Julien Duribreux

Romain Rouvoy

Martin Monperrus

An Energy-efficient Location Provider for Daily Trips

Keywords: energy-efficient, smartphone, clustering, location inference Energie, smartphone, GPS, localisation

It is well known that smartphones are energy greedy and their batteries do not last more than a few days. Since mobile phones consumption increased faster than batteries capacities it becomes important to find new ways to reduce their energy consumption. We claim that people using GPS location service everyday should not pay a full energy-cost since most of the time their are taking the same path to commute. Based on these observations, we developed a new implementation of the location API built from Android Location API, which is capable of learning and predict user location on time with an average accuracy of 50 meters while saving energy.

Key-

Introduction

It is not a secret, smartphones are taking a bigger place in our society and manufacturers tend to increase their sizes and power everyday. Because of that, smartphones can only stay unplugged one or two days in a raw without us having to refill their batteries. However, autonomy is one of the most important and challenging user requirement. To face that reality, we need to constantly improve energy policy in smartphones to increase the battery lifetime.

Additionnally, we observe that some applications tend to drain batteries faster than the others. New bugs appear-so called energy-bugs [START_REF] Pathak | What is keeping my phone awake?: characterizing and detecting nosleep energy bugs in smartphone apps[END_REF]-and one cannot directly notive them because applications are working perfectly, but behind the scene these bugs are draining the battery. For example, no-sleep bugs are defined as energy bugs resulting from mis-handling power control APIs in an app or framework, resulting in the smartphone components (screen, cpu, gps. . . ) staying on for an unnecessarily long period of time [START_REF] Pathak | Where is the energy spent inside my app?[END_REF], which is a big deal considering we already have limited autonomy. To improve the battery lifetime, we therefore have to develop and deploy smarter algorithms able to adapt application behavior to reduce energy cost.

Li et al. [START_REF] Gui | An Empirical Study of the Energy Consumption of Android Applications[END_REF] show that a major cause of energy leaks in mobile applications are communication and screen display, CPU is no more that greedy thanks to frequency scaling policy used in recent mobiles. On our side, we decided to focus on the GPS knowing that a typical smartphone will completely drain its battery in about 6 hours if the GPS is running continuously. Note that most of the energy is used in computing the location, not retrieving data from satellites [START_REF] Liu | Energy efficient GPS sensing with cloud offloading[END_REF] since there is no communication from the smartphone to satellites.

The key contributions of this paper are therefore:

1. A state of the art of energy-efficient software-based optimizations on mobile phones, 2. A low cost technique to measure mobile phones energy consumption at run-time,

3. An energy-efficient location computing process based on machine learning.

State of the Art

Many researchers all around the world are working on the energy challenge for mobile devices to figure out new ways to reduce their hardware and applications consumption. In this paper, we are focusing on software-based solutions because it does not require any hardware manipulations or Android SDK modifications and we think there is more potential for real-world deployment.

Energy-consumtion Understanding

Saving energy is only possible with good understanding of smartphones and applications behaviors. In this part we are focusing on tools and analysis toward a better understanding of smartphones.

Pathak et al. present the first comprehensive real-world no-sleep energy bug characterization study, then they also proposes a detection solution based on the classic reaching definitions dataflow analysis, to automatically infer potential no-sleep bugs in an app [START_REF] Pathak | What is keeping my phone awake?: characterizing and detecting nosleep energy bugs in smartphone apps[END_REF]. This is a good first step to understand how does the energy consumption model works in mobile phones and helps developers to build great energy-efficient applications.

Bavota et al. investigate energy-greedy Android APIs, with the purpose of understanding particular instances of API calls and API usage patterns that cause high energy consumption [START_REF] Bavota | Mining Energy-Greedy API Usage Patterns in Android Apps: An Empirical Study[END_REF]. However, they were only interested in patterns composed of calls to public Android methods. For this reason, they removed Android non public methods (private and protected methods) from the identified patterns. In particular, analysis shows that APIs related to GUI and Image Manipulation and Database represent, all together, 60% of the energy-greedy APIs.

Li et al. present a tool able to compute source line level energy consumption information combining hardware-based power measurements with program analysis and statistical modeling [START_REF] Li | Calculating source line level energy information for Android applications[END_REF].

Their approach is fast, it can calculate source line level energy measurements for each of their subject applications in less than 3 minutes and is able to calculate energy consumption within 10% of the ground truth measurements.

Offloading Strategies

Those strategies are based on the fact that it may take time to achieve heavy computing operations. Thus, it might be interesting to offload those calculus on a remote server and get final results. Even if sending data will increase consumption at some point, it is possible to achieve energy saving at application level because of the heavy computations.

Aggarwal et al. observe that radio communications are a significant cause of battery energy drain in smartphones. They tried to reduce this cost by using aggregation, instead of sending request one by one, they merge requests into communication spurs to reduce the number of state switches (low and high power mod), or using data compression with a cloud proxy to compress data before sending it. To this end, they employ an asymmetric, lossless, dictionary-based redundancy elimination technique, with minimal decompression overhead. Thus, bandwidth savings translate into equivalent energy savings. And finally, using scheduling, they send data based on signal variations [START_REF] Aggarwal | Stratus: Energy-Efficient Mobile Communication using Cloud Support[END_REF]. Overall this technique suffers from its very specific behavior because it relies on a distant server able to (de)compress data which is non-standard and would probably be difficult to deploy at large.

Kwon et al. use annotation to automatically transforms the application to enable it to offload parts of its functionality to the cloud. Doing that, they can effectively reduce the overall amount of energy consumed by these applications, with the actual numbers ranging between 25% and 50%. To do so, the offloading decisions should be made dynamically at runtime and continuously adjusted in response to the fluctuations in the mobile execution environment. Depending on the runtime execution environment, different portions of a program's state can be checkpointed and transferred across the network as required by the offloading strategy in place. To select the optimal offloading strategies, they combine dataflow and side-effect analyses. Based on the results, a bytecode enhancer then rewrites the application without changing its source code. In particular, they used Soot to implement their program analysis and transformations [START_REF] Kwon | Reducing the Energy Consumption of Mobile Applications Behind the Scenes[END_REF].

Merrer et al. created Hoop, a tool based on the fact that it takes time to upload HD pictures, videos and other heavy media on Internet. The idea is quite straightforward: instead of directly uploading the file to the online service, the user browser encrypts and uploads the file to the gateway, together with an authentication token, at a speed determined by the Wi-Fi connection of the access point. At this point, the user can disconnect from the access point, and potentially move and switch off her device, while the file is being asynchronously uploaded by the gateway. The offload time is reduced by up to a factor of 85 in a wired setting and by up to a factor of 46 in the wireless settings [START_REF] Le Merrer | Hoop: Offloading HTTP(S) POSTs from User Devices onto Residential Gateways[END_REF]. However, their optimization relies on custom gateway that may not be available everywhere and even if there is a gain in time, there is no energy saving since the gateway does the job.

Delaying Strategies

Those strategies focus on the fact that sending data as soon as possible may not be the best choice because of the components energy tail state. If the application is delay-tolerant, delay some operations avoid useless tail state and doing so, save energy.

Wang et al. try to find out how the energy consumption characteristics vary depending on the packet size and transmission interval, and also how to minimize the power consumption in case of constant bit rate data transmission [START_REF] Wang | Power consumption analysis of constant bit rate data transmission over 3G mobile wireless networks[END_REF]. They proposed a power consumption model based on their experimental results, taking into account packet sending intervals and packet size. Their results show that proposed parameters selection allows to significantly decrease the uplink power consumption on the mobile device taking signalling traffic and latency restrictions into account.

Balasubramanian et al. compare energy consumption of different mobile networking technologies. Long story short, it appears that WiFi is the most efficient way to exchange data once we are connected to an access point (AP). Otherwise, 2G is more efficient than 3G except for big transfert and strong signal. Although, this is interesting to understand that WiFi searching for AP consumes a lot of energy. They also introduce TailEnder that aggressively prefetches data, including potentially useless data, and yet reduces the overall energy usage. Delaying data sending can reduce energy by 35% for email applications, 52% for news feeds, and 40% for web search [START_REF] Balasubramanian | Energy Consumption in Mobile Phones: A Measurement Study and Implications for Network Applications[END_REF]. Even if this is a powerful strategy, this is only working on time tolerant applications, which cannot always be accepted for mobile apps.

Nikzad et al. create an annotation language (APE) to demarcate a power-hungry code segment whose execution is deferred until the device enters a state that minimizes the cost of that operation [START_REF] Nikzad | APE: An Annotation Language and Middleware for Energy-Efficient Mobile Application Development[END_REF]. Empirical evaluation shows that APE introduces an negligible overhead and achieve 63.4% energy savings compared to the case when there is no coordination between applications to save up energy.

Code Modification Strategies

The main idea here is that original source code may not be energy-efficient. To improve their behaviour, those strategies generate some alternatives versions of the same application searching for the most energy-efficient. [START_REF] Manotas | SEEDS: a software engineer's energyoptimization decision support framework[END_REF]. It generates application versions implementing many different alternative combinations of API implementation choices for all the object instantiation locations in the application. It performs power-monitored executions for a given test suite on all the generated versions, analyzes the collected energy usage data to identify the best combination of API implementation choices per object allocation location, and generates an optimized version of the application based on API implementation decision-making. SEEDSapi optimizes Java applications by identifying implementations of the Java Collections API that are more energy efficient by directly modifying the application's bytecode, if any, than the implementations currently used by the application. SEEDSapi improved the energy usage of subject applications by between 2 and 17%.

Manotas et al. create SEEDSapi

Li et al.

propose a new technique for automatically transforming the color scheme of a mobile web application [START_REF] Li | Making web applications more energy efficient for OLED smartphones[END_REF]. The approach rewrites the server-side code and templates of a web application so that the resulting web application generates pages that are more energy efficient when displayed on a smartphone. In the evaluation of their approach, they showed that they can achieve a 40 percents reduction in display power consumption. A user study indicates that the transformed web pages are acceptable to users with over 60 percents indicated that the transformed version would be acceptable for general use given the energy savings, and over 97 percents said it would be acceptable for use if the battery power was critically low.

Thokala et al.

propose Virtual GPS, a middleware layer that provides current location to the applications in a power efficient manner using a cross-layer approach [START_REF] Kumar | Virtual GPS: A Middleware for Power Efficient Localization of Smartphones Using Cross Layer Approach[END_REF]. The main contribution of their work is energy efficiency in determining the location by considering accuracy needed of applications and dynamically determining sensor type using. To do so, they use multiple techniques depending on that precision is required. Using the Virtual GPS layer to abstract location sensing provides solution that could save around 30% of energy when compared to existing solutions.

Synthesis A lot of work as already been done in this field, but a lot more has to be done if we want to keep increasing size and power of smartphones. In our study, we are focusing on location computing because we think that we can achieve major energy savings.

3 Materials and Methods

Hardware Workbench

In our experiment we use a Samsung I9300 Galaxy S III, which is a recent and powerful smartphone running on Android 4.3.1 with a custom CyanogenMod firmware [START_REF] Cyanogen | [END_REF], and we capture energy consumption with a java application running on a MacBook Pro.

Hardware Setup

To plug our ammeter, we need to pull out the battery, wire our device between the positive out of the battery and the positive pin inside the smartphone. Then, we link both minus together. Next, we plug the ammeter with mini-usb to a computer and it is ready to run (cf. Figure 1). Notice that we cannot recharge our battery inside the smartphone now, to avoid having to unmount everything we use a direct current lab generator configured to deliver 5V at 0.6mA. We could go to 1.2mA but batteries do not like fast-recharge, so to preserve it, we reduce the output current.

Why Using Cyanogen?

The Samsung I9300 battery has 4 small pins to communicate and transfer energy to the smartphone. Due to lack of room to link all of them together, we only focused on the significant ones, which means the positive and negative power pins. Because of that the Samsung front-end was able to detect a malfunction on the battery and keep dropping pop-ups warning to us about that. Obviously, this is not a kind of behavior we want because it may put the application running on foreground to the background and changing the energy consumption of the smartphone. Furthermore, Samsung front-end adds functionalities, which is great for a friendly user interface, but behind the scene it may cause energy overhead while running non-critical services. Changing to Cyanogen ensures us to have a clean install on the smartphone with minimum exotic extras running. Note that we also use the smartphone airplane mode to avoid energy noise from other components like WiFi, 3G. . .

Energy Usage Capture

To be able to quantify energy gain from our optimizations, we need to measure precisely the smartphone energy consumption. To do so, there are two major techniques. The first one is to estimate energy usage using energy models based on hardware characteristics with tools like PowerTutor [START_REF] Powertutor | [END_REF]. Overall this technique can suffer from low precision and induce energy consumption overhead due to the energy estimation process. And, of course, every smartphone is different, which means that if we want to catch energy consumption on a different devices we have to re-configure the energy model with parameters like screen size, battery capacity, CPU frequency scaling. . . With the second one, we can measure energy usage of the physical device, which gives us a great precision, but tools like Monsoon power meter [START_REF] Monsoon | [END_REF] use to be very expensive and it requires some basic knowledge in circuitry. To avoid these limitations, we use Yocto-Amp from Yoctopuce [START_REF] Yoctopuce | RR n°8586 RESEARCH CENTRE LILLE[END_REF]. This device is a digital USB ammeter that allows us to measure current automatically. It can provide rather accurate digital measures (2 mA, 1%) and it is very easy to use because of its great API avaible in 10 languages, giving us the possibility to do whatever we want with the ammeter input data.

Motivations for a Smart Location API

To achieve energy saving we decided to focus on software-based solution because we can use them with most of the applications on the Google Play Store. Indeed, modifying Android SDK or the smartphone hardware would require a very specific configuration to run our optimization and so, would decrease our potential impact on real world devices running on various Android versions and hardware.

We claim that people using GPS location service daily should not pay a full energy-cost since most of the time their are taking the exact same path, therefore we should be able interpolate their position in real time based on traveling patterns analysis and save battery lifetime.

To achieve energy saving, we try to reduce the number of GPS calls and avoid tail state statuses by learning commuting road trips and then, the next time user takes the same road, instead of calling for a GPS fix our API will compute what should be the next location based on previous location information. Doing so, we are trying to avoid energy tail state, which is a well known phenomenon causing energy leaks [START_REF] Nicholas D Lane | Piggyback CrowdSensing (PCS): Energy Efficient Crowdsourcing of Mobile Sensor Data by Exploiting Smartphone App Opportunities[END_REF] and save up energy used to compute user location (cf. Figure 2).

Algorithm Description

Learning Phase

To expect being able to estimate user location, we first need to learn its daily travelling patterns using GPS. Every GPS fix is what we call a GPSNode and Clusters are built with them (cf. Figure 3).

First During this phase, we need to collect as many locations as possible like any navigation application does which means continuously request for GPS fixes. Of course we do not expect that this kind of behavior save any energy considering it is the same behavior as usual;

Second Every time we get a fix, we store its information in a SQLite database, which means latitude, longitude but also speed, bearing, altitude and timestamp. Plus, we ignore information coming from fixes with accuracy below 15 meters to insure a decent accuracy while learning the trip;

Third For each new GPSNode, we check the distance from the closest cluster, if it is the same cluster as before, the user did not move, so we decide to ignore this fix. If it is within our range limit (15 meters), we update its information doing arithmetic means. If it is not the case, we create a new cluster with GPSNode information.

Fourth In addition, we also need to know how long did it take to move from cluster to cluster.

To do so, we need another relation which contains time between them and the orientation, as going from A to B is not the same than B to A (cf. Figures 4 and5). Note that we only consider the time between clusters, the time spent inside a cluster does not count and we fixed minimal distance between clusters to 15 meters.

Cluster A Cluster B

from A to B in T time road

Prediction Phase

Once we got enough Clusters our custom LocationManager is able to estimate current user location using time elapsed between last prediction. We fix the minimum duration between GPS calls to 11 seconds to avoid energy waste in tail states, but every second we predict what should be the actual user location like a real GPS does. When a real gps fix occurs, we compare our estimated location to the real one and decide or not to increase time between calls depending on how precise we are.

Path Selection

It may happen that some parts of the road are shared by different trips and at some point the user decides to go for one or the other. In those cases, it is important to make the most probable while App is running and every F seconds do In our experiment, we are considering how many times both tracks have been chosen and go for the most chosen. Sometimes, it may not be enough, but other techniques should be considered: using average timestamp and make the decision on what time it is instead of the number of occurrence.

T ← T + 1; if T mod R

Mobility Correction

Obviously, road traffic may not be the same, so time between clusters may change and fails the prediction in a big way if the car stops, for example. To avoid this kind of problem, we are using the accelerometer inside the mobile phone to know if we are moving or not. It should be apparent that in order to measure the real acceleration of the device, the contribution of the force of gravity must be eliminated. This can be achieved by applying a high-pass filter. Then, we make the sum of all acceleration axis and compute the absolute value.

σ = M ath.abs(α + β + γ)
Empirical study showed that sigma value must be of 0.25 (cf. Figure 6). Under this critical point, the car is not moving and above it is. It is important to notice that, due to the vibrations of the car and high sensors sensitivity, the value cannot be 0. Plus, even if we are moving at constant speed, which means acceleration should be close to the non-moving one, we are taking benefits of the variations of the road like holes, surface contact which increase vibrations amplitude to make make the difference between those two movement states. Also it may happen that in very rares circumstances, we got short periods of time while σ is below our limit. Due to the getTimestamp method accuracy 1ms may be changed in 100ms, to avoid false positive, we only arbitrary consider period of stability above 500ms.

Dalvik Bytecode Modification

Due to lack of time, this part is not fully explored, but preliminary results are encouraging. To be able to easily use our API inside another application, we decide to match almost perfectly the RR n°8586 To use the new location API in existing applications, without having source code, we need to work on the Dalvik Bytecode. To do so, we are using Dexpler [START_REF] Bartel | Dexpler: converting android dalvik bytecode to jimple for static analysis with soot[END_REF], based on Soot, which is a language manipulation and optimization framework. Since our modification is hidden behind our custom LocationManager, we just have to find and modify the Android LocationManager objects inside the targeted application and change it with our optimized one. When the application requests for location, instead of returning the last known location we got from real GPS fix using getLastKnownLocation method from the Android Location API, we will give away our last prediction computed by our prediction component.

About this technique :

Pro We can modify any application even if we do not own the source code.

Con Modifying APKs forced us to resign the application with our personal key. Doing so the application signature would be the same as the original and we will no longer benefit from future updates.

Results

This section presents an evaluation of our approach. First, we discuss the consumption overhead induce by our custom LocationManager during the learning phase. Then, we compare the energy consumption during the prediction and analyse how accurate our predictions are. Our optimization has been tested on the same application, running the Android Location Provider and our custom Location Provider during a road trip of 2.5km and 3.2km. While learning, we can hardly expect any energy saving because we need at first to capture user trips and to do so, request for GPS fixes continuously and store all those data. Plus, even if there is no prediction yet, our prediction component is already running, trying to find nearby cluster to compute the next location. As expected, learning creates an energy overhead of about 11% (cf. Figure 7), which seems acceptable considering GPS will drain the battery in only 6 hours, it represents about 40 minutes loss. Overall, this only occurs during roads discovery, as once the road is known, the prediction component will reduce the number of GPS calls and hopefully save up energy.

Prediction Phase

Once the road is well known, the number of GPS calls will start to decrease progressively starting at 1 every 11 seconds and, if predictions are good enough, it will keep going down. This choice is motivated by the duration of the tail state (cf. Figure 2). After 11 seconds, the period of heavy consumption ends and only few seconds of low consumption remains, +8% compared to the idle state. Plus, we want to avoid long period of no location verification because our prediction may go wrong and sending wrong location at lost energy-cost is not what we want to achieve.

Despite our best efforts, the application struggles to achieve energy saving. Even if at some points global consumption drops down under the regular GPS consumption, the computing overhead counter our energy gain (cf. Figure 8). Our average consumption is 190mA, which is slightly above the classic GPS behavior at 180mA. Overall, this is only 5.5% more and the cause of this result is that we are still requesting for real GPS fix too often. This behavior is due to our reset constraints: if we are unable to find a cluster in a close range or if our prediction is too bad, above 30 meters away from real location, then we try to improve our model by searching for a GPS fix. This decision may not be bad, but only works when we have a good learning model, at the beginning it cannot be energy-efficient.

The average accuracy is about 47 meters (cf. Figure 9), which is quite good considering the GPS checking interval and variation due to environmental factors. This suggests that we maybe could achieve energy saving using this technique by forcing GPS fixes every 15 seconds or more and keep a prediction good accuracy.

Threats to Validity

Obviously, our model is not perfect and prediction may not be accurate due to multiple reasons.

First Learning conditions may vary in a huge way due to environmental factors. One of the major cause is the weather: if the sky is clear, we can expect excellent location accuracy, more or less 5 meters. On the other hand, if the sky is cloudy, accuracy will drop significantly to 15 meters or more. This is due to difficulties to acquire GPS satellites, variation in the signal quality and lag;

Second Even if we are trying to reduce its effect, traffic can create time variations between clusters, which is crucial for our prediction. For example, 10km/h variation is equal to 2.77m/s. Thus, if the prediction occurs every 11 seconds (which is the default time we use to avoid energy tail state and hope for energy saving), it could be 30m wrong from the actual location. At the moment we are unable to estimate speed based on accelerometer due to its instability, so we cannot adjust our prediction based on deceleration or acceleration;

Third Due to the lack of data on some trips, our prediction model may not be able to compute user location. If the closest cluster is too far away from the current location our custom location provider will request for GPS fix to correct its lack of knowledge and doing so it will increase its global energy consumption and return a bad prediction.

Conclusion

In this paper, we propose a new Location Provider API based on Android Location API able to estimate user's location within 50 meters of accuracy and able to save energy based on daily learning. Compared to previous researches on smartphone energy-saving our API can be used in any existent applications using Dexpler to modify Dalvik bytecode and generate optimized APKs. We believe that real-time location approximations based on daily trips can still be improved using smartphones sensors. For instance, with better usage of the accelerometer it may be possible to compute the user speed. With this information, it should be possible estimate user location accurately using time and speed.

In the same spirit, compass do not require a lot of energy and can be use to detect orientation decision. Knowing that and the speed, it should theoretically be possible to compute user location with basic maths and only one real GPS fix.

Finally, sharing current location between different smartphones close to each others using Bluetooth Low Energy [START_REF] Bluetooth | [END_REF] should be an interesting way to share or compute user location at low energy cost knowing that the peak current consumption is only less than 15mA. Sharing the information with peers should be more efficient than computing the same location many times.

Figure 1 :

 1 Figure 1: Galaxy S3 plugged to the ammeter and running.

Figure 2 :

 2 Figure 2: GPS tail state visualization in mA during a GPS fix from Android API captured with Yocto-ammeter.

Figure 3 : 1 :

 31 Figure 3: Clusters on a Google Map. Colors indicate the direction where the user is going.

Figure 4 :Figure 5 :

 45 Figure 4: Relations between clusters in our SQLite Dabase

Figure 6 :

 6 Figure6: Variation of the acceleration during time. At first, the car is moving then it stops and finally there is no movement, the car is static. The red line is our limit at 0.25

Figure 7 :

 7 Figure 7: Consumption comparison between a classic GPS and our GPS during the learning phase with smooth bezier drawing

Figure 8 :

 8 Figure 8: Consumption evolution during prediction phase compared to default GPS behavior

Figure 9 :

 9 Figure 9: Evolution of accuracy during prediction phase

RR n°8586


Ying-Hen Hsieh
email: hsieh@mail.cmu.edu.tw

Hector De Arazoza

Rachid Lounes

Temporal Trends and Regional Variability of 2001-2002 Multi-wave DENV-3 Epidemic in Havana City: Did Hurricane Michelle Contribute to its Severity?

Keywords: Dengue, Cuba, DENV-3, mathematical model, basic reproduction number, turning point, climate, Hurricane Michelle

Objectives

To investigate the temporal and regional variability of the 2001-2002 d engue outbreak in Havana City, when 12,889 cases, mostly of DENV-3 type, were reported over a period of 7 months.

Methods

A simple mathematical model, the Richards model, is utilized to fit the weekly reported dengue case data by municipality, in order to quantify the transmissibility and temporal changes of the epidemic in each municipality via the basic reproduction number R 0 .

Results

Model fits indicate either a 2-wave or 3-wave outbreak in all municipalities. Estimates for R 0 vary greatly, from 1.97 (95% CI: 1.94, 2.01), for Arroyo Naranjo to 61.06 (60.44, 61.68) for Boyeros.

Conclusions

Wide regional variability in our estimates of R 0 for dengue is consistent with studies from various regions of the world, most likely due to heterogeneity in community structure, geographical locations, and social networking. The epidemic had initially started to ease around late October (e-week 43). However, infections spread once again after e-week 45, perhaps due to Hurricane Michelle, one of the most destructive and wettest tropical cyclones ever in Cuba that may have contributed to a protracted and more severe epidemic. For all municipalities with 3 waves, model fit indicates a new third wave occurred after Christmas/New Year, mostly likely attributable to a decrease in reporting during the holidays. Our results illustrate the potential impact of climatological events on disease spread, further highlighting the need to be wellprepared for potentially worsening disease spread in the aftermath of natural disasters such as hurricanes/typhoons.

Introduction

Dengue virus infection in humans causes a spectrum of illness ranging from asymptomatic or mild febrile illness to severe and fatal hemorrhagic disease, namely Dengue fever (DF) and dengue hemorrhagic fever/dengue shock syndrome (DHF/DSS), for which secondary infection is considered the main individual risk factor [START_REF] Nimmannitya | Dengue haemorrhagic fever with unusual manifestations[END_REF]; [START_REF] Halstead | Pathogenesis of dengue: challenges to molecular biology[END_REF][START_REF] Guzman | Dengue: an update[END_REF]. Infection with any of the four known serotypes of Dengue (DENV-1, DENV-2, DENV-3, and DENV-4) causes a similar clinical presentation that may vary in severity, depending on the strain and serotype of the infecting virus and the immune status, age, and genetic background of the human host, and induces life-long protective immunity to the infecting serotype, accompanied by short-term cross-protective immunity against the other viruses [START_REF] Sabin | Research on dengue during World War II[END_REF]). Due to its wide spread and multiple serotypes, the disease, even in absence of fatal forms, produces significant economic and social costs in terms of absenteeism, immobilization, debilitation, medication, and death).

Environmental factors such as climate and geography have been known to affect the spatio-temporal patterns of dengue transmission (Johnson et al. 2009). For example, climatological factors affect the development, maturation, and survival of the vector Ae. aegypti (see, e.g., Jetten & Focks 1997 or [START_REF] Mourya | Effect of temperature stress on immature stages and susceptibility of Aedes aegypti mosquitoes to chikungunya virus[END_REF]) as well as its role in the human-vector dengue transmission cycle. In particualr, the extrinsic incubation period (or the time for infected female mosquitoes to become infectious after biting an infectious individual) has been shown to be influenced by ambient temperature [START_REF] Keating | An investigation into the cyclical incidence of dengue fever[END_REF][START_REF] Chowell | Climate-based descriptive model of dengue fever: the 2002 epidemic in Colima, Mexico[END_REF][START_REF] Chowell | The influence of geographic and climate factors on the timing of dengue epidemics in Perú, 1994-2008[END_REF]. Moreover, factors such as high spatial heterogeneity levels in vector or host density plays an important role in determining the risk of dengue outbreaks and the reproduction number. Interestingly, a recent study [START_REF] Chowell | The influence of geographic and climate factors on the timing of dengue epidemics in Perú, 1994-2008[END_REF] shows significant heterogeneity in seasonality and timing of dengue epidemics at the province level across Peru, suggesting that dengue is frequently imported into coastal regions through infective sparks from endemic regions of neighboring endemic countries, where propitious environmental conditions promote year-round mosquito breeding sites. This pattern was found to be associated with climatologic conditions as well as connectivity among geographic regions.

In Cuba, Dengue viruses are transmitted by the Aedes aegypti mosquito. The mosquito is characterized by its biting pattern, which consists of multiple blood meals during each egg-laying cycle, and its ability to grow in water reservoir during of its immature stages (i.e., egg, larva and pupa). These features make it an ideal vector for dengue virus transmission, especially in large urban areas where the human population density is high with abundant artificial containers in where the aquatic stages of Aedes aegypti flourish [START_REF] Hammond | Characterization of Aedes Aegypti (Diptera: Culicidae) Production Sites in Urban Nicaragua[END_REF]). Aedes aegypti is infected by sucking infected human blood, while humans are infected with dengue viruses when bitten by an infective mosquito. The global spread of dengue can be directly attributable to the proliferation and adaptation of mosquitoes.

Currently the only way to control and reduce dengue transmission is to implement alternative strategies such as: (i) reduction of vector populations in both the adult (by fumigation and/or by other chemical/biological treatments, e.g., [START_REF] Thomé | Optimal control of Aedes aegypti mosquitoes by the sterile insect technique and insecticide[END_REF]) and the immature stages (by eliminating breeding sites); (ii) early detection of infected humans to prevent the virus transmission to susceptible mosquitoes. In Cuba the rainy season (lasting 6 months from May to October) produces a proliferation of mosquito populations which includes Aedes aegypti. This persistent presence of Aedes aegypti, together with the increased international arrivals from dengue-endemic countries in recent years, has led to several outbreaks including a major 2001 outbreak in Havana City [START_REF] Pelaez | Dengue 3 epidemic, Havana[END_REF]).

In the Caribbean region the first major outbreaks of dengue fever (with a significant number of severe cases) occurred in Cuba in 1977 (with DENV-1) and in 1981 (with DENV-2). Both epidemics affected the entire country, producing more than 500,000 and 300,000 dengue cases, respectively. In 2000, a minor outbreak of dengue was detected in Havana City with 138 cases of DF, when DENV-3 and DENV-4 viruses were isolated. In 2001, dengue transmission was detected in Havana City where 12,889 cases, mostly of DENV-3 type, were reported with 78 DHF cases and 3 deaths due to Dengue [START_REF] Pelaez | Dengue 3 epidemic, Havana[END_REF]).

To ascertain how did this epidemic come to pass, we will employ a simple mathematical model, the Richards model, to investigate the temporal progression of the epidemic in various municipalities in Havana City and to quantify the transmissibility of the epidemic via the basic reproduction number R 0 .

Methods and Materials

Data

The 2001-2002 dengue outbreak case data by reporting week (epidemiological week or e-week) for each of the 15 municipalities in Havana City are obtained from the Pedro Koury Tropical Medicine Institute (IPK) in Havana, Cuba, which spans from May 30, 2001 when the first case was reported in Playa (Figure 1), to the last reported case on

The Richards model

We fit the data to the Richards model [START_REF] Richards | A flexible growth function for empirical use[END_REF]): where C(t) is the cumulative number of cases reported at time t (in weeks). Here the prime "" denotes the rate of change with respect to time. The model parameter K is the maximum case number (or final outbreak size) over a single phase of outbreak, r is the per capita growth rate of the infected population, and a is the exponent of deviation. The solution of the Richards model is , where t i is the turning point of the epidemic (or the inflection point of the cumulative case curve), , and ln denotes the natural logarithm. Using the Richard model, we can directly fit empirical data from a cumulative epidemic curve to obtain estimates of epidemiological meaningful parameters, including the growth rate r.

In such a model formulation, the basic reproduction number R 0 is given by the formula , where T is the disease generation time defined as the average time interval from infection of an individual to infection of his or her contacts. To take into account of the extrinsic and intrinsic incubation periods as well as the duration of viremia, we use an estimated generation time of T=24 days with a range of 16-34 days (see Hsieh & Chen 2009, for detailed explanation). Expression of dengue reproduction number that more explicitly connect the intrinsic growth rate and the epidemiology of host/vector can also be found in, e.g., [START_REF] Favier | Early determination of the reproductive number for vector-borne diseases: the case of dengue in Brazil[END_REF] The turning point or inflection point t i of the cumulative case data, defined as the time when the rate of case accumulation changes from increasing to decreasing (or vice versa) can be easily pinpointed as the point where the rate of change transitions from positive to negative; i.e., the moment at which the trajectory begins to decline. For epidemics with two or more phases, a variation of the S-shaped Richards model has been proposed [START_REF] Hsieh | Real-time forecast of multi-wave epidemic outbreaks[END_REF]. This multi-staged Richards model distinguishes between two types of turning points: the initial S-Shaped cumulative case curve which signifies the first turning point that ends initial exponential growth, or simply the time where peak incidence of a wave of cases occurs; and a second type of turning point in the cumulative epidemic curve where the growth rate of the number of cumulative cases begins to increase again, signifying the beginning of the next wave. This variant of Richards model provides a systematic method of determining whether an outbreak is single-or multi-phase in nature, and can be used to distinguish true turning points from peaks and valleys resulting from random variability in case counts. Readers are also referred to (

Results

The results of the best Richards model fit for 14 of 15 municipalities in Havana City, with estimates for t i , r, K, R 0 and their respective 95% confidence intervals (CI), are listed in Table 1. The Akaike Information Criterion (AIC, Akaike 2974)) values are also given as a measure of the respective goodness of fit for each wave of the local outbreak. The municipality of Cotorro had only 34 cases reported very late after e-week 40 (in September) and scattered over the next 20 weeks, and hence cannot be fitted to the Richards model. We also fitted the combined total case data of all 15 municipalities in Havana City including Cotorro (see Table 1b), for the purpose of comparison. Note that the week in which the true turning point for each wave occurred is t i weeks (3 rd column in the tables) after the first week of the wave, rounding off to the next integer week. For example, the turning points for the three waves in Playa occurred in e-week 39 (24+14.3 in the first row of Table 1a), e-week 46 (43+2.46), and e-week 4 of 2002 (51+4.38), respectively.

The model fits for the most severely affected municipalities, namely Playa (with the first reported case of this epidemic), Plaza, Central Havana, and Old Havana, as well as for all 15 municipalities of Havana City, are given in Figure 2.

All model fits indicate a 2-wave or 3-wave outbreak for each of the 14 municipalities as well as for all of Havana City. Outbreaks in Old Havana, Regla, and Guanabacoa only are 2-wave, while all other municipalities exhibit 3-wave outbreaks.

For the purpose of comparing regional heterogeneity, we also provide timeline graphs of the 14 fitted municipalities in Figure 3. The model fits for the most severely affected municipalities, namely Playa (with the first reported case of this epidemic), Plaza, Central Havana, and Old Havana, as well as for all 15 municipalities of Havana City, are given in Figure 2.

All model fits indicate a 2-wave or 3-wave outbreak for each of the 14 municipalities as well as for all of Havana City. Outbreaks in Old Havana, Regla, and Guanabacoa only are 2-wave, while all other municipalities exhibit 3-wave outbreaks. For the purpose of comparing regional heterogeneity, we also provide timeline graphs of the 14 fitted municipalities in Figure 3.

Conclusions and Discussion

Previous provincial data were able to show a hierarchy of transmission events from large to small population areas during the large 2000-2001 dengue epidemic in Peru. We also note that although variability across municipalities is probably well represented in estimates for R 0 , the accuracy of the actual R 0 levels may be affected by the lack of explicit dependence of climatic conditions on dengue transmission dynamics in our modeling.

Moreover, there is also a clear regional heterogeneity in the temporal trend, where Old Havana, Regla, and Guanabacoa are 2-waved while the other municipalities are 3waved. In particular, Central Havana, which is closely nearby to Old Havana but has a different model fit with one additional (third) wave occurring at the turn of the calendar year (e-week 52).

Except for Playa (which had the earliest outbreak and a turning point as early as e-week 39), Regla, and Guanabacoa (regions, along with Cotorro, having late outbreaks starting as late as in September), all other municipalities as well as all of Havana City

2007

) have proposed that extreme weather conditions, such as typhoon or hurricane which brings substantial amount of precipitation, can be shown to significantly correlated to the occurrence of a wave of reported dengue cases with a lag of several weeks. Typically, a typhoon/hurricane first brings a sudden drop in temperature causing mosquito inactivity and decreased biting/infection, the ensuing heavy rainfall then leads to increased breading reservoir for the larvae to proliferate. It is hence conceivable that Hurricane Michelle had contributed to, if not actually causing, the new wave of cases in these municipalities after e-week 45. In other words, the dengue epidemic in Havana had started to ease initially around e-weeks 41-43 (first turning point/peak incidence), but spread once again after e-week 45 after Hurricane Michelle, causing a more severe and longer-lasting epidemic. We note that, since the case data is by reporting week, there is a delay of around one week from actual infections to reporting, mainly due to intrinsic dengue incubation period of 4-7 days (see, e.g., [START_REF] Nishiura | Natural history of dengue virus (DENV)-1 and DENV-4 infections: Reanalysis of classical studies[END_REF].

Our result illustrates the potential impact of climatological events on disease spread. It further highlights the need for health community to be aware and better prepared, with programs such as preemptive spraying and elimination of reservoirs for larvae, for a potentially worsening disease spread in the aftermath of natural disasters such as hurricanes and typhoons. Community-wide cross pre-immunity from earlier epidemics may also have played a role, which is however difficult to gauge without sizable serologic dataset.

A Further limitation of this work is that the Richards model does not explicit incorporate vector biology or the effect of temperature on extrinsic incubation period or vector mortality rate, which could impact our results. However, to appropriately consider these biological aspects of the dengue transmission requires a much more complex vector-host model that incorporates environmental factors such as seasonality as well as detailed and high quality vector and human data, and therefore is beyond the scope of the current work.

February 27 ,

 27 2002. Subsequently the data spans 40 weeks, from e-week 22 of 2001 (May 27 to June 2, 2001) to e-week 9 of 2002 (February 24-March 2, 2002). During the 2001-2002 Dengue III epidemic in Havana all suspected Dengue fever cases were tested. Dengue infection was confirmed in 17.86% of them through serological studies. The initial test was carried out at the Provincial Epidemiological Center of Havana using the ultramicro-enzyme-linked inmunosorbent assay (ELISA) for Dengue IgM detection. A second seoconversion test is carried out 2-3 weeks after illness onset to confirm seroconversion, at the National Reference senter for Dengue in Cuba at the Tropical Medicine Institute "Pedro Kouri". For more information see Pelaez et al. (2004).

  It has been shown mathematically that, given the growth rate r, the equation provides the upper bound of the basic reproduction number regardless of the distribution of the generation interval used (Wallinga & Lipsitch 2007). Additional technical details regarding the Richards model can be found in (Hsieh et al. (2004); Hsieh & Cheng (2006); or Hsieh (2008).

  Hsieh & Ma (2009); and Hsieh & Chen (2009) for its applications to dengue, to (Hsieh (2010; Hsieh et al. (2010),; Hsieh et al. (2011a),; Hsieh et al. (2011b) for applications to 2009 H1N1, and to (Wang et al., (2012in press) for the connection between the Richards model and the traditional SIR compartmental model. Model parameter estimates based on the explicit solution of the Richards model can be obtained easily and efficiently using any standard software with a least-squares approximation tool, such as SAS or Matlab.

  large dengue epidemics in Cuba were associated with DENV-1 (in 1977) and DENV-2 (in 1981). More recently, two smaller dengue outbreaks were reported in 1997 (DENV-2) and September 2000 (DENV-3 and DENV-4) (Pelaez et al. 2004). Subsequently, there was very little pre-existing immunity among the population in Havana for this 2001-2002 DENV-3 epidemic, although some cases of DHF/DSS might have occurred in persons infected with DENV-3 in a background of immunity to DENV-1 and DENV-2 from either the 1981 epidemic or dengue epidemics during or before the 1940s (Alvarez et al. 2006).

  had a first turning point (peak incidence) during e-weeks 41-43 that indicates a downturn of cases, regardless of whether the data fit exhibits a 2-wave or 3-wave outbreak. Moreover, with the exception of Arroyo Naranjo, Regla, and Guanabacoa, all other municipalities as well as all of Havana City has a turning point of second type around e-weeks 43-45 that signals an increase in case number and the beginning of a new wave of cases, regardless whether it has a 2-wave or 3-wave outbreak. Interestingly, all 14 municipalities had a turning point (peak incidence) during e-weeks 46-51, of the number of waves or the timing of initial outbreak. Finally, for all 11 municipalities with 3 waves, the third wave started around e-week 51 to e-week 1 of 2002. The last turning point (peak incidence), or a downturn toward the end of the outbreak, came during e-weeks 2-5 of 2002 for all municipalities except Old Havana which had a 2-wave outbreak. Even within Havana city, geographical heterogeneity played a significant role in temporal trends as well as transmission potential of the infected individuals. Playa, Plaza and Central/Old Havana have more work offices where people commute to daily, and hence are important in driving the epidemic (Figure 3a), while the municipalities of Guanabacoa, Regla and Cotorro all contain some less populated areas and therefore have very late and very minor outbreaks. More significantly and intriguing is the underlying cause of this multi-wave epidemic. For the first wave, we note that Hurricane Michelle, the most destructive hurricane in the history of Cuba based on its actual damage (Pielke et al. 2003), struck Cuba on November 4, 2001, the first day of week 24 in our study. Landing on the coast of western and southern Cuba, Michelle was one of the wettest tropical cyclones ever in Cuba, produced 4-5 foot waves along with a heavy storm surge. Rainfall amounting up to 754 mm was recorded across the island ( Instituto Nacional de Recursos Hidráulicos 2003 ) . Previous studies (e.g., Hsieh & Chen 2009; Chowell & Sanchez 2006; Wu et al.

  After a downturn (peak incidence) around e-weeks 46-51, a new turning point (of second type) for start of a third wave occurred after e-weeks 51 for all 11 municipalities with 3-wave fit (not including Old Havana, Regla, and Guanabacoa). However, we note that Christmas and New Year happened to fall on, respectively, e-weeks 52 and e-week 1 of 2002. The new wave of reported cases is likely attributable to a decrease in reporting partly due to reluctance on the part of some ill persons for hospital visits during the holidays, and a subsequent surge in cases after the holidays. Hence the cause for this third wave is human behavior, rather than anything relating to disease transmission or other environmental or social factors. During the 1981 dengue epidemic, the Cuban health authorities started a National Program for Eradication of Aedes aegypti which has continued to the present. The campaign was based on the principles of dengue control established by the Pan American Health Organization (PAHO) Guidelines (Pan American Health Organization 1994) with the involvement of the whole community (governmental and political bodies at all levels, householders, community organizations, etc.), where thousands of workers were mobilized with the task of periodic inspection of housing, detection and elimination of breeding points for the vector, chemical control of mosquitoes, and an educational campaign. These activities were carried out regularly and reinforced whenever cases of dengue are detected, as workers clean up empty places looking for larvae and spray all homes within 500-meter radius of the suspected case. The 2001 epidemic was no exception (Pelaez et al. 2004), which may have contributed to the 2001 epidemic being less severe than the previous epidemics in 1977 and 1981.

Figure 1 .

 1 Figure 1. Geographical map of 15 municipalities of Havana City with colors signifying

Figure 2 .

 2 Figure 2. Richards model fit for 2001 dengue outbreak in (2a) Playa, (2b) Plaza, (2c)

Figure 3 .

 3 Figure 3. Timelines for 2001 dengue outbreak (with turning points in green numerals)

Table 1a .

 1a Estimated multi-wave Richards model parameters values with 95% confidence intervals (in parenthesis) for Playa, Plaza, Central Havana, Old Havana, Diez de Octubre, Cerro, Marianao, and Lisa. Note that the week in which the true turning point for each wave occurred is t i weeks after the first week of the wave. Akaike Information Criterion (AIC) values are given for the respective goodness of fit.

	Octubre	45 -52	61.5	4.6 (4.1, 5.1)	0.157 (0.133, 0.180)	848 (806, 891)	1.71 (1.69, 1.73)
		52 -7 *	29.9	2.8 (0.9, 4.7)	0.636 (0.228, 1.043)	119 (109, 129)	8.84 (8.56, 9.13)
	Cerro	38 -43	5.4	4.1 (4.0, 4.1)	0.483 (0.470, 0.496)	375 (369, 381)	5.23 (5.22, 5.24)
		43 -51	75.1	2.6 (0.3, 4.9)	0.195 (0.056, 0.335)	850 (763, 937)	1.95 (1.85, 2.06)
		51 -8 *	78.1	5.9 (4.7, 7.1)	0.030 (0.023, 0.037)	1009 (985, 1033)	1.11 (1.10, 1.11)
	Mariana	29 -44	86.1	12.7 (12.6, 12.8)	0.581 (0.556, 0.605	627 (618, 636)	7.32 (7.30, 7.34)
	Region o	E-week	AIC	Turning point ti	Growth rate r	Case number K	R0
		44 -1*	61.3	1.7 (0.5, 2.9)	0.122 (0.088, 0.156) 1062 (1037, 1088)	1.52 (1.49, 1.55)
	Playa	24 -43	155.7	14.3 (14.0, 14.6) 0.393 (0.364, 0.422) 1601 (1564, 1638)	3.85 (3.82, 3.87)
		1 * -6 *	33.0	3.4 (2.4, 4.4)	0.028 (0.018, 0.038) 1147 (1130, 1165)	1.10 (1.10, 1.11)
		43 -51	52.7	2.5 (1.3, 3.7)	0.019 (0.014, 0.023) 1764 (1706, 1822)	1.07 (1.06, 1.07)
	Lisa	34 -44	39.8	7.7 (7.5, 7.9)	0.760 (0.674, 0.847)	249 (241, 257)	13.56 (13.48, 13.63)
		51 -6 *	71.8	4.4 (4.1, 4.6)	0.274 (0.247, 0.301)	79 (76, 81)	2.56 (2.54, 2.56)
		44 -1 *	58.1	4.4 (3.6, 5.2)	0.081 (0.066, 0.097)	426 (406, 445)	1.32 (1.31, 1.33)
	Plaza	33 -44	97.9	7.7 (7.2, 8.3)	0.660 (0.145, 1.175)	845 (627, 1064)	9.61 (9.17, 10.05)
		1 * -7 *	26.6	1.9 (1.1, 2.8)	0.034 (0.023, 0.044)	478 (472, 483)	1.12 (1.12, 1.13)
		44 -1 *	61.7	3.8 (3.2, 4.3)	0.106 (0.092, 0.119) 1389 (1336, 1442)	1.44 (1.43, 1.45)
		1 * -8 *	44.6	1.8 (1.1, 2.5)	0.025 (0.018, 0.031) 1468 (1459, 1476)	1.09 (1.08, 1.09)
	Central	32 -44	99.2	9.8 (8.9, 10.7)	0.747 (0.475, 1.018)	613 (534, 693)	12.94 (12.70, 13.17)
	Havana
		44 -52	65.7	3.6 (2.3, 5.0	0.137 (0.093, 0.182) 1366 (1184, 1549)	1.60 (1.31, 1.90)
		52 -7 *	50.1	2.6 (2.0, 3.2)	0.044 (0.037, 0.052) 1564 (1543, 1585)	1.16 (0.87, 1.46)
	Old	33 -44	64.6	8.4 (7.5, 9.2)	0.655 (0.434, 0.876)	167 (151, 184)	9.44 (9.25, 9.63)
	Havana
		44 -9 *	87.5	11.7 (11.3, 12.0) 0.122 (0.114, 0.129)	817 (800, 834)	1.52 (1.51, 1.53)
	Diez de	32 -45	103.2	9.90 (9.5, 10.3)	0.327 (0.285, 0.370)	362 (345, 379)	3.07 (3.03, 3.11)

* denotes e-week in 2002.

Table 1b .

 1b Estimated multi-wave Richards model parameters values with 95% confidence intervals (in parenthesis) for Boyeros, Regla, Habana de Este, Guanabacoa, Arroyo Naranjo, and SMP. Note that the week in which the true turning point for each wave occurred is ti weeks after the first week of the wave. Akaike Information Criterion (AIC) values are given for the respective goodness of fit.

	Region Boyeros Havana City Total	E-week AIC Turning point ti 35 -44 53.2 6.7 (5.1, 8.4) 4.1 (3.5, 4.7) 24 -44 216.0 17.5 (17.4, 17.7) 0.316 (0.305, 0.327) Growth rate r 1.199 (0.428, 1.971) 0.082 (0.068, 0.097) 44 -52 94.2 4.5 (4.1, 4.8) 0.095 (0.087, 0.103) 339 (290, 387) 59.8 Case number K 616 (603, 629) 5933 (5771, 6095) 11003 (10578, 11418) 52 -9 * 101.2 3.0 (2.7, 3.4) 0.037 (0.034, 0.041) 12879 (12820, 12938)	R0 1.33 (1.32, 1.34) 2.96 (2.95, 2.97) 1.39 (1.38, 1.39) 1.14 (1.13, 1.14)
	* denotes e-week in 2002.

  Table 1 indicate that the estimates for R 0 in the initial wave vary from 1.97 (95% CI: 1.94, 2.01) for Arroyo Naranjo to 17.88 (17.35, 18.41) for Havana del. Este. Only Boyeros had an unusually high disease transmission

	potential of R 0 =61.06 (60.44, 61.68) in the initial wave starting comparatively late in e-
	week 35 (Figure 3b), possibly caused by multiple imported cases from neighboring
	municipalities with earlier outbreaks which has been shown to be able to impact the
	estimation of basic reproduction number (Nishiura & Roberts 2010). The wide regional
	variability exhibited in our estimates is consistent with other reported values of R 0 for
	dengue in literature in various regions of the world using high resolution spatial and

temporal data (see, e.g.,

Favier et

al. 2006; Luz et al. 2003; Favier et al. 2006; Chowell et al. 2007; Chowell et al. 2008, or Table 2 in Hsieh & Chen 2009), most likely due to heterogeneity in community structure, geographical locations, interevention measures, as well as in social networking. Interestingly, a study (Chowell et al. 2008) using

Acknowledgments

YHH is supported by the National Science Council of Taiwan (grants 100-2314-B-039 -028-MY3 and 100-2115-M-039-002). This work was carried out during visits to the University of Paris Descartes by YHH and H de A. The authors received support from the French "Agence National pour la Recherche" project "Viroscopy". H de A also received support from the Spanish AECID, from their project PCI D/023835/09. We would like to thank Dr. Gustavo Kouri, who passed away recently, and his colleagues at the Pedro Kouri Tropical Medicine Institute for access to the dengue surveillance data used for this study. The authors also would like to thank the reviewers for constructive comments which greatly improved this paper.


Raphael Belmin

Chengole Mulindo

Martin Welimo

Geoffrey Kamau

Bernard Triomphe

Building on illegal value chains to achieve sustainable management of natural resources? The case of indigenous aloe

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

In developing countries, inclusive approaches to improve access to natural resources are perceived as a way for public action to break the 'downward spiral' of rural poverty and environmental degradation [START_REF] Scherr | A downward spiral? Research evidence on the relationship between poverty and natural resource degradation[END_REF]. But livelihood strategies sometimes rely on illegal exploitation and trade of natural resource through non-official supply chains. Such configurations often lead to environmental threat that governments fail to mitigate [START_REF] Ascher | Why Governments Waste Natural Resources: Policy Failures in Developing Countries[END_REF]. Therefore a challenge for public action is to jointly address poverty and environment degradation by intervening at the supply chain level. This paper explores the hypothesis that chances of success of public interventions with approaches to sustainable management of natural resources increase when external interventions build on preexisting illegal supply chains. We illustrate this idea by reporting how official and non-official stakeholder networks have been interacting with each other in Baringo (Kenya), within a six years period. During that time, a public Research & Development (R&D) intervention actively promoted sustainable production and export of aloe-based products as an alternative to illegal Wild Aloe Exploitation (WAE). Trade of all aloe species (except for Aloe vera) is regulated by the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES).

METHODS

A qualitative and participatory assessment was conducted in Baringo between April and November 2012, with the purpose of analyzing the multistakeholder process in which aloe cultivation and certified trade were introduced as 2 innovations to replace illegal WAE. We used an analytical framework and guidelines derived from the Innovation System (IS) theoretical framework [START_REF] Lundvall | Introduction[END_REF]. The framework paid particular attention to how this innovation process unfolded over a 30 year period, and focused among others on stakeholders and their roles, the types of innovations developed, the triggers and drivers, the role of markets and projects. This study was conducted in the framework of the JOLISAA project [START_REF] Triomphe | What does an inventory of recent innovation experiences tell us about agricultural innovation in Africa?[END_REF], by a team comprising of researchers, students, and community representatives. Data were collected through individual interviews, group discussions, workshops as well as from published and unpublished documents.

RESULTS

Baringo County lies in the Rift Valley Province of Kenya. South-North geographical disparities lead to differences among farmers in terms of poverty, market access, and livelihood systems. WAE has been occurring without control from the 1980s in the drought-prone and poverty-stricken pastoral areas of northern Baringo. Demand for aloe sap created by traders supplying the growing international market for aloe sap derivates triggered WAE in the early 1980s. Traders put in place logistical arrangement (decentralized processing units, storage facilities, transportation), and set up networks of aloe sap buyers/processors spread across the territory. They also played the role of knowledge brokers by developing a series of innovations in aloe harvesting and trade and by training local farmers on harvesting methods, sap quality tests and sap processing. In doing so, traders became the central node of a vibrant supply chain linking northern Baringo households to the global market (Fig. 1). In northern Baringo, the illegal aloe value chain offered marginalized stakeholders a fragile livelihood source, as 2 traders offered low sap buying prices. But in their dire context aloe represented a diversification option and a drought aversion strategy well fitted with the social organization of pastoral households.

In 1986, uncontrolled commercial harvest was accused of destroying aloe populations and WAE was declared illegal through a presidential decree. Since this decree was not translated into law, nothing hampered the illegal supply chain with the exception of occasional custom seizures. In 2004, growing attention on the issues raised by unchecked WAE prompted the Kenyan government to try and establish a legal and administrative framework to enable the country to export aloe derivates in accordance with CITES, with Baringo selected as a pilot area for aloe certified exploitation and trade.

From 2004 to 2009, public R&D interventions took place in Baringo to promote aloe cultivation and to build up a certified, sustainable aloe supply chain (Fig. 1). Most activities focused on the more accessible agro pastoral areas of southern Baringo, an area in which WAE had never occurred. In contrast to Northern Baringo, opportunity cost of harvesting aloe was higher because smallholders benefited from a relative diversity of livelihood sources and good market access. Smallholders received trainings and aloe seedlings, a sap-processing factory was built in partnership between a private trader and the communities, a farmer organization was empowered and Aloe Management Units (AMUs) were established. AMUs were delineated areas from where registered smallholders harvested and sold aloe sap to certified collection points following production rules and quotas. Despite this flurry of interventions, several challenges reinforced each other and contributed to the quick paralysis of the newly-emerging certified supply chain: low interest by southern Baringo farmers in aloe cultivation and sap selling, conflicts between project partners leading to market uncertainties and to limited development of certified trade.

Meanwhile, in northern Baringo unintended interactions between official and non-official networks had positive consequences, despite limited external interventions. Aloe cultivation met with a favorable interest from local farmers, and smallholders started supplying AMUs collection points before certified trade stagnated. Farmers perceived aloe cultivation as a strategy for reducing the risks linked to a fragile dependency on spontaneous aloe. Moreover, uncertainties in certified commercial outlet were reduced by relying on -or reverting to -the historically much more stable and effective non-official supply chain. Lastly, adoption of aloe cultivation was made easy by the anchorage of aloe-related activities in pastoral households' social organization, as well as by the knowledge accumulated on aloe harvest. On the negative side, the official/non-official networks interaction also led to price competition between the legal and illegal channels. This encouraged smallholders to harvest more spontaneous aloe than usual, thus increasing the pressure on the resource (Fig. 1).

DISCUSSION

In northern Baringo, temporary success of public interventions was the consequence of unplanned hybridization between official and non-official supply chain. On the other hand, by ignoring prior existing practices and networks, projects were confronted by a double challenge: low interest from smallholders in the south, and competition with illegal supply chain leading to accelerated natural resource depletion in the north. The Baringo aloe case suggests that wild harvesters and illegal value chains' stakeholders are fully part of natural resource management systems, and that intervening agencies should explore ways of recognizing and incorporating their experience to allow them to evolve towards certified trade (in this view, innovation system theory provides relevant analytical framework). Doing so appears to have a greater potential to achieve sustainable management of natural resources and poverty reduction.

Figure 1 .

 1 Figure 1. Interactions among illegal and public networks for aloe exploitation and trade.

R. Belmin is from the Research Laboratory for Livestock Development, National Institute for Agricultural Research, Corte, France (belmin@corse.inra.fr).J.C. Mulindo is from the Perkerra centre of Kenya Agricultural Research Institute, Marigat, Kenya (cjmulindo@gmail.com).M. Welimo is from the Perkerra centre of Kenya Forest Research Institute, Marigat, Kenya (mwelimo@yahoo.com).G. Kamau is from headquarters of Kenya Agricultural Research Institute, Nairobi, Kenya (gmkamau_1@yahoo.com).B. Triomphe is from the joint Research Unit Innovation, Agricultural Research Centre for International Development, Montpellier, France (bernard.triomphe@cirad.fr).

ACKNOWLEDGEMENT

This work was carried out as part of the EU-funded FP7 project Joint Learning in and about Innovation Systems in African Agriculture (JOLISAA, www.jolisaa.net).