Statistical evaluation of diagnostic performance

For antibody responses to a single antigen, diagnostic sensitivity is defined as the proportion of patients with RT-qPCR confirmed SARS-CoV-2 infection with antibody levels above a given seropositivity cutoff. For assessment of classification performance, samples taken from individuals less than 10 days after symptom onset were excluded, because antibody levels have often not yet increased during this time period.

Diagnostic specificity is defined as the proportion of negative controls (with no history of SARS-CoV-2 infection) with antibody levels below a given seropositivity cutoff. This trade-off between sensitivity and specificity is evaluated using Receiver Operating Characteristic (ROC) analysis.

Applying a statistical correction to account for imperfect sensitivity and specificity, we can obtain more accurate estimates of seroprevalence (Figure 5.4C). For both the monoplex and multiplex serological assays, the adjusted estimates are not accurate when the false positive rate exceeds the true prevalence.

Figure 5.4B-C presents the scenario when seroprevalence is known to be 5%. In real applications, true seroprevalence is not known a priori. For a range of seroprevalence from 0.1% to 100%, Figure 4D presents values of the assay's sensitivity and specificity that have been optimized to minimize the expected relative error. For a monoplex assay based on anti-S tri IgG antibodies, if true seroprevalence <20% the relative error is minimized when we select specificity >99%. When true seroprevalence <2% the relative error is minimized when specificity = 100%. For a multiplex serological assay, if true seroprevalence <30%, the relative error is minimized when we implement an algorithm with specificity = 100%. Figure 5.4E presents a comparison of the expected relative error for the monoplex and multiplex assays. The expected relative error depends on the possible values of sensitivity and specificity, as well as the uncertainty in these estimates. For true seroprevalence >2% the monoplex assay has lower error (a consequence of the lower levels of variation in the ROC curve). For true seroprevalence <2%, the multiplex assay has lower error, a consequence of the high levels of specificity.

General discussion

The objectives of this work were to validate a serological assay for identifying people at high risk of relapsing P. vivax infections in longitudinal studies conducted in high malaria transmission settings of Loreto (highway and riverine communities) and apply the validated serological assay to measure the exposure to P. vivax in cross-sectional surveys executed in low and moderate transmission areas of Loreto (peri-urban and rural areas). Finally, the adaptation and development of a multiplex serological assay for SARS-CoV-2 using the same principles as the developed P. vivax antibody assay. In this last chapter, the main findings will be recapitulated, and we will discuss how they provided new knowledge about the immunity to P. vivax in the Peruvian Amazon. Ultimately, these findings could be interpolated to different geographic regions with similar malaria endemicity.

2017).

The second study was a cross-sectional survey that aimed to characterise the level of exposure to vivax malaria by serological and molecular tools (Chapter 4). This cross-sectional survey was executed in two geographically different areas of Maynas province, department of Loreto: Peri-urban areas of Iquitos and riverine communities of Mazán. Santo Tomás, Quistococha and Rumococha were the communities selected from the peri-urban areas of Iquitos. Early studies in peri-urban areas of Iquitos demonstrated the association of malaria transmission with deforestation activities due to the creation of Nauta-Iquitos road [START_REF] Vittor | The effect of deforestation on the human-biting rate of Anopheles darlingi, the primary vector of Falciparum malaria in the Peruvian Amazon[END_REF]. The most common occupations were commerce, mototaxi and housewife [START_REF] Chuquiyauri | Socio-demographics and the development of malaria elimination strategies in the low transmission setting[END_REF]. In Mazán, seven communities were selected: Gamitanacocha, Libertad, Puerto Five hundred ninety (590) individuals from the Peruvian cohort were included from which 350 were followed up to 37 months, and 240 individuals were followed for the last 13 months of the study. For the Brazilian (13 months) and Thailand (14 months) cohort, 925 and 826 individuals were included. In the three cohorts, a monthly blood sample was taken for diagnosis by light microscopy and qPCR. "0" denotes the last time point of following up where a serum sample was taken for Luminex assay. Appendix Table 3 ¥ Occupation in Peru: Farmer (30.83%), fishing (1.73%), woodcutter (5.76%). # : Individuals followed for 13 months (n=240), individuals followed for 37 months (n=350). a : Significant differences between Thailand and Peru, p<0.05; b : Significant differences between Brazil and Peru, p<0.05; c : Significant differences between Thailand and Brazil, p<0.05; d : Significant differences between Brazil and Peru, p<0.05. ⱡ Prevalence includes single -and mixed-species infections.

Appendix

Appendix Table 3 Abbreviations: GMT = Geometric mean titer; 95% CI = 95% confidence interval. * Data of relative antibody titers interpolated from standard curves were geometric mean with 95% confidence interval (CI). IgG levels were multiplied by 1000. Participants were categorized according to the time since last qPCR detection in the last year. Kruskal-Wallis test by ranks was used for analysing the variance among groups.

Appendix Table 3 Abbreviations: GMT = Geometric mean titer; 95% CI = 95% confidence interval. * Data of relative antibody titers interpolated from standard curves were geometric mean with 95% confidence interval (CI). IgG levels were multiplied by 1000. Participants were categorized according to their number of qPCR detection in the last year. Kruskal-Wallis test by ranks was used for analysing the variance among groups.

Appendix

Comparison with other immunoassays

The

Pairwise comparison of classification performance of biomarkers

The classification performance of a biomarker can be assessed on its potential to correctly classify positive and negative samples, where classification will depend on the choice of cutoff. The classification performance of two biomarkers can be statistically compared using McNemar's test. For cutoffs corresponding to a high specificity target, Appendix Table 5.2 provides a pairwise comparison of the classification performance of 14 SARS-CoV-2 biomarkers (7 IgG and 7 IgM). The best individual biomarker is anti-S tri IgG antibody levels, which provide significantly better classification than all other biomarkers.

Appendix Table 5.2. Comparison of classification performance between biomarkers for a high specificity target (>99%). Pairwise comparisons are made using

McNemar's test. The above diagonal element shows the odds ratio with 95% confidence intervals. Odds ratio > 1 indicates that biomarker indicated by the row has better classification than the biomarker indicated by the column. The corresponding element below the diagonal presents the P value.

S tri RBDv1 RBDv2

S1 S2

RBDv1 IgG

< 10 -10 0•76

43 (16•5, 159•6) 2•18

25•67 (11•5, 71•0) 21•1

45 (17•3, 166•9) 2•06

29•17 (13•1, 80•6) 66 (22•3, 322•8) 32•2 (13•5, 100•5) 25•83 (11•6, 71•5)

S1 IgG

< 10 -10 < 10 -10 < 10 -10 0•4

S tri IgM

< 10 -10 < 10 -10 < 10 -10 < 10 -10 < 10 -10 < 10 -10 < 10 -10 0•01 68•5 (18•6, 571•3) 162 (28•7, 6437) 41•67 (13•9, 204•8) 24 (10,75•26) RBDv2 IgM 39 (13•0, 191•8) 47•67 (16•0, 233•9) 26•25 (9•95, 98•13) 14•43 (6•75, 36•8)

S1 IgM

< 10 -10 < 10 -10 < 10 -10 < 10 -10 < 10 -10 < 10 -10 < 10 -10 0•21 < 10 -10 < 10 -10 7•5

Results of multiplex classification with random forests algorithm

The classification performance of a diagnostic test can be assessed against a number of sensitivity and specificity targets. Here we consider three targets in detail:

 high sensitivity target: sensitivity > 99% is enforced, and subject to these constraints specificity is maximised.

 balanced target: sensitivity and specificity are equally weighted, so that a combination that minimises |sensitivity -specificity| is selected.

 high specificity target: specificity > 99% is enforced, and subject to these constraints sensitivity is maximised.

For these three targets, Appendix Table 5.3 shows the sensitivity and specificity for the 14 individual biomarkers, plus multiplex combinations up to size six. Multiplex combinations were selected on their potential to optimize the high specificity target. For example, anti-S tri IgG antibody levels provided the greatest sensitivity for combinations of size 1. Therefore, for combinations of size 2, anti-S tri IgG antibody levels were automatically included, and all other possible biomarkers were included in combinations. The 187 pairwise combination that maximized sensitivity was anti-S tri IgG antibody levels and anti-RBD v2 IgG antibody levels. In general, the optimal multiplex combination of size n was selected by starting with optimal multiplex combination of size n -1 and adding possible remaining biomarkers.

Note that the routine described above does not necessarily guarantee that the optimal combination of size n will be selected. To account for this possibility, we tested all possible combinations of IgG antibody levels to the seven SARS-CoV-2 antigens. This resulted in a total of 2 7 -1 = 127 combinations. The output of all of these combinations against a range of targets is summarized in detail in a spreadsheet provided in the GitHub repository associated with the publication (https://github.com/MWhite-InstitutPasteur/SARS_CoV_2_SeroDX_phase2/blob/master/SARSCoV2_IgG_antigen_combination.xlsx).

Appendix Table 5.3. Sensitivity and specificity targets for single biomarkers and multiplex combinations. 95% binomial confidence intervals were calculated using Wilson's method. Antigen combinations were selected to optimize sensitivity for the high specificity target, i.e. the highest sensitivity while enforcing specificity > 99%.

Antibody kinetics models

Prior longitudinal data on long-term antibody responses to coronaviruses

There are limited available longitudinal data on SARS-CoV-2 antibody kinetics, and no data from long-term follow-up (as of July 2020). However, there are a number of published studies on the long-term antibody kinetics to other coronaviruses, most notably Severe Acute Respiratory Syndrome coronavirus (SARS-CoV). Here we review some of the available published data, and describe how this can be used to provide prior information for modelling SARS-CoV-2 antibody kinetics.

Appendix Table 5.4 summarises some of the published data on the long-term antibody kinetics to a number of coronaviruses: SARS-CoV, human seasonal coronavirus 229E, and Middle East Respiratory Syndrome coronavirus (MERS-CoV) (Callow et al. 1990[START_REF] Liu | Two-year prospective study of the humoral immune response of patients with severe acute respiratory syndrome[END_REF][START_REF] Mo | Longitudinal profile of antibodies against SARS-coronavirus in SARS patients and their clinical significance[END_REF][START_REF] Cao | Disappearance of antibodies to SARS-associated coronavirus after recovery[END_REF][START_REF] Wu | Duration of antibody responses after severe acute respiratory syndrome[END_REF][START_REF] Tang | Lack of peripheral memory B cell responses in recovered patients with severe acute respiratory syndrome: a six-year follow-up study[END_REF][START_REF] Choe | MERS-CoV Antibody Responses 1 Year after Symptom Onset, South Korea, 2015[END_REF]. From the extracted time series, we estimated two summary statistics characterizing the long-term antibody response: the half-life of the long-lived component of the antibody response, and the percentage reduction in antibody response after one year. The half-life of the long-lived component of the antibody response was estimated by fitting a linear regression model to measurements of (log) antibody response taken greater than six months after symptom onset. The percentage reduction in antibody response after one year was estimated based on the reduction from the peak measured antibody response to the estimated antibody level at one year. Although a wide range of assays from ELISA to micro-neutralisation were used in the reviewed studies, in this simple and approximate analysis we did not attempt to account for assay dependent effects, except to subtract background antibody levels where necessary.

Based on the estimated summary statistics, we assume that the long-term IgG antibody kinetics can be characterized as having a half-life of dl = 400 days with a 60% reduction after one year. In terms of the parameters of the mathematical model of antibody kinetics, this corresponds to prior estimates of cl = log(2)/dl = 0.0017 and ρ ~ 0.9. For sensitivity analyses, we also considered scenarios where dl = 200 days and dl = 800 days. For IgM antibody kinetics, we assumed dl = 100 days and ρ ~ 0•9. For sensitivity analyses, we also considered scenarios where dl = 50 days and dl = 200 days.

A prior estimate of the half-life of IgG molecules of 21 days is assumed [START_REF] Mankarious | The half-lives of IgG subclasses and specific antibodies in patients with primary immunodeficiency who are receiving intravenously administered immunoglobulin[END_REF]. A prior estimate of the half-life of IgM molecules of 10 days is assumed (Kenneth M. [START_REF] Murphy | Janeway's Immunobiology, Ninth Edition: Garland Science[END_REF]. Prior estimates for the short-lived component of the antibody response (half-life = 3•5 days) are consistent with data from several sources (Andraud et al. 2012, White et al. 2014a, White et al. 2014b, White et al. 2015, Teunis et al. 2016).

Appendix Table 5.4. Prior data on the duration of antibody responses to coronaviruses. Data from longitudinal studies on measured antibody levels to SARS coronavirus, seasonal coronavirus 229E, and MERS coronavirus. For each study, the time series describing the antibody kinetics was extracted. The half-life of the long-lived component of the antibody response was estimated using measurements of antibody response measured after 6 months from symptom onsetthe subset of the data used for this calculation is indicated in bold below. The percentage reduction in antibodies after one year is estimated based on the reduction from the peak measured response to the estimate antibody level at year. study half-life (days) 1 year reduction SARS-CoV; Wu et al. [START_REF] Grzelak | A comparison of four serological assays for detecting anti-SARS-CoV-2 antibodies in human serum samples from different populations[END_REF] time (days)

Fit of antibody kinetic model to data.

For all seven SARS-CoV-2 antigens, we fit the mathematical models of antibody kinetics to data on IgG antibody levels. We did not attempt to fit the model to data on IgM antibody levels because of higher levels of noise in the IgM assay, and a lack of prior data on the long-term kinetics of IgM antibodies. Prior and posterior parameter estimates with 95% credible intervals are presented in Appendix Table 5.5. Appendix Figure 5.5 shows data from a patient from Hôpital Bichat with frequent longitudinal sampling.

The data and model indicate that the antibody response is in a rising phase between 5 and 30 days after symptom onset. The seroconversion time depends on the seropositivity cutoff. For the cutoffs shown, seroconversion occurs for anti-S tri IgG, anti-RBDv1 IgG and anti-S2 IgG, but not for anti-RBDv2 IgG, anti-S1 IgG, anti-NPv1 IgG and anti-NPv2 IgG.

For all 215 individuals with RT-qPCR SARS-CoV-2 infection, Appendix Figure 5.5B shows the model predicted IgG antibody response to SARS-CoV-2. For all antigens, we predict a bi-phasic pattern of waning with a first rapid phase between one and three months after symptom onset, followed by a slower rate of waning. The percentage reduction in antibody level after one year was mostly determined by prior information and estimated to be 47% (95% CrI: 18%, 90%) for anti-S tri IgG antibodies, with comparable estimates for other antigens (Appendix Figure 5.5B & Appendix Table 5.6). Sensitivity was assessed using the seropositivity cutoff based on a high specificity target (>99%). For all antigens considered, we predict that there will be a reduction in sensitivity over time, although there is a large degree of uncertainty (Appendix Figure 5.5C). For each of the 215 individuals sampled, the mathematical model of antibody kinetics was used to simulate estimates of antibody levels during the first year of follow-up. To this data, we applied monoplex and multiplex classification algorithms to assess diagnostic sensitivity within the first year of follow up.

These results are presented in Figure 3 (main manuscript) and in Appendix Table 5.6.

Appendix

Antibody kinetics: case study of Honk Kong data

In order to obtain prior estimates of the parameters describing the early stage kinetics of the antibody response following SARS-CoV-2 infection, we performed a secondary analysis of data from patients admitted to Princess Margaret Hospital and Queen Mary Hospital in Hong Kong, following the primary analysis by To, Tsang et al (To et al. 2020). 23 patients with RT-qPCR confirmed SARS-CoV-2 infection were followed longitudinally for up to four weeks after initial onset of symptoms. Ten patients had severe COVID-19, all of whom required oxygen supplementation, and 13 patients had mild disease.

The Hong Kong based team expressed and purified recombinant proteins for receptor-binding domain --22 [START_REF] Longley | Development and validation of serological markers for detecting recent Plasmodium vivax infection[END_REF]67) 23 [START_REF] Longley | Development and validation of serological markers for detecting recent Plasmodium vivax infection[END_REF]73) half-life of IgG molecules (days)