The influence of local environment on the aging and mortality ofAedes aegypti(L.): Case study in Fortaleza-CE, Brazil

It is generally assumed that the daily probability of survival of mosquitoes is independent of age. To test this assumption we have conducted a three-year experimental fieldwork study (2005–2007) at Fortaleza-CE in Brazil, determining daily survival rates of the dengue vector Aedes aegypti (L.). Survival rates of adult Ae. aegypti may be age-dependent and the statistical analysis is a sensitive approach for comparing patterns of mosquito survival. The mosquito survival data were better fit by a Weibull survival function than by the more traditionally used Gompertz or logistic survival functions. Gompertz, Weibull, or logistic survival functions often fit the survival, and the tails of the survival curves usually appear to fall between the values predicted by the three functions. We corroborate that the mortality of Ae. aegypti in semi-natural conditions may no more be considered as a constant phenomenon during the life of adult mosquitoes but varies according to the age and environmental conditions under a tropical climate. This study estimates the variability in the survival rate of Ae. aegypti and environmental factors that are related to such variability. The statistical analysis shows that the fitting ability, concerning the hazard function, was in decreasing order: Seasonal Cox, the three-parameter Gompertz, and the three-parameter Weibull, that was similar to the three-parameter logistic. The advantage of using the Cox model is that it is convenient for exploring the relationship between survival and several explanatory variables. The Cox model has the advantage of preserving the variable in its original quantitative form and of using a maximum of information. The survival analyses indicate that mosquito mortality is both age- and environment-dependent. Dengue fever, together with associated dengue hemorrhagic fever, is the most important vector-borne viral disease affecting humans. Aedes aegypti (L.), the urban yellow fever mosquito, is also the principal dengue-carrying vector. A secondary vector is Aedes albopictus (Skuse). Ae. aegypti (L.) is distributed in the majority of tropical and subtropical cities (Hopp and Foley 2001) with its wild form living in tropical Africa (Diallo et al. 2005). In most urban areas, the vector of the dengue fever virus (DEN) causes dramatic epidemics (Gubler 1997). As there is yet no available vaccine, vector control remains the only means to prevent epidemics, but it is actually very difficult to maintain the mosquito populations at a suitable level, especially when resistance to insecticides occurs (Carvalho et al. 2004, Lima et al. 2003, Luna et al. 2004). Social and environmental factors, including increased urbanization (particularly of poor populations lacking basic health services), as well as expansion of international travel and trade, are linked to the resurgence of dengue disease (Hales and van Panhuis 2005). Climate change also may affect transmission, as dengue mosquitoes reproduce more quickly and bite more frequently at higher temperatures (McMichael 2003, Hales et al. 2002). Dengue currently occurs in nearly 100 tropical and subtropical countries. Epidemics have become progressively larger. In 2002, the disease was responsible for an estimated 19,000 deaths, as well as the loss of 616,000 disability-adjusted life years. The World Health Organization (WHO) currently estimates there may be 50 million dengue infections worldwide every year. Therefore, available scientific knowledge about vector ecology and disease epidemiology may potentially be harnessed to improve environmental management and community action as a means for combating a most dreaded disease. In Brazil, since some older historical reports published when clinical descriptions were available (Pedro 1923), DEN re-emerged with ever growing incidence since the epidemics in Boa Vista-RR in 1982 and Rio de Janeiro-RJ in 1986 (Degallier et al. 1996). In this country, seasonal and interannual variations of DEN epidemics are clearly linked to seasonal and interannual variability of regional climate (Tauil 2002). Yearly peaks of dengue cases are generally noted during and just after the wet season (Degallier et al. 1996). That is the case for Fortaleza-CE, a 3-million-inhabitant city located at the oceanic bordure of the semi-arid region of northeastern Brazil (Figure 1) and where several epidemics occurred over the last decades (Vasconcelos et al. 1989, Vasconcelos et al. 1995). (a) Map of Brazil, (b) map of Ceará State, (c) map of Fortaleza with the areas of experiments: (0) UFC, (1) Montese, (2) Serrinha, (3) São Geraldo, (4) Pio XII, (5) Aldeota, (6) Mucuripe, (7) Iracema (SESACE). Knowledge of the relations between meteorological parameters and mosquito survival/mortality need both field evaluation and regional validation. Here a difficulty arose, as some authors have shown that during the life of the mosquito, the mortality rate does not remain constant, contrary to the generally accepted assumption (Styer et al. 2007, Dawes et al. 2009). These authors showed that younger mosquitoes have a lower mortality rate than old ones and thus may have a higher vectorial capacity. Dengue is found in tropical and sub-tropical climates worldwide, mostly in urban and semi-urban areas. The region of Fortaleza (Figure 1) has a sub-tropical climate, specifically a tropical wet and dry climate, with high temperatures and high relative humidity throughout the year, with a large seasonal-to-interannual variability in the rainfall regime. The wet season is roughly limited to four months, February to May, with more than 60% of the annual total amount, about 960 mm/year on average (Hastenrath and Greishar 1993), with much rainfall particularly in March and April. The weekly DEN case number recorded for the city of Fortaleza remained very low (<100 cases/week) during the first four months of the years 2005 to 2007. This number rapidly grew to 1,000 cases per week in July, 2005 and June, 2006, a few months after the end of the wet season. The year 2007 was somewhat different with an earlier slow increase, including a lower seasonal peak (around 600 cases/week in May-June) and a continuation of a significant number of cases during the second semester (around 150 cases/week). This study is built on data obtained from extensive fieldwork. In this light, it is an important contribution to the literature and can help to improve current understanding of mosquito distribution. Daily mortality is an important determinant of a vector's ability to transmit pathogens. It has long been accepted that mosquito mortality rate varies according to its age, but there is no published work describing the relationship between the survival rate of Ae. aegypti and environmental factors. Although different statistical models, such as exponential, Weibull, and Gompertz models are used in the literature to describe the relationship between mortality rate and age, in this study we apply the proportional hazards model, also called the Cox model, to relate time to death to a number of environmental explanatory variables (covariates) as a robust alternative to the previously developed models. The Cox model has the advantage of preserving the variables in their original quantitative form while using a maximum of information. We tested whether Ae. aegypti do senesce and if there are associations and interactions among some environmental conditions, such as locality, survey surroundings, and dry (average precipitation below 60 mm) and wet (average precipitation above 180 mm) seasons, and the daily mortality of the mosquito. For that, we developed a three-year fieldwork, 2005–2007, in Fortaleza-CE, combining an experimental study with a statistical analysis of survival. The goal of mortality/survival analysis is to gain a deeper understanding of the mechanisms involved in aging according to different local environments and meteorological conditions that modulate mortality rate. Twelve experiments (EXP1 to EXP12) were conducted in Fortaleza during the three years of the study. They were distributed on seven sites (#1 to #7) distanced from each other by 3 to 10 km (Figure 1c and Table 1). Site #1 to Site #6 were operated in private or institutional locations with a natural air environment, while Site #7 was operated inside a closed air-conditioned room at the Ceará State Secretary of Health (SESACE). A programmable automatic recorder for air temperature and relative humidity sensor was located near a small cage with about 50 Ae. aegypti mosquitoes (see details below). Six experiments were conducted during the wet season: EXP1, EXP2, EXP6, EXP7, EXP10, EXP11 and the six others during the dry season: EXP3, EXP4, EXP5, EXP8, EXP9, EXP12 during the years 2005–2007. For Sites #1 to #6, submitted to natural air conditions, significant seasonal differences were noted between the experiments operated during the dry seasons and the experiments operated during the wet seasons. The daily variations recorded a higher variability for averages and daily ranges during the wet seasons. However, the climate parameters were quite stable during the dry seasons. Independently of a specific season, the daily variations of the three variables were somewhat different among the sites. For instance, they were systematically higher for Site #2 than for Site #1. In order to observe mosquito mortality under conditions which may be as close as possible to natural ones, 40–50 mosquitoes (35–45 females and about five males) were released in netted wooden cages (30×30×30 cm). The mosquitoes were drawn from their rearing cage two days after hatching. Just before release in the experimental cages, the females were blood fed for 2 h on anaesthetized quail. In the same day of blood feeding, the cages were installed in some of the experimental sites (Figure 1c). Ten percent sugar-imbibed cotton plugs were changed and dead mosquitoes were counted daily. A small tube with filter paper and tap water was renewed daily in each cage to collect the eggs (not studied here). Most of the twelve experiments lasted until the death of the last mosquito in the last cage. Some additional information about local conditions, the proportional contributions of age, season, and sites, are presented in Figure 2 and Table 1. Figure 2a shows that 2/3 of the data were obtained during the dry season and that there were no data from the wet season in the third year (2007). In Figure 2b, it appears that the three years were evenly represented in five sites, but only Sites #1, #2, and #7 were equally represented in the data. In these same sites, we obtained more data during the dry season than during the wet season: Sites #4, #5, and #6 were sampled only during one season (Figure 2c). Figure 3 shows the proportion of relevant data obtained in each environmental condition, even with the experimental difficulties encountered in Sites #4, #6, and #7. All experiments were conducted only in Sites #1, #2, and #7. The proportional conribution of the (a) season and year, (b) site and year, and (c) site and season effects to the mosquito mortality experiment. The proportional contribution of the site and eperimentation (EXP) effects to the mosquito mortality experiment. According to the experimental design, we quantified the influence of two local environmental factors on mosquito mortality: season (dry or wet) and site. Since these environmental factors are assumed not to change during each experiment, their influence can be measured straightforwardly by comparing the average lifetime obtained in each instance of each factor. As the number of instances and resulting sample sizes used for this comparison vary and are relatively small, it was clearly necessary to cautiously assess the statistical significance of obtained differences. To perform the latter, we used p-values of the classical Kruskal-Wallis (KW) test (Corder and Foreman 2009). The modeling of survival data centers on the hazard function (the instantaneous death rate), which is used to express the risk or hazard of death at time t. Mortality rates and survival are related to one another by the equation ds/dt=–ms, describing change on time, where s is the fraction of a population surviving and m is the mortality rate. This equation is used to determine the survival function corresponding to a mortality function. The survival function, conventionally denoted by S, is defined as S(t)=1-F(t), where F(t)=P(T≤t) is the cumulate distribution function (cdf), with T the random variable denoting the time of death. The survival function must be non-increasing. This reflects the notion that survival to a later age is only possible if all younger are The survival function is usually assumed to approach as age The hazard function as hazard rate), is defined as the rate at time on survival until time or the hazard function can be defined as where is the probability function and is the survival function of it is convenient to model survival the hazard function or the hazard function. Some of the most hazard functions in survival analysis were used to the mosquito These a constant death the Gompertz function (the rate of mortality with age in such a that its is proportional to the Weibull function (the rate of mortality or with age on the values of a and a and the the Cox model statistical for exploring the relationship between survival and several explanatory In the Gompertz model, one that the of mortality after a age. In this model, the hazard rate is by the where the is the or the rate of during and is proportional to the relative when the mortality with age. The is the mortality with to the is the relative rate at the is with t. that since the Gompertz model is for a mortality one can it to the survival function. that is a function of This a in time approach which may be The Gompertz survival function thus to mortality rate with The three-parameter Weibull hazard function mortality is where is the is the and is the the hazard function is the mortality rate is and when the mortality rate is In the three-parameter model, the hazard rate for a specific age is by the where the is the the is the mortality for to and the is the relative rate at the In all these hazard rate models one can a constant an mortality models, Survival data is to age-dependent models for mosquitoes (Styer et al. 2007, Dawes et al. and some are better by a logistic survival function than by the more traditionally used Gompertz or Weibull functions we experimental survival data to three mortality models, the Gompertz hazard function, Weibull hazard function, and logistic hazard function, the a case of the Cox the of hazard we estimated an hazard function, with a constant hazard in the case of an distribution of survival with a hazard in the case of the Gompertz distribution of survival with to the Weibull distribution of survival with and The Gompertz survival function to mortality rate with The Weibull survival function to mortality rates that as a function of A three-parameter logistic mortality function reflects mortality rates that and become constant with age. The of using three models is to the of hazard rate with age. We thus the fit obtained with a constant hazard rate to the fit obtained with two hazard functions that in to model mosquito Therefore, to be with survival analysis models, we the basic model to and It is to that Gompertz and Weibull functions causes of aging the describing mortality can from an in the of to causes in the Gompertz model and the of causes at older in the Weibull They thus by Cox the proportional hazards model was developed to the effects of different Cox proportional hazard the analysis of the of several risk factors on survival. The proportional hazard model is the most of the models it is not on concerning the or of the survival distribution. The model that the hazard rate is a function of the independent variables no are about the or of the hazard function. model may be considered to be a According to this model, the hazard rate of an is not only by its but also by the under which it for instance, a of different factors such as air relative air and local This function or by an order of with in each As in the or Weibull models, the hazards remain proportional Cox and This model has a number of and For instance, under a model assumption for and about the life distribution model, it is possible to experimental data and maximum of the twelve experiments from 2005 to 2007 lasted until the death of the last mosquito. the appears it does not prevent difficulties are according to five which appear in Table and are in its associated Most of the occurred Sites to #7. Site #1 and Site #2 were generally from these using only Sites #1 and #2, the total average of the first (the was operated with is days with an average during the dry season a than during the wet season The is when comparing the averages of the first experiments (dry and seasons for Site #1 or for Site #2 the mortality of the mosquitoes higher when the daily of conditions is also as However, that by the The total average of the for Site to be a seasonal site where the air was operated during the is very close to the average for Site #1 and Site #2 days the experiment, the only one with mosquitoes in the cages, does not for about a significant between the conditions of the In of all these we the experimental data analyses according to statistical the of which are in the the of the Kruskal-Wallis we at a significance of the assumption for the two environmental factors and at one has a significant influence on mosquito significant year was but this analysis was only we that for 2007 the mortality was below in 2005 and the mortality was above However, there is some between season and year The 2007 has the below average be that the experimental survey was only in the dry season, to the wet season during this year. one the season one that the wet season has a influence on lifetime and on survival (Figure lifetime is increased by during the wet season as to the dry season. This influence is but also year by year and site by site (Figure Seasonal varies from year to year, and the during the wet season was much in 2005 than it was in Sites also influence lifetime (Figure The site effects vary somewhat with year and season but are However, it is not which in the sites such Figure this the three and Iracema Site #1 shows a significant between mortality during the dry and wet seasons (Figure but there were no seasonal effects concerning the two other sites. The and the Kruskal-Wallis for (a) year, (b) season, and (c) The and the Kruskal-Wallis for three locations by season. A concerning the survival analysis two or more is there a between the survival we have a test available in the It if there is a between two or more survival curves using the of and where the that there is no between or the to EXP11 that the in Figure of the hazard function by the of the of the survival function to the there is a statistical significant between the and the seasons on the mosquito experimental sites. The seasonal hazard functions for three The the season and the the dry season. The survival time average was days for Site #1 and days for Site #2, with a survival average during the dry season of about days for Site #1 and for Site #2, than the wet season: days for Site #1 and days for Site the Kruskal-Wallis test we that for Site #2 and Site #7 there was no seasonal the for Site #1, there was a significant between the seasons on the mosquito We that the total average of the related to the for Site #7 throughout the dry season of 2007, where the air was operated during the day time, is very close to the average for Sites #1 and it difficult to that the daily conditions have a significant influence on mosquito It is to observe that for the during the dry season of 2007 with a number of there was a significant between the conditions at Iracema and For the to EXP11 at Iracema and the estimated hazard function showed a (Figure during most of the a for old This is with the of et al. (2007). To assess the significance of the hazard #1 and Site a #7) one observe that the parameters of the Gompertz model are the is very to be an of with of et al. (2007). To assess the significance of the a constant of hazard one that of the and Weibull models that the is (Figure the is also to be by and is to be a of mosquito life This is also with the of et al. (2007). A in or is to determine whether or not variables are with survival one can the of proportional hazards of the Cox proportional model (Figure for where mortality was more by only the seasons have significant at a significance The in the second of the first and in the first of the second of the are as effects on the as we the case of the other an additional wet season the daily mosquito survival by a of about on or The and at the of the are of the that all of the are In this instance, the test are in close and the is The estimated Cox proportional hazard functions for seasonal at The is survival mortality is an important of vectorial especially in the case of for which no is and thus the of the remains the only means of et al. mortality of adult mosquitoes may be to or factors control The work showed that mortality of Ae. aegypti in semi-natural conditions may no more be considered a constant phenomenon during the life of adult It varies according to the age of the mosquitoes and to local environment, even in tropical Most authors have considered that only the distribution of the survival may be and that tropical mosquito mortality rates are independent of age et al. et al. et al. 1995). However, for a more of survival time other are The more frequently used are the Gompertz or the Weibull used distribution in survival time analysis is the distribution and but this distribution does not have the proportional hazards the only the exponential, the Weibull, and the Gompertz models the assumption of proportional hazards with the Cox proportional hazard function and 1997). statistical analysis showed that the fitting ability was in the order: seasonal Cox proportional hazard function three-parameter Gompertz three-parameter Weibull three-parameter logistic. these in models that better of of epidemics (Degallier et al. In the age-dependent and mortality on mosquito population models have been developed to determine more a for risk in a This work was of the of the and on the from a by of the in the for the was by a from We all the and from and often with but with in the and of the