Aina Astudillo Fernandez

Anne-Catherine Mailleux

Fabrice Saffre

Bertrand Krafft

Jean Louis Deneubourg

Aina Astudillo Fernandez

Effect of retentive markers on the dynamics of settlement: The case of arthropod silk

Keywords: Population dynamics, Colonisation, Agregation, Anelosimus eximius, social spiders

During a settlement decision, the presence of conspecifics is crucial to species subject to Allee effects, for which the number of founders affects the subsequent growth of the colony. Marking the area (physically or chemically) conveys information about the number of conspecifics present in a new patch. Here, we study how the individual affinity for the marker affects the dynamics of a foundation process. A generic population model is presented, in which marking and affinity for the marker are at stake. Our results

show that population size thresholds can appear, below which settlement is not possible. This model is then used to study the dynamics of migration and aggregation in a set of interconnected populations. We show that affinity for the marker can induce asymmetries in the population distribution.

Anelosimus eximius is a social spider subject to Allee effects, for which silk

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Effect of Retentive Markers on the Dynamics of Settlement: The Case of Arthropod Silk Introduction A key stage of population dynamics is the settlement of individuals in new environments. Settlement results from complex decision-making processes regarding habitat selection. Consequently, its dynamics can greatly vary depending on the different habitat selection strategies (Mihoub et al., 2009). Among the biotic factors which play a role in habitat selection are the conspecifics. In many species, specially in group-living species, the presence of conspecifics increases the probability of reaching a new patch (conspecific attraction) or the probability of staying in a new patch (conspecific retention). Either way, the probability to settle in a patch increases with the number of conspecifics. By definition, this type of selection strategy entails positive feedback loops (DeAngelis, 1986;Ray and Gilpin, 1991), which induces non-linearities in the dynamics of settlement (Fletcher, 2006;Smith and Peacock, 1990). The complex and often non-intuitive behaviour of this kind of system is worthy of interest.

Conspecific attraction (or retention) is found in various taxonomic groups

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throughout the animal kingdom (Mueller et al., 1997;Stamps, 1988). The most frequently mentioned benefits of settling near conspecifics include reduced search and settlement costs (Fletcher, 2006), the use of conspecifics as indicators of habitat quality (Doligez et al., 2003), and the fitness increase associated with Allee effects (Courchamp et al., 2008). While these ultimate causes have received great attention, relatively little research has focused on the mechanisms used to evaluate the presence and/or the number of conspecifics.

The mechanisms used to assess the number of conspecifics in a given patch depend to a great extent on the type of information available to the animal. When the sensory system does not allow access to global information (e.g. Long distance view of big mammals, auditory information for birds (see references in Forbes and Kaiser, 1994)), the evaluation of the number of conspecifics has to be based on local information. This is usually the case for arthropods. Some use indirect cues that are representative of the number of conspecifics. These cues are the result of environment modification or marker production (chemical or physical) by the conspecifics in a density-dependent manner (Depickère et al., 2004;Rivault et al., 1998).

To our knowledge, population models of conspecific attraction (Mihoub et al., 2009;Ray andGilpin, 1991, Smith andPeacock, 1990) or retention [START_REF] Jeanson | Conspecific attraction and shelter selection in gregarious insects[END_REF] have always assumed direct attraction between conspecifics. Therefore, the consequences of conspecific attraction when it is mediated by a marker remain unexplored. Yet, one can expect the resulting population dynamics to be different, since the marker accumulates in the patch, which potentially causes delays and reinforces feedback loops. Here,

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we study the effects of this habitat selection strategy on the dynamics of settlement.

We focus on the case of Anelosimus eximius, a social spider found in South America. The spiders of this species live in large colonies ranging from a few hundred to a few thousand individuals [START_REF] Avilés | Colony Size and Individual Fitness in the Social Spider Anelosimus eximius[END_REF].

The benefits they obtain from living in groups are the result of cooperation in activities such as prey catching (Pasquet and Krafft, 1992), brood care [START_REF] Avilés | Sex-ratio bias and possible group selection in the social spider Anelosimus eximius[END_REF] and web construction (Christenson, 1984). Not surprisingly, they are submitted to multiple Allee effects: their survival and fecundity rates increase with colony size [START_REF] Avilés | Colony Size and Individual Fitness in the Social Spider Anelosimus eximius[END_REF] up to a certain extent. Although colonies are rather stable and may persist for several years in one site (Vollrath 1986), there are conditions that trigger dispersal of some individuals that then create new colonies. New foundations only take place at relatively large sizes of the mother colony [START_REF] Avilés | Colony Size and Individual Fitness in the Social Spider Anelosimus eximius[END_REF],

possibly when drought provokes a food shortage (Venticinque et al. 1993).

Other causes of dispersal may be linked to sudden changes in the environment (a fallen tree, for instance) that might degrade the local conditions or the web. Departure may also be totally accidental (passive transport by animals or humans). In any case, when new colonies need to be founded, it is crucial for individuals to settle in a group, and there is experimental evidence of a group size threshold below which they are not likely to settle [START_REF] Leborgne | Experimental study of foundation and development of Anelosimus eximius colonies in the tropical forest of French Guiana[END_REF].

One way to ensure that there are enough settlers to guarantee the fitness of the colony is to disperse in a group. Anelosimus eximus exhibits various patterns of dispersal: swarming or budding (Vollrath, 1982) are the

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most common [START_REF] Leborgne | Experimental study of foundation and development of Anelosimus eximius colonies in the tropical forest of French Guiana[END_REF]. Spiders may also leave the colony individually, however it has been shown that dragline attachment acts as a recruitment process (Jeanson et al., 2004a;Saffre et al.,1999), which makes it likely that isolated individuals end up aggregated with other spiders. Indeed, a group of spiders scattered in a bush tends to aggregate [START_REF] Leborgne | Experimental study of foundation and development of Anelosimus eximius colonies in the tropical forest of French Guiana[END_REF]. Although these strategies increase the potential number of founders on arrival, they offer no information about the actual number of founders.

Therefore, one could expect that spiders use additional mechanisms to assess group size in order to make an informed decision regarding settlement.

Our hypothesis is that silk, or some chemical component in it, acts as information about the number of settlers in a patch (Giraldeau,1997;Wagner and Danchin, 2003). The probability of settling in a given site would therefore increase with the amount of silk on that site. [START_REF] Bernard | L'attraction pour la soie , base de la cohésion du groupe et des comportements collectifs chez les araignées sociales[END_REF] used a T-maze to show that in Anelosimus eximius, spiders are attracted to conspecific silk. Furthermore, they showed that silk has a strong retention effect: spiders spent significantly more time on the branch containing silk. Affinity for silk has also been put forward in many other spider species [START_REF] Krafft | Contribution la biologie et l'éthologie d' Agelena consociata Denis (araigne sociale du Gabon) II[END_REF][START_REF] Leborgne | Influence of conspecific silk-structures on the choice of a web-site by the spider Zygielle x-notata (Clerck)[END_REF]Schuck-Paim and Jimenez Alonso, 2001).

We present a general model for the growth of a set of interconnected populations, where every individual produces and is retained by a marker (in this case, silk). First, we explore the results of the model for a single population.

We show that affinity for silk can give rise to thresholds in the number of founders, below which the population does not settle. Then, we study the general case of n populations, and demonstrate that a heterogeneous distri-

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bution of the population on equal sites can be stable due to the retentive effect of silk. Our model is validated by comparing theoretical predictions on the particular case of two interconnected populations with field experiments carried out in French Guiana on Anelosimus eximius. Finally, the importance of retentive markers is put forward with a comparison between our results and those of models in which retention is caused by the individuals directly.

General population model

The general theoretical setup consists of a system of n interconnected populations. We define two variables: N i is the number of individuals on the i th patch and S i the quantity of silk accumulated on that patch. The model is based on the following assumptions: (1) The individual tendency to settle in a patch i increases with the amount of silk S i accumulated in that patch.

To model this, we define the probability to leave the patch as a decreasing function of the quantity of silk. The function f (S i ) = β/(1 + (S i /α) ) can take several shapes, among which a threshold-type S-shape. Other typically used functions (Sumpter and Pratt, 2009) such as the sigmoid function give the same results. The maximal tendency to leave β is halved for the quantity of silk α, than can be seen as a "silk threshold". The shape of the function is determined by . Biologically, can be understood as the sensitivity of the response (tendency to leave) to changes in the amount of silk, for quantities of silk approaching α (see Fig. 1 for a graphical representation of f (S i )).

For very high values of , the sensitivity of the response is so high that the departure rate switches sharply from the maximum to zero when the quantity of silk equals α;

(2) When individuals leave a patch, they have a probability (3) The time-scale we focus on (a couple of days) is short enough to discount births and deaths, as it takes approximately a month after oviposition for spiderlings to emerge [START_REF] Avilés | Sex-ratio bias and possible group selection in the social spider Anelosimus eximius[END_REF]; (4) Silk is produced by every individual at a constant rate γ; (5) Silk degrades over time at a rate of θS. The system of Eq. 1 describes the population dynamics of the ith patch:

dN i /dt=-N i f (S i ) + (1 -P e)/(n -1) j =i N j f (S j ) dS i /dt=γN i -θS i (1)

Dynamics of one population

Before exploring the dynamics of the system, let us first consider the case of one population (n = 1). We assume that individuals have arrived using collective migration, thus there is a group of potential founders N 0 in a new patch. Individuals can settle or leave. When individuals leave, they exit the system and do not return. This particular case allows us to understand the dynamics of foundation when a group of founders arrives in a new patch, or more precisely, the effect of departure on this foundation process.

At this stage, it is convenient to scale the system of equations, that is to express it in non-dimensional terms (Eq. 2). Although this change of variables and parameters does not have a direct biological meaning, it reveals the links between parameters, and synthesises the information concerning their We redefine the variables as follows. The number of individuals N is replaced by the ratio between the number of individuals and the initial population size X = N/N 0 . The quantity of silk S is substituted by a new variable Z = βS/γN 0 that is directly proportional to the quantity of silk.

We also define two new parameters a and b, that are directly proportional to α (threshold quantity of silk) and θ (degradation coefficient of silk) respectively. Finally, we define a new time (τ = βt). The results obtained with these scaled variables and parameters can easily be converted back into the biologically meaningful variables (See Table 1 for a summary of all variables and parameters).

dX/dτ =-X/(1 + (Z/a) )

dZ/dτ = X -bZ (2)
This system has only one stable steady state (X = 0, Z = 0). However, the behaviour of the system is qualitatively different for ≤ 1 and > 1.

For analytical clarity, we will develop only the case of = 1 and = 2 as examples of the two subsets. Numerical resolutions show consistency in the results for other values of in both subsets.

For = 1, the behaviour of the system is intuitive: regardless of the For = 2 (or in fact for values of > 1), the non-linearity of the system makes its behaviour less intuitive. The system presents a particular behaviour if b = 0. For some values of a, there is an asymptotic steady state X A > 0 (Fig. 2C). By "asymptotic steady state of a variable", we mean the value towards which the variable tends asymptotically, as time tends to infinity. It is not a steady state sensu stricto, because the other variable (Z) continues to increase. Nevertheless it is the value of X towards which the system converges. In biological terms, it means that eventually, the quantity of silk is such that the departure rate tends to zero and the population size tends asymptotically to a given fraction X A of the initial population (Eq. 3).

For details on the calculations, see Appendix A.

X A = X 0 -aπ/2 ( 3 ) X A > 0 if 0 < a < 2/π or N 0 > αβπ/2γ
Our model can be used to determine the necessary conditions for a successful establishment. The parameter space in which the population stabilises at a fraction X A of the initial population can be interpreted biologically. First of all, > 1 means that the response of the spiders to the presence of silk has to be S-shaped. Secondly, N 0 > αβπ/2γ means that the initial population has to exceed a critical size, for the colony to be viable. Note that the larger the silk threshold (α) and the maximal departure rate β, the larger the population has to be in order to settle successfully. Logically, if silk production (γ) is very intense, the population threshold is lowered.

This asymptotic steady state is a particularity of the case where degra-

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dation is negligible, however for b = 0, X is also attracted to asymptotical steady state X A , although not indefinitely (Fig. 2C). Nevertheless, if the population is maintained near the asymptotic steady state for long enough, reproduction can compensate for the departure rate of individuals until there is enough silk for the establishment to be successful.

Flow between n interconnected populations

In this section, we focus on the flow between populations. Hence we will temporarily consider the probability of exiting the system (P e ) to be nil. The combined effects of flow and departure (P e ) are discussed in the next section.

The general form of the model for n interconnected populations is given by Eq. 4 (in its scaled form).

dX i /dτ =-X i /(1 + (Z i /a) ) + 1/(n -1) j =i) X j /(1 + (Z j /a) ) dZ i /dτ =X i -bZ i (4)
As for the isolated population, the dynamics vary depending on . If ≤ 1 there is only one stable steady state X 1 = X 2 ... = X n , which means that regardless of the initial conditions, the flow tends to equalise the size of the populations and they eventually reach the same size. If > 1, however, the system may exhibit an asymmetric stable steady state with one population greater than all the others X 1 > X 2 = X 3 ... = X n . Equation 5gives the sizes of the populations at the asymmetric steady state when = 2.

X 1 =(1 + 1 -4(n -1)(ab) 2 )/2 X j =(1 -X 1 )/(n -1) for j = 0
(5)

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This asymmetric steady state is real and positive (and therefore biologically meaningful) if n < 1 + 1/(2ab) 2 . In these conditions, it is always stable.

On the other hand, the symmetrical steady state is unstable when the asymmetrical steady state exists, except when n > 1/ab, in which case they are both stable (see Appendix B for the stability analysis). The initial conditions determine which of the two steady states is reached. See Fig. 3 for a stability diagram. A bifurcation diagram (Fig. 4) shows the solutions for the particular case of two populations (n = 2).

To summarise, the lower the product ab , the greater the asymmetry between the two populations. In biological terms, (ab = αθ/γN 0 ), this means that a high silk threshold (α) and a high degradation rate (θ) result in a high asymmetry, even for a very fragmented (n >> 1) landscape (Fig 3 .). On the contrary, high silk production rates (γ) and high initial population sizes (N 0 )

have an inhibiting effect on the asymmetry.

The evolutionary advantage of this heterogeneous distribution is easy to

IF i1 =F (N 1 )(1 + r(N 1 -1)) + rF (N 2 )N 2 IF i2 =rF (N 1 )(N 1 -1) + F (N 2 )(1 + rN 2 ) (6)
The fitness difference IF for the movement from the first population to the second is expressed as

IF = IF i2 -IF i1 = F (N 2 ) -F (N 1 )
. This fitness difference is a gain when N 2 > N 1 and a loss when N 2 < N 1 . This means that it is always beneficial in terms of fitness to move to the largest population. Therefore, any asymmetry between the two populations should be amplified to maximize the inclusive fitness of all individuals.

Experimental validation of the model

In order to test the validity of our model on the social spider Anelosimus eximius, we conducted a field experiment on Anelosimus eximius. Previous experiments on a T-maze [START_REF] Bernard | L'attraction pour la soie , base de la cohésion du groupe et des comportements collectifs chez les araignées sociales[END_REF] show that the silk of conspecifics has a retention effect on these spiders. Here, we test whether this retention effect results in the dynamics we predict. We artificially reproduce the particular case of two interconnected founding populations in the field and compare it with our theoretical predictions. This setup offers the possibility to analyse the coupled effects of flow between populations and of departure from the system.

Experimental methods

Anelosimus eximius is a highly cooperative theridiid spider found through- of about 250 km on the coast of French Guiana following a previously used procedure [START_REF] Mailleux | How[END_REF]Saffre et al., 1999). Thirteen fork shaped trees were used as the setups for thirteen experiments. Each tree was shaped so as to be as similar as possible on each side, by removing branches, then adjusting branch lengths and trimming the number of leaves on either side (Fig. 5A). The local surroundings (a radius of 2m) of each tree were cleared of vegetation to minimize immediate emigration of the spiders after release.

At t=0, we placed plastic cups (10 cm deep, 5 cm wide) containing 50 spiders on each of the two branches. After t 1 = 24hrs and t 2 = 48hrs, the number of spiders on each branch was counted.

Individual Based Model

It is not convenient to compare experimental results with the solutions of ordinary differential equations (ODE), because the latter only give the mean field results or more precisely the most likely results. Therefore, we formulated an individual based model (IBM), which can be thought of as a stochastic version of our deterministic ODE model. The algorithm for the IBM simulations is described in Fig. 5B. In the IBM, we included the probability P a of spinning an amount γ/P a of silk. This allowed us to incorporate the fact that spiders do not spin all the time. It is not really a difference with the ODE model, in the sense that on average, each spider spins γ silk per time unit. Therefore, the mean of the results of the IBM simulations match the ODE numerical resolutions (see an example in Fig. 6).

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Results

After 24 hours, the distribution of the spiders between the two cups was already noticeably asymmetric (Fig. 7). We performed binomial tests to

show that the distribution of the spiders between the two sides was different from the expected distribution if they had chosen at random, independently from each other, with an equal probability of going to either side (binomial distribution). All experiments except one clearly differed from a binomial distribution (p-values always lower than 0.0005). The selection of one of the branches did not result from a bias in the setup, because the number of times the left and the right branch were selected was not statistically different from a binomial choice of probability 0.5 (P-value=0.1334). From then onwards, the populations on each side remained stable: a linear regression (R 2 = 0, 9249) between the populations at 24 hours and 48 hours (P 48 = s • P 24 + y 0 )

shows that the slope is not different from 1 (s = 1.046 (0.9201, 1.171)) and that the y-intercept is not different from zero (y 0 = -0.3966(-5.918, 5.125)).

We ran the IBM simulations, conducting a systematic parameter search (see Appendix C) and found that the parameter set in table 1 provides the best correspondence between the experiments and the simulations (Fig. 7).

Although the model does not reproduce the great variability of the experimental results, we found that, on average, the experimental and theoretical results were very close (Table 2).

Markers versus conspecifics

It is interesting at this point to assess the extent to which the patterns we observe in our model are caused by the intervention of a marker. In

(X) = 1/(1 + (X i /a ) ),
where a = α/N 0 . This function is used in other models of conspecific retention [START_REF] Amé | Cockroach aggregation based on strain odour recognition[END_REF][START_REF] Jeanson | Conspecific attraction and shelter selection in gregarious insects[END_REF].

Let us first look at the case of n interconnected populations in a closed system (there is no departure from the system). The population on the i th patch is described by Eq. 7.

dX i /dτ = -X i f (X i ) + 1/(n -1) j =i X j f (X j ) ( 7 )
All that is said about the silk model in n interconnected sites is true of this model too, at least qualitatively. For > 1, there are asymmetric steady states with one population greater than all the others. The values and the stability of the steady states have the same form as the ones of our marker model (see Fig. 3 and Fig. 4), except for a parameter (a instead of ab).

However, this similarity between the two models ceases to be true in an open system that allows exits. Indeed, the transfer of information through markers can provoke qualitative changes in the behaviour of the system. If we look at the effect of departures only (the case of one population), the system is described by Eq. 8.

dX/dτ = -X/(1 + (X/a ) ) ( 8)

Like the marker model, the alternative model has only one stable steady state (X s = 0). However, in this case there is no asymptotic steady state,

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regardless of the exponent . We can see this by looking at the evolution of the population X as a function of time. Integration of Eq. 8 gives:

ln X/X 0 + (X -1 -X -1 0 )/ a = -τ
It appears clearly that when time τ tends to infinity, X tends to zero. Therefore we can conclude that in the alternative model there are no asymptotic steady states. This is an important difference, because it means that the population goes extinct unless the retention effect is secured by a marker that accumulates.

Furthermore, we ran individual-based simulations with the alternative model, searching a wide range of parameters (following the method described in Appendix C), and we could not reproduce our experimental results (see Fig. 8). The main feature that we were unable to obtain with the simulations is the stability of the results between 24 and 48 hours. It is impossible for the simulated populations to remain at a constant size for that long, because the retention effect of conspecifics is not sufficiently stable. As conspecifics leave, the probability of leaving increases, and this positive feedback leads the population to extinction. A marker that does not degrade on the other hand creates a much more stable retention effect.

Discussion

In One key feature of the theoretical results is the importance of the shape of the response function. Indeed, thresholds and asymmetries appear only when the function describing the response to the marker is S-shaped. As Sumpter and Pratt (2009) point out, the non-linearity of individual responses, in particular of sigmoidal responses is key to the speed and accuracy of collective decisions. They call this type of sigmoidal response "quorum response". The difference between our response function and quorum responses, is that in this case the response is not a function of the number of conspecifics, but of a marker that is, in turn, a cumulative function of the number of conspecifics.

This difference is crucial because it is precisely this property that is responsible for the appearance of asymptotic steady states, thresholds, and the stability of the populations in an open system. In a closed system, however, the spatial patterns observed (aggregation versus homogeneous distribution) are similar to those obtained with other quorum models [START_REF] Amé | Cockroach aggregation based on strain odour recognition[END_REF][START_REF] Jeanson | Conspecific attraction and shelter selection in gregarious insects[END_REF], as we show in the previous section.

The agreement between the experiments and the simulations suggests that social spiders respond to silk in a sigmoidal way. This agreement alone is not in itself sufficient to validate the model with certainty. To complete this validation, further experiments would be useful (eg. film the whole experiment to observe the fine kinetics of the collective choice, test different sizes for the total population, or measure individual time of residence in a patch as a function of silk...). Nonetheless, the fact that A.eximius are

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attracted and retained by silk [START_REF] Bernard | L'attraction pour la soie , base de la cohésion du groupe et des comportements collectifs chez les araignées sociales[END_REF], the agreement between our model and the experiments, and the disagreement between tha latter and the alternative model (direct evaluation of conspecifics), altogether make a strong case for our hypothesis.

According to our theoretical predictions on a single population, this sigmoidal response to silk implies that the number of founders has to exceed a critical value for settlement to be successful. This would explain the results of [START_REF] Leborgne | Experimental study of foundation and development of Anelosimus eximius colonies in the tropical forest of French Guiana[END_REF], namely that big groups (> 100 spiders) settle successfully where they are released whereas isolated females mostly leave the area after a very short time. It has been shown that isolated females can and do settle alone [START_REF] Avilés | Colony Size and Individual Fitness in the Social Spider Anelosimus eximius[END_REF], although it is quite rare and their survival chances are relatively small (Vollrath, 1982).

There is a substantial amount of evidence that suggests that A. eximus, like most cooperative species, is submitted to Allee effects [START_REF] Avilés | Colony Size and Individual Fitness in the Social Spider Anelosimus eximius[END_REF] and the references therein). Strong Allee effects can give rise to thresholds (called Allee thresholds) of colony size under which the population is not viable: if there are not enough individuals in the population, it dies out (Courchamp et al., 2008;Stephens and Sutherland, 1999). The threshold in population size that results from our model is somewhat different from an Allee threshold. In populations below the threshold of our model, the individuals do not die, they just leave. We suspect that both types of threshold exist. The thresholds provoked by silk retention would guarantee that the settling populations are large enough to overcome Allee effects. Silk attraction/retention could therefore have been selected, amongst other reasons, because it ensures that colonies do not settle under a non-viable size.

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We used a binary choice setup to show that spiders tend to adopt an asymmetrical distribution. An almost identical Y-choice setup had been used

to show that silk draglines can act as a recruitment factor and lead to group migration (Jeanson et al., 2004a;Saffre et al., 1999). In these experiments, the initial population was placed in the middle of the setup and the spiders were given the choice between the two branches. Eventually they made a collective choice for one of these branches. In our experiments, we start with two populations and observe that the individuals tend to aggregate into one population. The mechanisms at stake are somewhat different but the two phenomena are complementary: while the former experiments show that silk maintains the cohesion of the group, in the latter silk triggers the aggregation and provokes the cohesion of a fragmented group. These two strategies (group migration and settling where the amount of settlers exceeds a threshold) serve the same functional purpose of insuring subsequent growth of the colony. This redundancy confers a high degree of robustness to the system (Hammerstein, 2006): if the group migration fails, silk attraction offers the possibility for the cohesion to be restored.

Although on average our simulations agree with the experiments, they are slightly less scattered than the experimental results. However, this should not discredit the model: the experimental variability could be due to other external parameters that we did not measure or control (micro-climate, particularities of each experimental tree). Nevertheless, we should point out that for the sake of generality, we had to sacrifice some precision in the model. For example, we assumed throughout our study that all the sites were of equal quality. However, in a natural context where the sites may differ in lighting,

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availability of prey, etc... it is likely that the quality of the site influences the decision to leave or to settle and thereby the quantity of silk. In modelling terms, the quality of the site would affect parameter β (probability to leave the site when there is no silk), and indirectly also affect the quantity of silk.

It would be an interesting perspective to test the distribution and aggregation patterns of the spiders on two sites of different quality experimentally (eg. by controlling the number of leaves on each site). Indeed, the interplay between conspecific attraction and environmental preference can produce a wide range of distribution patterns [START_REF] Jeanson | Conspecific attraction and shelter selection in gregarious insects[END_REF]. This question has been studied in other species (Franks et al., 2003;Pratt et al., 2002;Halloy et al., 2007;Jeanson et al.,2004b). Also, we did not take any saturation effect into account, however it would be reasonable to expect settlers to avoid overcrowded areas [START_REF] Avilés | Colony Size and Individual Fitness in the Social Spider Anelosimus eximius[END_REF]. [START_REF] Bernard | L'attraction pour la soie , base de la cohésion du groupe et des comportements collectifs chez les araignées sociales[END_REF] show that hunger decreases attraction for silk. In overcrowded areas, food shortage could provoke a decrease in the retention effect of silk, and hungry individuals would have a higher probability of leaving.

Our model applies to A. eximius but also possibly to other social spiders, and even to gregarious territorial spiders, that show different degrees of affinity for silk [START_REF] Krafft | Contribution la biologie et l'éthologie d' Agelena consociata Denis (araigne sociale du Gabon) II[END_REF][START_REF] Leborgne | Influence of conspecific silk-structures on the choice of a web-site by the spider Zygielle x-notata (Clerck)[END_REF]Schuck-Paim and Jimenez Alonso, 2001). Furthermore, silk is spun by many arthropods (see Craig, 1997 for a review). Well-documented examples are caterpillars (Fitzgerald and Costa, 1999), weaver ants (Oecophylla) [START_REF] Hölldobler | The evolution of communal nest-weaving in ants[END_REF], spider mites (Tetranychidae) (Saito, 1986) and of course, spiders (see Vollrath and Selden, 2007 for a review). In many of these species, silk plays a key role in communication, and in favouring cooperative interactions A c c e p t e d m a n u s c r i p t (Bernard and Kraft, 2002). There is evidence of affinity for conspecific silk in other spinning arthropods, such as spider mites (Yano, 2008) and caterpillars (Colasurdo andDespland, 2005, Fitzgerald, 1995), therefore it is likely that the predictions we make apply to other species.

In this context, silk can be thought of as an extended phenotype used as a signal. Employing the extended phenotype such as a retentive secretion as a signal is common practice in a great number of gregarious species such as cockroaches [START_REF] Amé | Cockroach aggregation based on strain odour recognition[END_REF] or ants (Depickère et al., 2004;Sempo et al., 2006). Silk carries information about the number of conspecifics in a patch, and it is likely that spiders use that information as a signal. This type of communication offers several advantages. For instance, extended phenotypes are by definition separated in space and in time from the signaller (Schaedelin and Taborsky, 2009). This enables the signaller to allocate energy and time to other purposes whilst the information is conveyed. Furthermore, once produced, the information can be used by any number of receivers for an extended period with no further expenditure of energy.

Colonisation is one of the crucial processes underlying metapopulation dynamics, and is of great importance in the study of invasions and range expansions. In this study we show that affinity for a marker can greatly affect the decision to leave or to settle in a patch. This may have strong implications on colonisation dynamics and in the spatial distribution of populations, specially for species exhibiting fission-fusion patterns (Couzin and Laidre, 2009). Our future work aims at quantifying the influence of retentive markers in an explicitly defined space, where the connexions between different sites are more complex than the ones defined in the present study. So far,

A c c e p t e d m a n u s c r i p t

∂X 1 ∂X 2 • • • ∂X n ∂Z 1 ∂Z 2 • • • ∂Z n ∂ Ẋ1 ∂ Ẋ2 . . . ∂ Ẋn ∂ Ż1 ∂ Ż2 . . . ∂ Żn ⎛ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎝ -F F n-1 • • • F n-1 -GF 2 GF 2 n-1 • • • GF 2 n-1 F n-1 -F . . . F n-1 GF 2 n-1 -GF 2 . . . GF 2 n-1 . . . . . . . . . . . . . . . . . . . . . . . . F n-1 F n-1 • • • -F GF 2 n-1 GF 2 n-1 • • • -GF 2 1 0 • • • 0 -b 0 • • • 0 0 1 . . . 0 0 -b . . . 0 . . . . . . . . . . . . . . . . . . . . . . . . 0 0 • • • 1 0 0 • • • -b ⎞ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠ With F = 1 1 + (1/abn) 2 and G = 2 a 2 bn 2
We calculate the eigen values associated with the jacobian matrix. The steady state is stable when all of its eigenvalues are negative or zero. There are only four different eigen values of which two (ω 3 and ω 4 ) are repeated n/2 -1 times:

ω 1 = 0 ω 2 = -b ω 3 = b -nb -nF -(b -nb + nF ) 2 + 4n(n -1)GF 2 2(n -1) ω 4 = b -nb -nF + (b -nb + nF ) 2 + 4n(n -1)GF 2 2(n -1)
All eigen values are always negative or equal to zero, except ω 4 . For the symmetric steady state to be stable ω 4 cannot be positive. Solving ω 4 ≥ 0 Out of the seven parameters of the IBM model, four could be estimated thanks to the following assumptions. We set a time step of one minute as it took approximately one minute for spiders to move from one branch to the other. The asymmetry of the experimental results suggested that > 1, so we took = 2. We assumed that silk did not degrade over the two days of the experiment (θ = 0). We defined one unit of silk as the amount of silk spun by an active spider in one minute, so γ = 1. On average 30 spiders left the setup during the first 48 hours. This allowed us to estimate the probability to exit the setup P e = 0.01.

The remaining 3 parameters (α,β,P a ) had to be fine tuned. Our knowledge of the system and a preliminary random search helped us define the range that we would test for each of the 3 parameters:

0 ≤ β ≤ 1 0 ≤ P a ≤ 1 1000 ≤ α ≤ 2000

A c c e p t e d m a n u s c r i p t

It is now useful to visualise this parameter space as a three-dimensional grid, each dimension being a parameter and each intersection of the grid being a parameter set. We searched this three-dimensional parameter space by brute force, that is we tested every parameter set (every intersection) and searched for the best correspondence between the experiments and the simulations.

Of course near the best correspondence zone, we narrowed the grid in order to get more precision. We chose two criteria to define a good correspondence:

(1) a minimal difference between the experimental and simulated averages (total number of spiders and number of spiders in the selected side) for both 24 hours and 48 hours, (2) a maximum number of experiments falling in the 95% confidence interval of the simulations (see Fig. 6). Expectedly, the optimum for the two criteria coincided in the parameter set we propose.

P

  e of exiting the system, or a probability 1 -P e of spreading into the other patches. The probabilities of moving into any of the other patches are equal 1/(n -1);

  Mathematically, it simplifies the analytical resolutions by reducing the number of parameters substantially.

  parameter set, population decreases until it becomes extinct. What varies with the parameters is the amount of time it takes. The slowest case possible is when b = 0 (silk degradation rate θ is negligible). For non-zero values of decrease of the population occurs more quickly (Fig.2A,B).

  understand for species subject to Allee effects. By definition, the individual fitness F of such species is an increasing function of the number of individuals N, at least up to some extent. Clearly, at very high population sizes the cost of competition outweighs the benefits of group living. Nevertheless, the population sizes we consider here (founding populations) are far below that point. Let N 1 and N 2 be two connected populations in a closed system. The inclusive fitness IF of an individual can be calculated as the sum of his direct fitness (his own offspring) and his indirect fitness (his conspecifics' offspring multiplied by their relatedness coefficient r). Eq. 6 gives the inclusive fitness A c c e p t e d m a n u s c r i p t of an individual i of the first population IF i1 and the inclusive fitness for that same individual if he were to move to the second population IF i2 .

out

  Central and South American forests, occupying edge habitat along open A c c e p t e d m a n u s c r i p t byways such as roads or riverbanks. The experiments were carried out in this natural habitat. Spiders were collected from various nests along a transect

  could direct evaluation of the number of individuals produce the same results? In an alternative model where markers do not intervene, the probability to leave decreases with the number of individuals instead of the quantity of silk, according to the same function f

  this study, we examine how retention caused by an indirect indicator of the number of conspecifics -such as the quantity of a marker secreted by conspecifics -affects the dynamics of settlement. The theoretical part of this study provides a few insights into the growth and distribution patterns exhibit this type of positive response to conspecifics. The experimental part allows us to validate and parameterise the model on Anelosimus eximius.

  If we replace G and F by their respective expressions, we have 2 a 2 bn 2 (1 + (1/abn) 2 )

  Stephens, P.A., Sutherland, W.J., Freckleton, R.P., 1999. What is the Allee effect? Oikos 87,185-90 Venticinque, E. M., Fowler, H. G., Silva, C.A.. 1993. Modes and frequencies of colonization and its relation Vollrath, F., 1982. Colony foundation in a social spider. Zeitung Tierpsychologie 60, 313-324 Volrath, F., 1986. Environment, reproduction and the sex ration of the social spider Anelosimus eximius (Araneae, Theridiidae). Journal of Arachnology 14,267-281 Vollrath, F. Selden, P., 2007. The role of behavior in the evolution of spiders, silks, and webs. Annual review of ecology evolution and systematics 38,819-846 Wagner, R.H., Danchin, E., 2003. Conspecific copying: a general mechanism of social aggregation. Animal Behaviour 65,405408, doi:10.1006/anbe.2003.2037 Yano, S., 2008. Collective and solitary behaviors of twospotted spider mite (Acari:Tetranychidae) are induced by trail following. Annals of the Entomo-

Figure . 4 :

 4 Figure .4: Three-dimensional bifurcation diagram: Values of the steady states X s as a function of the two parameters a and b. The black surface represents the unstable steady states, whereas the grayscle surface represents the stable steady states.

Figure . 5 :

 5 Figure .5: Experimental setup (A) and IBM algorithm (B). The dimensions of the trees in cm (mean ± STD): d = 54.5 ± 6.0, D = 72.6 ± 13.3, h = 41.1 ± 7.2, H = 47.5 ± 9.1. Number of leaves in the right: 15.4 ± 6.0 and on the left d = 14.8 ± 6.0.

Figure . 6 :

 6 Figure .6: Comparison between the stochastic (solid line) and the deterministic (dashed line) model with equal sets of parameters (IBM: α = 250, β = 0.5, γ = 1, = 2, θ = 0.14, P e = 0, P a = 1; ODE: a = 2.5, b = 0.28, = 2 ). The grey surface represents the standard deviation of the simulations. The steady states are identical, but they are reached faster in the IBM. This is easily explained by the discrete nature of the IBM. While increases in population size in the ODE model are infinitesimal, they are at least of one unit in the IBM, therefore the feedbacks are launched faster.

Figure . 7 :

 7 Figure .7: Experimental and simulation results. The experimental results significantly differ from the binomial distribution with one exception. Our simulation results coincide with the experiments, with the difference that they are slightly less scattered. The contour graph contains two areas: the light grey area contains the 95% most likely results from 2000 simulations. The dark grey area contains the remaining 5%. The parameter set used in the simulations is α = 1846, β = 0.535, γ = 1, = 2, θ = 0, P e = 0.01, P a = 0.05.

Figure . 8 :

 8 Figure .8: Experimental and simulation (2000 simulations) results for the alternative model. The best fits for 24 hours are rather bad fits for 48 hours and vice versa. If we maximise the goodness of fit for both 24 hours and 48 hours simultaneously, the goodness of fit is poor for both. The parameter sets used are A. α = 18.2, β = 0.525, = 2, P e = 0.0058; B. α = 14.8, β = 0.375, = 2, P e = 0.0058; C. α = 11.4, β = 0.45, = 2,P e = 0.0105

Table . 1

 . : Variables and parameters.

Corresponding author: Astudillo Fernndez, A. (aastudil@ulb.ac.be -Tel:+3226505119 -Fax: +3226505987)

Bruxelles). J.L.D. is research associate from the Belgian National Funds for Scientific Research. We also wish to thank D. Lalor, N. Goldman and two anonymous referees for their helpful comments and suggestions.

Appendix A.Integration in the phase plane

In our model, there are two variables, and there is no simple analytical way of integrating both equations at once. What we propose instead is to look at the behaviour of the system in the phase plane. The phase plane is the plane defined by the variables X and Z. We can describe the trajectory of the system from an initial point (X 0 ,Z 0 ), by integrating the following expression:

This equation can be simplified because we focus on the case where b = 0:

The integral of this equation differs according to :

A c c e p t e d m a n u s c r i p t

This curve describes how the population X will vary according to Z, starting in the initial point (X = X 0 ,Z = 0). As time advances, the system will move along that curve: the population will logarithmically decrease and the quantity of silk will increase. At the point when the curve crosses the Z-axis, the population goes extinct X = 0. This takes place when Z = a(e 1/a -1). From then onwards, the system will remain at that point because no individuals will arrive and no silk will be spun.

if = 2, the integral is different:

This curve approaches a horizontal asymptote: as Z tends to infinity, X tends to X A = X 0aπ/2. This is an asymptotic steady state because it will never be reached, no matter how far we travel along that curve. Therefore as long as X A is positive, the population never goes extinct.

Appendix B. Stability of the symmetric steady state in n interconnected populations

At the symmetric steady state

The jacobian matrix can be written as follows:

A c c e p t e d m a n u s c r i p t the case of = 2. In the white area, the only stable steady state is the asymmetrical one.

In the light grey area, it is the symmetric steady state that is stable. In the dark grey area, both steady states are stable.


E K Kapaskelis

A Vouloumanou

P I Michalopoulos

Rafailidis

M E Falagas
email: m.falagas@aibs.gr>

N V Cholevas

A M Kapaskelis

E K Vouloumanou

A Michalopoulos

P I Rafailidis

Epidemiological aspects of 2009 H1N1 influenza: the accumulating experience from the Northern Hemisphere

Epidemiological aspects of 2009 H1N1 influenza: the accumulating experience from the Northern Hemisphere

<H1>. Introduction

Influenza is an important clinical entity. The burden of this infection is considerable, as it is associated with considerably high hospitalization and mortality rates, as well as with increased direct and indirect costs. [START_REF] Molinari | The annual impact of seasonal influenza in the US: measuring disease burden and costs[END_REF] costs [START_REF] Molinari | The annual impact of seasonal influenza in the US: measuring disease burden and costs[END_REF]. The burden of influenza infection became even more pronounced with the emergence of the current influenza pandemic. Inevitably, the effective management of this new global threat necessitates the epidemiological features of 2009 pandemic influenza.

Evidence deriving from studies presenting the experience, experience of countries of the Southern Hemisphere with pandemic influenza, as well as preliminary data from U.S.A the USA, suggests that 2009 pandemic influenza affects mostly populations of younger age, [START_REF] Jain | Hospitalized patients with 2009 H1N1 influenza in the United States, April-June April-June 2009[END_REF][START_REF] Kelly | Grant, Interim analysis of pandemic influenza (H1N1) 2009 in Australia: surveillance trends, age of infection and effectiveness of seasonal vaccination[END_REF][4][START_REF] Webb | Critical care services and 2009 H1N1 influenza in Australia and New Zealand[END_REF] age [START_REF] Jain | Hospitalized patients with 2009 H1N1 influenza in the United States, April-June April-June 2009[END_REF][START_REF] Kelly | Grant, Interim analysis of pandemic influenza (H1N1) 2009 in Australia: surveillance trends, age of infection and effectiveness of seasonal vaccination[END_REF][4][START_REF] Webb | Critical care services and 2009 H1N1 influenza in Australia and New Zealand[END_REF], a fact that is contradictory to the knowledge regarding seasonal influenza influenza, in which severe disease as well as mortality is observed more frequently in elderly populations. [START_REF] Thompson | Epidemiology of seasonal influenza: use of surveillance data and statistical models to estimate the burden of disease[END_REF] populations [START_REF] Thompson | Epidemiology of seasonal influenza: use of surveillance data and statistical models to estimate the burden of disease[END_REF].

Additionally, patients with comorbidity and and, specifically, sub-populations such as pregnant women and obese individuals also appear to suffer more severely form from pandemic influenza. [4,[START_REF] Webb | Critical care services and 2009 H1N1 influenza in Australia and New Zealand[END_REF]influenza [4,[START_REF] Webb | Critical care services and 2009 H1N1 influenza in Australia and New Zealand[END_REF].

The aim of this review was to collect and evaluate the available evidence regarding various epidemiological aspects of 2009 H1N1 influenza that was derived from studies, published up to this writing, referring to the experience of countries from the Northern Hemisphere with pandemic influenza.

<H2>. Data sources

The articles to be included in our review were retrieved from searches performed in the PubMed database. The PubMed database was assessed until to the end of November, November 2009. We used the following search term: "[(swine OR H1N1) AND (flu OR influenza OR virus OR outbreak OR pandemic) AND "last 6 months"]", that which is currently provided from by the PubMed database to its users, in order to enable quick and easy access to the latest H1N1 citations. We also hand-searched the bibliographies of relevant articles.

<H2>. Study selection criteria

A study was considered as eligible for inclusion in our review if it reported original data regarding epidemiological features of patients of any age with 2009 influenza A (H1N1) infection (either confirmed or probable), probable) and also originated from countries of the Northern Hemisphere. Abstracts presented in scientific conferences were not regarded as eligible for inclusion in our review. Only articles published in the English language were regarded as eligible for inclusion in our review.

<H2>. Data extraction

Two reviewers (N.V.C and E.K.V.) performed the literature search, assessed the eligibility of relevant studies, and extracted the data, independently. Data obtained from the included articles referred to study characteristics (country, study period, study population), demographic characteristics of the patients (age, sex), any kind of potential risk factors for influenza, comorbidity (such as obesity, pregnancy, respiratory diseases, cardiovascular disease, diabetes mellitus, chronic neurological disease), as well as history of seasonal influenza 2008-2009 2008-2009 vaccination). The above-mentioned data were extracted for the total of the whole patient populations involved in each one of the included studies, as well as for the sub-populations of patients with pandemic influenza of different severity, as this was indicated by the level of health care health-care they received, [hospitalized received (hospitalized patients, patients admitted in the intensive care unit (ICU)] [ICU]), and the outcome of the influenza infection (fatal cases).

<H2>. Study characteristics

A total of 35 studies were regarded as eligible for inclusion in our review. [2, review [2, 4, 7-39] 7-39]. Specifically, fourteen 14 of the 35 included studies referred to European countries, [START_REF] Rello | Intensive care adult patients with severe respiratory failure caused by Influenza A (H1N1)v in Spain[END_REF][START_REF]New influenza A(H1N1) investigation teams (2009) New influenza A(H1N1) virus infections in France, April -May 2009[END_REF][START_REF]Surveillance Group for New Influenza A(H1N1) Virus Investigation and Control in Spain (2009) New influenza A(H1N1) virus infections in Spain, April-May 2009[END_REF][START_REF]HPA Northern Ireland Swine influenza investigation teams (2009) Epidemiology of new influenza A (H1N1) virus infection, United Kingdom[END_REF][11][START_REF] Ciblak | Cases of influenza A(H1N1)v reported in Turkey, May-July 2009 May[END_REF][START_REF] Gilsdorf | Working Group Pandemic Influenza A(H1N1)v (2009) Influenza A (H1N1)v in Germany: the first 10,000 cases[END_REF][START_REF] Hahne S Hahné | Epidemiology and control of influenza A(H1N1)v in the Netherlands: the first 115 cases[END_REF][15][START_REF] Lytras | Enhanced surveillance of influenza A(H1N1)v in Greece during the containment phase[END_REF][START_REF] Yeung | Presentation and management of critically ill patients with influenza A (H1N1): a UK perspective[END_REF][START_REF] Lister | Swine-origin influenza virus H1N1, seasonal influenza virus, and critical illness in children[END_REF][START_REF] Smith | An outbreak of influenza A(H1N1)v in a boarding school in South East England, May-June May[END_REF][START_REF]Preliminary descriptive epidemiology of a large school outbreak of influenza A(H1N1)v in the West Midlands, United Kingdom, May 2009 (2009) 2009[END_REF] 8 countries [START_REF] Rello | Intensive care adult patients with severe respiratory failure caused by Influenza A (H1N1)v in Spain[END_REF][START_REF]New influenza A(H1N1) investigation teams (2009) New influenza A(H1N1) virus infections in France, April -May 2009[END_REF][START_REF]Surveillance Group for New Influenza A(H1N1) Virus Investigation and Control in Spain (2009) New influenza A(H1N1) virus infections in Spain, April-May 2009[END_REF][START_REF]HPA Northern Ireland Swine influenza investigation teams (2009) Epidemiology of new influenza A (H1N1) virus infection, United Kingdom[END_REF][11][START_REF] Ciblak | Cases of influenza A(H1N1)v reported in Turkey, May-July 2009 May[END_REF][START_REF] Gilsdorf | Working Group Pandemic Influenza A(H1N1)v (2009) Influenza A (H1N1)v in Germany: the first 10,000 cases[END_REF][START_REF] Hahne S Hahné | Epidemiology and control of influenza A(H1N1)v in the Netherlands: the first 115 cases[END_REF][15][START_REF] Lytras | Enhanced surveillance of influenza A(H1N1)v in Greece during the containment phase[END_REF][START_REF] Yeung | Presentation and management of critically ill patients with influenza A (H1N1): a UK perspective[END_REF][START_REF] Lister | Swine-origin influenza virus H1N1, seasonal influenza virus, and critical illness in children[END_REF][START_REF] Smith | An outbreak of influenza A(H1N1)v in a boarding school in South East England, May-June May[END_REF][START_REF]Preliminary descriptive epidemiology of a large school outbreak of influenza A(H1N1)v in the West Midlands, United Kingdom, May 2009 (2009) 2009[END_REF], eight to U.S.A.,[2, the USA [START_REF] Jain | Hospitalized patients with 2009 H1N1 influenza in the United States, April-June April-June 2009[END_REF]4,[21][START_REF] Jamieson | H1N1 2009 influenza virus infection during pregnancy in the USA[END_REF][23][START_REF]Novel influenza A (H1N1) virus infections among health-care personnel -United personnel-United States, April-May 2009 (2009)[END_REF][25][START_REF] Crum-Cianflone | Clinical and epidemiologic characteristics of an outbreak of novel H1N1 (swine origin) influenza A virus among United States military beneficiaries[END_REF] 5 [21][START_REF] Jamieson | H1N1 2009 influenza virus infection during pregnancy in the USA[END_REF][23][START_REF]Novel influenza A (H1N1) virus infections among health-care personnel -United personnel-United States, April-May 2009 (2009)[END_REF][25][START_REF] Crum-Cianflone | Clinical and epidemiologic characteristics of an outbreak of novel H1N1 (swine origin) influenza A virus among United States military beneficiaries[END_REF], five to Mexico, [31][32][33][34][35] 4 Mexico [31][32][33][34][35], four to Canada, [27][START_REF] Cutler | Investigation of the first cases of human-to-human infection with the new swine-origin influenza A (H1N1) virus in Canada[END_REF][START_REF] O'riordan | Risk factors and outcomes among children admitted to hospital with pandemic H1N1 influenza[END_REF][START_REF] Fisman | Older age and a reduced likelihood of 2009 H1N1 virus infection[END_REF] 2 Canada [27][START_REF] Cutler | Investigation of the first cases of human-to-human infection with the new swine-origin influenza A (H1N1) virus in Canada[END_REF][START_REF] O'riordan | Risk factors and outcomes among children admitted to hospital with pandemic H1N1 influenza[END_REF][START_REF] Fisman | Older age and a reduced likelihood of 2009 H1N1 virus infection[END_REF], two to Japan,[37, 38] 1 Japan [37, 38], one to Colombia,[36] Colombia [36], whereas the remaining 1 study referred to a specific number of fatal cases associated with pandemic influenza that were reported worldwide up to a specific time point. [39] point [39]. The data retrieved and evaluated from this specific study referred only to continents or countries of the Northern Hemisphere. The data retrieved from studies originating from European countries, USA and Canada, and from the rest of the world are presented in tabulated form in [32,34] May, [35] April [32,34], May [35], and June, [31] June [31], respectively. Eleven of the 35 included studies referred to a time period starting from April and ending in May, [START_REF]New influenza A(H1N1) investigation teams (2009) New influenza A(H1N1) virus infections in France, April -May 2009[END_REF]May [8,[START_REF]Surveillance Group for New Influenza A(H1N1) Virus Investigation and Control in Spain (2009) New influenza A(H1N1) virus infections in Spain, April-May 2009[END_REF][START_REF] Ciblak | Cases of influenza A(H1N1)v reported in Turkey, May-July 2009 May[END_REF][21][START_REF] Jamieson | H1N1 2009 influenza virus infection during pregnancy in the USA[END_REF][23][START_REF]Novel influenza A (H1N1) virus infections among health-care personnel -United personnel-United States, April-May 2009 (2009)[END_REF][START_REF] Crum-Cianflone | Clinical and epidemiologic characteristics of an outbreak of novel H1N1 (swine origin) influenza A virus among United States military beneficiaries[END_REF][START_REF] Cutler | Investigation of the first cases of human-to-human infection with the new swine-origin influenza A (H1N1) virus in Canada[END_REF]33,37] 4 37], four to a period starting from April and ending in June, [START_REF] Jain | Hospitalized patients with 2009 H1N1 influenza in the United States, April-June April-June 2009[END_REF]June [2,[START_REF]HPA Northern Ireland Swine influenza investigation teams (2009) Epidemiology of new influenza A (H1N1) virus infection, United Kingdom[END_REF][START_REF] Hahne S Hahné | Epidemiology and control of influenza A(H1N1)v in the Netherlands: the first 115 cases[END_REF][START_REF] Fisman | Older age and a reduced likelihood of 2009 H1N1 virus infection[END_REF] 2 30], two to a period from April until July, [START_REF] Lytras | Enhanced surveillance of influenza A(H1N1)v in Greece during the containment phase[END_REF]39] 4 July [START_REF] Lytras | Enhanced surveillance of influenza A(H1N1)v in Greece during the containment phase[END_REF]39], four from April until August, [4,August [4,[START_REF] Rello | Intensive care adult patients with severe respiratory failure caused by Influenza A (H1N1)v in Spain[END_REF][START_REF] Gilsdorf | Working Group Pandemic Influenza A(H1N1)v (2009) Influenza A (H1N1)v in Germany: the first 10,000 cases[END_REF]27] 27], and 1 one to an extended time period from April to October. [15] October [15]. Four other studies covered a time period from May until June, [11,June [11,[START_REF] Smith | An outbreak of influenza A(H1N1)v in a boarding school in South East England, May-June May[END_REF][START_REF]Preliminary descriptive epidemiology of a large school outbreak of influenza A(H1N1)v in the West Midlands, United Kingdom, May 2009 (2009) 2009[END_REF]38] 38], whereas 2 two studies covered a time period from May until July. [START_REF] O'riordan | Risk factors and outcomes among children admitted to hospital with pandemic H1N1 influenza[END_REF]36] July [START_REF] O'riordan | Risk factors and outcomes among children admitted to hospital with pandemic H1N1 influenza[END_REF]36]. Two other studies covered a time period from June to July, [START_REF] Lister | Swine-origin influenza virus H1N1, seasonal influenza virus, and critical illness in children[END_REF]25] July [START_REF] Lister | Swine-origin influenza virus H1N1, seasonal influenza virus, and critical illness in children[END_REF]25], whereas the remaining 1 study covered a time period from June to September. [START_REF] Yeung | Presentation and management of critically ill patients with influenza A (H1N1): a UK perspective[END_REF] September [START_REF] Yeung | Presentation and management of critically ill patients with influenza A (H1N1): a UK perspective[END_REF]. Additionally, the evaluated population constituted solely of hospitalized cases with 2009 H1N1 influenza, [2, influenza [2, 4, 15, 17, 25, 29, 32, 37] in 8 of the 35 included studies, of patients admitted in into the intensive care unit (ICU) ICU in 3 studies,[7, three studies [START_REF] Rello | Intensive care adult patients with severe respiratory failure caused by Influenza A (H1N1)v in Spain[END_REF][START_REF] Lister | Swine-origin influenza virus H1N1, seasonal influenza virus, and critical illness in children[END_REF]27] 27], and from fatal cases associated with 2009 H1N1 influenza in 1 study. [39] one study [39]. Of note, 2 of the 35 included studies involved only pregnant cases. [START_REF] Jamieson | H1N1 2009 influenza virus infection during pregnancy in the USA[END_REF]25]cases [START_REF] Jamieson | H1N1 2009 influenza virus infection during pregnancy in the USA[END_REF]25].

<H2>. Hospitalization, ICU admission, and fatality rate

The rate of hospitalization rate among the evaluated pandemic influenza cases ranged between 0%-93.8% 0 and 93.8% in 13 studies that provided relevant data. [8, data [8, 10, 11, 13, 14, 19-22, 24, 26, 28, 36] 36]. The rate of admission in into the ICU among the evaluated pandemic influenza cases ranged between 0%-36.4% 0 and 36.4% in 4 studies. [14, four studies [START_REF] Hahne S Hahné | Epidemiology and control of influenza A(H1N1)v in the Netherlands: the first 115 cases[END_REF]21,[START_REF] Jamieson | H1N1 2009 influenza virus infection during pregnancy in the USA[END_REF][START_REF] Cutler | Investigation of the first cases of human-to-human infection with the new swine-origin influenza A (H1N1) virus in Canada[END_REF] 28]. The rate of admission in into the ICU among hospitalized cases ranged between 9%-66.7% 9 and 66.7% in 6 studies.[2, six studies [START_REF] Jain | Hospitalized patients with 2009 H1N1 influenza in the United States, April-June April-June 2009[END_REF]4,15,[START_REF] Yeung | Presentation and management of critically ill patients with influenza A (H1N1): a UK perspective[END_REF][START_REF] O'riordan | Risk factors and outcomes among children admitted to hospital with pandemic H1N1 influenza[END_REF]32] 32]. Specific data are presented in Table 1. The fatality rate among the evaluated pandemic influenza cases was 0% in 13 of the included studies. [8-10, studies [8-10, 12-14, 16, 19, 20, 24, 26, 28, 37] 37]. However, the respective rates reported from 5 five other studies ranged between 2%-38.5%. [18, 2 and 38.5% [START_REF] Lister | Swine-origin influenza virus H1N1, seasonal influenza virus, and critical illness in children[END_REF]21,[START_REF] Jamieson | H1N1 2009 influenza virus infection during pregnancy in the USA[END_REF]33,36] 36]. The fatality rate among hospitalized cases ranged between 0%-38.9% 0 and 38.9% in 6 six of the included studies,[2, studies [START_REF] Jain | Hospitalized patients with 2009 H1N1 influenza in the United States, April-June April-June 2009[END_REF]4,15,[START_REF] Yeung | Presentation and management of critically ill patients with influenza A (H1N1): a UK perspective[END_REF][START_REF] O'riordan | Risk factors and outcomes among children admitted to hospital with pandemic H1N1 influenza[END_REF]32] 32], whereas the fatality rate among cases admitted in into the ICU ranged between 25%-41.4% 25 and 41.4% in 3 studies.[7, three studies [START_REF] Rello | Intensive care adult patients with severe respiratory failure caused by Influenza A (H1N1)v in Spain[END_REF]27,31]31].

<H2>. Epidemiological characteristics of cases

In Table 4 4, we present the percentage of specific epidemiological groups (defined with regard to age, sex, or the presence of risk factors or comorbid conditions) among the patients involved in the respective included studies (stratified with regard to the type of diagnosis, level of health care healthcare they received, and outcome).

<H2>. Age and sex

Among the evaluated cases of 2009 H1N1 influenza, young and middle aged middle-aged adults constituted the majority of the reported cases; the percentages ranged between13.2%-83.7% between 13.2 and 83.7% among studies with available relevant data. Older children and adolescents followed followed, with percentages ranging between 6.3%-79.5%, 6.3 and 79.5%, and young children with percentages ranging from 2.3% 2.3 to 62.9%. This observation was consistent regarding the evaluated hospitalized cases. Specific data are presented in Table 4. In 2 two studies with available relevant data, the highest rate of admission in into the ICU was observed for young and middle aged middle-aged adults (50%, [START_REF] Rello | Intensive care adult patients with severe respiratory failure caused by Influenza A (H1N1)v in Spain[END_REF] (50% [START_REF] Rello | Intensive care adult patients with severe respiratory failure caused by Influenza A (H1N1)v in Spain[END_REF] and 58.8%, [27] 58.8% [27], respectively), whereas the lowest rate was observed for senior patients (3.1%, [START_REF] Rello | Intensive care adult patients with severe respiratory failure caused by Influenza A (H1N1)v in Spain[END_REF] (3.1% [START_REF] Rello | Intensive care adult patients with severe respiratory failure caused by Influenza A (H1N1)v in Spain[END_REF] and 8.3%, [27] 8.3% [27], respectively). Among fatal cases, young and middle aged middle-aged adults constituted the majority, with percentages ranging from 65.5% 65.5 to 91.7%, according to the data provided from 6 six of the included studies. [27, studies [27, 31, 33-35, 39]Yet, 39]. Yet, substantial percentages ranging from 4.2% 4.2 to 20.7% were observed for seniors. [27, seniors [27, 31, 33-35, 39]39]. Considerable differences were not observed between the male and female sex among the evaluated, hospitalized, ICU, and fatal cases. Specific data are presented in Table 4. Yet, men constituted the majority in 3 three studies reporting data for 97 laboratory confirmed case, [START_REF] Crum-Cianflone | Clinical and epidemiologic characteristics of an outbreak of novel H1N1 (swine origin) influenza A virus among United States military beneficiaries[END_REF] laboratory-confirmed cases [START_REF] Crum-Cianflone | Clinical and epidemiologic characteristics of an outbreak of novel H1N1 (swine origin) influenza A virus among United States military beneficiaries[END_REF], 58 hospitalized cases, [START_REF] O'riordan | Risk factors and outcomes among children admitted to hospital with pandemic H1N1 influenza[END_REF] cases [START_REF] O'riordan | Risk factors and outcomes among children admitted to hospital with pandemic H1N1 influenza[END_REF], and 32 ICU cases. [START_REF] Rello | Intensive care adult patients with severe respiratory failure caused by Influenza A (H1N1)v in Spain[END_REF] cases [START_REF] Rello | Intensive care adult patients with severe respiratory failure caused by Influenza A (H1N1)v in Spain[END_REF]. The respective percentages were 75.3%, [START_REF] Crum-Cianflone | Clinical and epidemiologic characteristics of an outbreak of novel H1N1 (swine origin) influenza A virus among United States military beneficiaries[END_REF] 60.3%, [START_REF] O'riordan | Risk factors and outcomes among children admitted to hospital with pandemic H1N1 influenza[END_REF] 75.3% [START_REF] Crum-Cianflone | Clinical and epidemiologic characteristics of an outbreak of novel H1N1 (swine origin) influenza A virus among United States military beneficiaries[END_REF], 60.3% [START_REF] O'riordan | Risk factors and outcomes among children admitted to hospital with pandemic H1N1 influenza[END_REF], and 73.3%. [START_REF] Rello | Intensive care adult patients with severe respiratory failure caused by Influenza A (H1N1)v in Spain[END_REF]73.3% [START_REF] Rello | Intensive care adult patients with severe respiratory failure caused by Influenza A (H1N1)v in Spain[END_REF].

<H2>. Seasonal (2008-2009) (2008-2009) influenza vaccination

The percentage of patients vaccinated for seasonal (2008-2009) (2008-2009) influenza ranged between 3.4%-66.3% 3.4 and 66.3% among the evaluated cases in 8 eight studies that provided relevant data. [9, data [9, 12, 14, 22, 24, 26, 28, 31] 31]. The respective percentage of hospitalized cases ranged between 15.8%-51.2% 15.8 and 51.2% in 3 three studies providing relevant data, [21, data [21, 32, 37] 37], whereas a single study reported a rather low percentage of 6% among ICU cases. [27] cases [27]. No relevant data were available regarding fatal cases.

<H2>. Comorbidity

Cases without any comorbidity constituted the majority of the evaluated cases in 2 of the 3 studies that provided relevant data. [START_REF]New influenza A(H1N1) investigation teams (2009) New influenza A(H1N1) virus infections in France, April -May 2009[END_REF][START_REF] Gilsdorf | Working Group Pandemic Influenza A(H1N1)v (2009) Influenza A (H1N1)v in Germany: the first 10,000 cases[END_REF] data [START_REF]New influenza A(H1N1) investigation teams (2009) New influenza A(H1N1) virus infections in France, April -May 2009[END_REF][START_REF] Gilsdorf | Working Group Pandemic Influenza A(H1N1)v (2009) Influenza A (H1N1)v in Germany: the first 10,000 cases[END_REF]. The respective percentages were 96.7%, [START_REF] Gilsdorf | Working Group Pandemic Influenza A(H1N1)v (2009) Influenza A (H1N1)v in Germany: the first 10,000 cases[END_REF] 96.7% [START_REF] Gilsdorf | Working Group Pandemic Influenza A(H1N1)v (2009) Influenza A (H1N1)v in Germany: the first 10,000 cases[END_REF] and 75%, [31] 75% [31], respectively. Previously healthy cases were also represented represented, with considerably high percentages ranging from 12.1% 12.1 to 81.6% in 9 nine studies that reported relevant data for hospitalized cases.[2, cases [START_REF] Jain | Hospitalized patients with 2009 H1N1 influenza in the United States, April-June April-June 2009[END_REF]4,15,[START_REF] Yeung | Presentation and management of critically ill patients with influenza A (H1N1): a UK perspective[END_REF]21,[START_REF]Novel influenza A (H1N1) virus infections among health-care personnel -United personnel-United States, April-May 2009 (2009)[END_REF][START_REF] O'riordan | Risk factors and outcomes among children admitted to hospital with pandemic H1N1 influenza[END_REF]32,37] 37]. Previously healthy cases were represented by percentages of up to 46.8% among ICU cases, [7, cases [7, 27, 29] and up to 42.1% among fatal cases.[2, cases [START_REF] Jain | Hospitalized patients with 2009 H1N1 influenza in the United States, April-June April-June 2009[END_REF]4,15,31,32] 32]. Common types of comorbidity comorbidity, such as respiratory diseases, including asthma and chronic obstructive pulmonary disease, cardiovascular disease, chronic neurological disease, and diabetes mellitus mellitus, were reported among the cases involved in the included studies. Specific data are presented in Table 4.

<H2>. Pregnancy

Two of the studies included in our review involved exclusively pregnant cases with H1N1 infection. [START_REF] Jamieson | H1N1 2009 influenza virus infection during pregnancy in the USA[END_REF]25] infection [START_REF] Jamieson | H1N1 2009 influenza virus infection during pregnancy in the USA[END_REF]25]. Specifically, 34 pregnant women (31 with laboratory confirmed laboratory-confirmed infection and 3 three with probable infection) were included in the one study. [START_REF] Jamieson | H1N1 2009 influenza virus infection during pregnancy in the USA[END_REF] study [START_REF] Jamieson | H1N1 2009 influenza virus infection during pregnancy in the USA[END_REF]. Fourteen (41.2%) of these 34 pregnant cases were hospitalized, 3 (8.8%) were included in the ICU, and 1 (2.9%) died. [START_REF] Jamieson | H1N1 2009 influenza virus infection during pregnancy in the USA[END_REF] died [START_REF] Jamieson | H1N1 2009 influenza virus infection during pregnancy in the USA[END_REF]. The other study recorded 7 seven hospitalized pregnant cases. [25] cases [25]. Regarding the remaining studies, pregnant women constituted 0.2%-16.7% 0.2 to 16.7% among the evaluated cases in 3 studies. [11, three studies [11,[START_REF] Gilsdorf | Working Group Pandemic Influenza A(H1N1)v (2009) Influenza A (H1N1)v in Germany: the first 10,000 cases[END_REF][START_REF] Crum-Cianflone | Clinical and epidemiologic characteristics of an outbreak of novel H1N1 (swine origin) influenza A virus among United States military beneficiaries[END_REF]

<H2>. Obesity

Obesity was recorded in 0.2%-36.2% 0.2 to 36.2% among the evaluated cases in 2 two of the included studies that provided relevant data, [13, data [13, 31] and 5.7%-47.6% 5.7 to 47.6% among hospitalized cases in 3 studies.[2, three studies [START_REF] Jain | Hospitalized patients with 2009 H1N1 influenza in the United States, April-June April-June 2009[END_REF]4,[START_REF] O'riordan | Risk factors and outcomes among children admitted to hospital with pandemic H1N1 influenza[END_REF]Obese 29]. Obese patients also constituted 18.8%, [START_REF] Rello | Intensive care adult patients with severe respiratory failure caused by Influenza A (H1N1)v in Spain[END_REF]and 33.3%, [27] 18.8% [START_REF] Rello | Intensive care adult patients with severe respiratory failure caused by Influenza A (H1N1)v in Spain[END_REF] and 33.3% [27] of the evaluated ICU cases reported in the respective 2 studies. [START_REF] Rello | Intensive care adult patients with severe respiratory failure caused by Influenza A (H1N1)v in Spain[END_REF]27]Among two studies [START_REF] Rello | Intensive care adult patients with severe respiratory failure caused by Influenza A (H1N1)v in Spain[END_REF]27]. Among fatal cases, obese patients constituted the 39% in a large study involving a total of 1088 1,088 patients and recorded a total of 118 deaths.

Morbidly obese patients (BMI > 40) (BMI>40) in particular, constituted the 13.8% of the evaluated cases in a single study, [31] study [31], whereas their percentage percentages ranged between 2.2%-26.0% 2.2 and 26.0% in 3 three studies that provided relevant data for hospitalized cases.[2, cases [START_REF] Jain | Hospitalized patients with 2009 H1N1 influenza in the United States, April-June April-June 2009[END_REF]4,15] 15]. In addition, morbidly obese patients constituted the 12.5% of the fatal cases recorded in a large study involving a total of 32 ICU patients. [START_REF] Rello | Intensive care adult patients with severe respiratory failure caused by Influenza A (H1N1)v in Spain[END_REF]patients [START_REF] Rello | Intensive care adult patients with severe respiratory failure caused by Influenza A (H1N1)v in Spain[END_REF].

<H2>. Comparisons between pandemic and seasonal influenza

Three of the 35 included studies provided comparative data regarding a specific period of pandemic influenza and previous seasonal influenza periods. [18, periods [18, 29, 34] 34]. Two of these 3 three studies referred to pediatric populations with 2009 H1N1 infection. [START_REF] Lister | Swine-origin influenza virus H1N1, seasonal influenza virus, and critical illness in children[END_REF][START_REF] O'riordan | Risk factors and outcomes among children admitted to hospital with pandemic H1N1 influenza[END_REF] infection [START_REF] Lister | Swine-origin influenza virus H1N1, seasonal influenza virus, and critical illness in children[END_REF][START_REF] O'riordan | Risk factors and outcomes among children admitted to hospital with pandemic H1N1 influenza[END_REF]. Specifically, 1 one of these 2 two studies provided comparative data regarding 13 critically ill pediatric cases of pandemic influenza versus 58 critically ill pediatric cases with seasonal influenza (reported during seasons 2004-2007). [START_REF] Lister | Swine-origin influenza virus H1N1, seasonal influenza virus, and critical illness in children[END_REF] 2004-2007) [START_REF] Lister | Swine-origin influenza virus H1N1, seasonal influenza virus, and critical illness in children[END_REF]. No substantial differences were observed regarding sex between the compared children. Median The median age of children with pandemic influenza was 9 years compared to 2.7 years of children with seasonal influenza. [START_REF] Lister | Swine-origin influenza virus H1N1, seasonal influenza virus, and critical illness in children[END_REF] influenza [START_REF] Lister | Swine-origin influenza virus H1N1, seasonal influenza virus, and critical illness in children[END_REF]. Regarding the other pediatric study that compared 58 hospitalized children with pandemic influenza with 200 hospitalized children with seasonal influenza (seasons 2004-2005, 2004-2005 and 2008-2009), 2008-2009), the median age of children with pandemic influenza was 6.4 years compared to 3.3 years of children with seasonal influenza. [START_REF] O'riordan | Risk factors and outcomes among children admitted to hospital with pandemic H1N1 influenza[END_REF]influenza [START_REF] O'riordan | Risk factors and outcomes among children admitted to hospital with pandemic H1N1 influenza[END_REF].

According to another study that also provided comparative data regarding the age distribution of fatal cases of pandemic influenza versus fatal cases of seasonal influenza (seasons 2006-2008), 2006-2008), the percentages of fatal pandemic cases were lower from those of the respective fatal seasonal cases in the age groups 0-4 years, 0-4 years and >60 years. [34] years [34]. Additionally, another study that compared a total of 1829 1,829 confirmed pandemic cases with cases that were found to be negative for H1N1v reported that patients <53 years of age had an increased risk of infection from pandemic influenza in comparison with to those that were found to be positive for seasonal H1N1 and H3N2 influenza strains. [START_REF] Fisman | Older age and a reduced likelihood of 2009 H1N1 virus infection[END_REF]strains [START_REF] Fisman | Older age and a reduced likelihood of 2009 H1N1 virus infection[END_REF].

<H1>. Discussion

The evaluation of contemporary published evidence regarding the experience of countries of the Northern Hemisphere with 2009 pandemic (H1N1) influenza suggests that pandemic influenza is an infection with a considerable hospitalization, ICU admission admission, and mortality rate. Specifically, a considerably higher fatality rate was observed in more severely afflicted cases. Young and middle aged middle-aged adults constituted the majority of the evaluated cases, followed by older children and adolescents. Yet, considerable percentages of elderly patients were reported among more severely afflicted cases. Men constituted the majority in only 3 three of the evaluated studies involving cases with different disease severity. Despite the fact that considerable percentages of patients without any underlying conditions were observed among the total of the cases involved in the herein reviewed studies, as well as among hospitalized, critically ill ill, and fatal cases, pregnancy and obesity appear to represent particular risk factors for pandemic (H1N1) 2009 influenza infection, as well as for severe pandemic influenza infection.

Our findings are in accordance with evidence derived from studies focused on the experience gained during the 2009 winter season from countries of the Southern Hemisphere. Specifically, these studies suggest that 2009 pandemic influenza occurs more frequently in younger populations, [START_REF] Kelly | Grant, Interim analysis of pandemic influenza (H1N1) 2009 in Australia: surveillance trends, age of infection and effectiveness of seasonal vaccination[END_REF][4][START_REF] Webb | Critical care services and 2009 H1N1 influenza in Australia and New Zealand[END_REF] populations [START_REF] Kelly | Grant, Interim analysis of pandemic influenza (H1N1) 2009 in Australia: surveillance trends, age of infection and effectiveness of seasonal vaccination[END_REF][4][START_REF] Webb | Critical care services and 2009 H1N1 influenza in Australia and New Zealand[END_REF], whereas fatal outcome can occur at any age. [4] age [4]. The observed "epidemiological shift" to younger ages appears to be contradictory to the current knowledge regarding seasonal influenza that mostly affects younger children and elderly frail individuals.[40] individuals [40]. Of note, a similar "epidemiological shift" to younger ages was observed during previous influenza pandemics. [41] pandemics [41]. A possible explanation may be the fact that elderly individuals that were infected with previous influenza strains that have antigenic similarities with 2009 H1N1v may have already developed a substantial antibody response to this new pandemic influenza virus. However, the considerable percentages of elderly individuals among critically ill and fatal cases observed in the herein reviewed studies imply that elderly individuals do suffer severely from pandemic influenza. This might be due to the presence of serious comorbidity and inadequate immune response mechanisms in elderly individuals.

Pregnant women constituted a considerable percentage among the total of the evaluated pandemic cases reported in the included studies, as well as among the evaluated hospitalized, critically ill ill, and fatal cases. This finding is in accordance with the findings of relevant studies from the Southern Hemisphere. However, pregnancy has already been regarded as a risk factor for severe influenza infection, as well as for an adverse outcome of influenza infection, based on the experience from seasonal influenza.[42, 43] influenza [42,43]. A possible explanation for this may be the alterations to the immune response system, as well as psychological alterations that may occur during pregnancy. Notably, a recent review suggests there is a significant relationship between psychosocial factors and the onset or the progression of acute respiratory tract illness.[44]illness [44].

Obese and morbidly obese patients also constituted a considerable proportion of the evaluated cases with pandemic influenza infection of different severity. Specifically, obese and morbidly obese patients represented considerably high percentages among hospitalized, critically ill ill, and fatal cases. This finding, along with the evidence provided from studies from the Southern Hemisphere, [START_REF] Webb | Critical care services and 2009 H1N1 influenza in Australia and New Zealand[END_REF] Hemisphere [START_REF] Webb | Critical care services and 2009 H1N1 influenza in Australia and New Zealand[END_REF], imply that obesity and morbid obesity may be regarded as a risk factor of severe pandemic influenza infection. Many factors may account for this finding. Some of these are inadequate immune response mechanisms in obese patients and the common presence of serious comorbidity. The role of psychological factors should not be underestimated in this specific subpopulation as well. either. Recent reviews have also suggested that obese and morbid obese individuals are more likely than people with normal body weight to develop infections of various types of infections, as well to develop serious complications of common infections, such as respiratory infections. [45,46] infections [45,46]. The fact that obese patients may suffer more severely from pandemic influenza has considerable implications in regard to the treatment of these patients. Specifically, the adjustment of dosing of anti-influenza agents agents, in particular, for bodyweight body weight, may prove to be a necessity, as was suggested for the treatment with antimicrobial agents from a recent review.[47]review [47].

Our study has limitations that should be taken into consideration before extrapolation of the findings. Firstly, considerable heterogeneity was observed among the included studies regarding the methodology used to identify and present the pandemic cases, the clinical settings, the period in to which the study referred to, referred, as well as the evaluated populations. This hampered our attempts to perform any statistical analysis, and consequently analysis and, consequently, the establishment of firm conclusions based on a pooled evaluation of the extracted data. Moreover, the retrospective nature of the collection and presentation of the data regarding the pandemic cases that was used in the majority of the included studies may also induce a bias. However, the herein reviewed data data, along with the experience gained from countries of the Southern Hemisphere Hemisphere, may aid considerably in the clarification, at least to some extent extent, of some of the epidemiological features of pandemic influenza.

In conclusion, the evaluation of the currently available published literature regarding the experience of the Northern Hemisphere with the pandemic 2009 A(H1N1) influenza suggests that pandemic influenza affects younger patients compared with seasonal influenza. Pandemic influenza seems also seems to be associated with considerable morbidity in the patients that present with serious illness, even in otherwise healthy individuals. Pregnancy and obesity may also be regarded as risk factors for both infection, infection as well as severe pandemic influenza infection.

Funding

Tables: Two satisfied criteria for obesity, of whom 1 one child, who also had asthma, required intensive requiredintensive care. Five of the 23 without height data had weights above the 97th percentile. Two of these 5 five children required intensive care: 1 one child had asthma, asthma and the other had seizures.seizures f Logistic regression analysis, with age as a continuous variable, showed an age-adjusted odds ratio for asthma of 4.38 (95% confidence interval 1.81-10.60); p = 0.00 g Excluding asthma

Table

  1, Table 2, and Table 3, respectively. Four of the 35 included studies referring to Mexico, Mexico covered a time period starting in March and ending in April,

  26].Pregnant women constituted the 4.5%-17.4% 4.5 to 17.4% among the hospitalized cases in 5 five studies that provided relevant data.[2, data[START_REF] Jain | Hospitalized patients with 2009 H1N1 influenza in the United States, April-June April-June 2009[END_REF] 4, 15,[START_REF] Yeung | Presentation and management of critically ill patients with influenza A (H1N1): a UK perspective[END_REF] 21] 21]. Among ICU cases, pregnant women were represented by percentages of 11.5%,[27] 11.5%[27] and 18.2%,[START_REF] Rello | Intensive care adult patients with severe respiratory failure caused by Influenza A (H1N1)v in Spain[END_REF] 18.2%[START_REF] Rello | Intensive care adult patients with severe respiratory failure caused by Influenza A (H1N1)v in Spain[END_REF] in 2 two other studies that provided relevant data. Among fatal cases, pregnant women were represented by percentages ranging from 2.2%-15.8% 2.2 to 15.8% in 3 studies.[33]three studies[33].

Table 1

 1 Data retrieved from studies originating from European countries regarding specific epidemiological features of pandemic influenza in patients with 2009(H1N1) 2009 (H1N1) influenza of different severity ‡a 33. [No authors listed] (2009) Human infection with new influenza A (H1N1) virus: clinical observations from Mexico and other affected countries, May 2009 (2009) 2009. Wkly Epidemiol Rec 84(21):185-18984(21):185-189 34. Chowell G G, Bertozzi SM, Colchero MA, Lopez-Gatell H, Alpuche-Aranda C, Hernandez M et al (2009) Severe respiratory disease concurrent with the circulation of H1N1 influenza. N Nishiura H et al H, Castillo-Chavez C, Safan M, Chowell G (2009) Transmission potential of the new influenza A(H1N1) virus and its age-specificity in Japan. Euro Surveill 14(22)14(22). pii:19227 39. Vaillant L et al L, La Ruche G, Tarantola A, Barboza P (2009) Epidemiology of fatal cases associated with pandemic H1N1 influenza 2009. Euro Surveill 14(33)14(33). pii:19309 40. Thompson WW WW, Shay DK, Weintraub E, Brammer L, Cox N, Anderson LJ et al (2003) Mortality associated with influenza and respiratory syncytial virus in the United States. JAMA 289(2):179-186 41. Simonsen L et al L, Clarke MJ, Schonberger LB, Arden NH, Cox NJ, Fukuda K (1998) Pandemic versus epidemic influenza mortality: a pattern of changing age distribution. J Infect In cases where the denominators were <10, percentages (%) were not displayed at in this table.table §: b The proportion of people with risk factors who were hospitalised hospitalized for influenza was 18.5% (20/108), while the proportion of people without risk factors that who were hospitalised hospitalized for influenza was influenzawas 7% (220/3,162).(220/3,162) In cases where the denominators were <10, percentages (%) were not displayed at in this table.table †: b Includes asthma, chronic obstructive pulmonary disorder, bronchopulmonary dysplasia/respiratory distress syndrome, bronchiolitis obliterans organizing pneumonia, Sjogren syndrome, and Sjögren's syndrome,and obstructive sleep apnea.apnea ∞ : c Includes congenital heart disease, atrial fibrillation, status post-aortic post-aortic valve replacement, congestive heart failure, hypertensive heart disease, and coronary artery.artery *: d Of the 45 deaths from pandemic H1N1 virus infection reported to the CDC from April 15 to June 16, 2009, six (13%) were in from pregnant women, including one death from the original case originalcase series of 34 pregnant cases. #: As a result of increased patient volumes, many experienced delay in admission to the ICU, and 4 remained in the emergency department until death. **: Influenza-like illness. ®: Logistic regression analysis, with age as a continuous variable, showed an age-adjusted odds ratio for asthma of 4.38 (95% confidence interval 1.81-10.60); p = 0.001. ¢: Excluding asthma.cases ∩ : e Data on height and weight were available for 35 of the children with pandemic H1N1 influenza.

	Engl J Med 361(7):674-679
	35. Fajardo-Dolci GE GE, Hernández-Torres F, Santacruz-Varela J, Rodríguez-Suárez J, Lamy P,
	Arboleya-Casanova H et al (2009) Epidemiological profile of mortality due to human influenza
	A (H1N1) in Mexico. Salud Pública Méx 51(5):361-371
	36. Castro-Jimenez MA Castro-Jiménez MA, Castillo-Pabón JO, Rey-Benito GJ, Pulido-
	Domínguez PA, Barbosa-Ramírez J, Velandia-Rodriguez DA et al (2009) Epidemiologic
	analysis of the laboratory-confirmed cases of influenza A(H1N1)v in Colombia. Euro Surveill
	14(30):19284
	37. [No authors listed] (2009) Human infection with new influenza A (H1N1) virus: clinical
	observations from a school-associated outbreak in Kobe, Japan, May 2009 (2009) 2009. Wkly
	Epidemiol Rec 84(24):237-4484(24):237-244
	38. Dis 178(1):53-60
	42. Dodds L L, McNeil SA, Fell DB, Allen VM, Coombs A, Scott J et al (2007) Impact of influenza
	exposure on rates of hospital admissions and physician visits because of respiratory illness
	among pregnant women. CMAJ 176(4):463-468
	43. Schanzer DL, Langley JM, Tam TW (2007) Influenza-attributed hospitalization rates among
	pregnant women in Canada 1994-2000. 1994-2000. J Obstet Gynaecol Can 29(8):622-629
	44. Falagas M et al M, Karamanidou C, Kastoris AC, Karlis G, Rafailidis PI (2009) Psychosocial
	factors and susceptibility/outcome to/of susceptibility to or outcome of acute respiratory tract
	infections (in press). infections. Int J Tuberc Lung Dis14(2):141-148
	45. Falagas ME et al ME, Athanasoulia AP, Peppas G, Karageorgopoulos DE (2009) Effect of body
	mass index on the outcome of infections: a systematic review. Obes Rev 10(3):280-289
	46. Falagas ME, Kompoti M (2006) Obesity and infection. Lancet Infect Dis 6(7):438-446
	47. Falagas ME, DE Karageorgopoulos (2009) DE (2010) Adjustment of dosing of antimicrobial
	agents for bodyweight in adults. Lancet375(9710):248-251
	First author Year[ref]Author(s), year	Country Study population Study period Method	Epidemiological groups	Evaluated	Hospitalized patients
		Country, study

Table 3

 3 Data retrieved from studies originating from countries of the rest of the world (except for the USA and Canada) regarding specific epidemiological features of pandemic ofpandemic influenza in patients with 2009(H1N1) 2009 (H1N1) influenza of different severity ‡a In cases where the denominators were <10, percentages (%) were not displayed at in this table.table ***: b As a result of increased patient volumes, many experienced delay in admission to the ICU, and four remained in the emergency department until death c Among a total of 5,563 confirmed cases.cases

	MexicoAuthor
	(s), year

Table 4

 4 Percentage of different epidemiological groups among cases of pandemic influenza with different severity in studies originating from the Northern Hemisphere ‡a

	withpandemicH1N1
	influenza2009,April-
	July,NR

file://C:\SPS\WMS\repos\ev98\hmi01002\xml\xml\delta\1002_Delta.html NoneNone. file://C:\SPS\WMS\repos\ev98\hmi01002\xml\xml\delta\1002_Delta.html Jain 2009[2]et al. 2009 [2] USA 272 hospitalized cases April-June RT-PCR USA,272hospitalizedcases,Apri June,RT-PCR

\SPS\WMS\repos\ev98\hmi01002\xml\xml\delta\1002_Delta.html

Conflict of interest

NoneNone. population, study period, method of diagnosis Rello et al2009 [START_REF] Rello | Intensive care adult patients with severe respiratory failure caused by Influenza A (H1N1)v in Spain[END_REF]al. 2009 [START_REF] Rello | Intensive care adult patients with severe respiratory failure caused by Influenza A (H1N1)v in Spain[END_REF] Spain Spain, 32