 Research
 Open Access
 Published:
Temporal and microspatial heterogeneity in the distribution of Anopheles vectors of malaria along the Kenyan coast
Parasites & Vectors volume 6, Article number: 311 (2013)
Abstract
Background
The distribution of anopheline mosquitoes is determined by temporally dynamic environmental and humanassociated variables, operating over a range of spatial scales. Macrospatial shortterm trends are driven predominantly by prior (lagged) seasonal changes in climate, which regulate the abundance of suitable aquatic larval habitats. Microspatial distribution is determined by the location of these habitats, proximity and abundance of available human bloodmeals and prevailing microclimatic conditions. The challenge of analysing—in a single coherent statistical framework—the lagged and distributed effect of seasonal climate changes simultaneously with the effects of an underlying hierarchy of spatial factors has hitherto not been addressed.
Methods
Data on Anopheles gambiae sensu stricto and A. funestus collected from households in Kilifi district, Kenya, were analysed using polynomial distributed lag generalized linear mixed models (PDL GLMMs).
Results
Anopheline density was positively and significantly associated with amount of rainfall between 4 to 47 days, negatively and significantly associated with maximum daily temperature between 5 and 35 days, and positively and significantly associated with maximum daily temperature between 29 and 48 days in the past (depending on Anopheles species). Multipleoccupancy households harboured greater mosquito numbers than singleoccupancy households. A significant degree of mosquito clustering within households was identified.
Conclusions
The PDL GLMMs developed here represent a generalizable framework for analysing hierarchicallystructured data in combination with explanatory variables which elicit lagged effects. The framework is a valuable tool for facilitating detailed understanding of determinants of the spatiotemporal distribution of Anopheles. Such understanding facilitates delivery of targeted, costeffective and, in certain circumstances, preventative antivectorial interventions against malaria.
Background
The density of Anopheles mosquito vectors relative to human hosts (the vectortohost ratio) is a critical component of the intensity of malaria transmission [1]. Spatial and temporal heterogeneity in the density and distribution of anopheline mosquitoes is determined principally by the availability of aquatic habitats suitable for the maturation and development of their larvae [2, 3]. Climate is a major determinant of macrospatial (district, regional or country) and temporal heterogeneity in the distribution of such habitats and consequently of heterogeneity in the distribution of mosquito vectors [4–8]. In turn, climatic variation drives heterogeneity in the intensity of malaria transmission within and among human populations, both in the short (seasonal) [9] and long (climate change) terms [10]. The fine, microspatial distribution of vectors within communities depends on the location of aquatic habitats [11, 12], the proximity of human bloodmeals [13, 14] (Anopheles gambiae sensu stricto and A. funestus mosquitoes are strongly anthropophagic [15]) and microclimatic conditions, particularly the direction of the prevailing wind [16] that transports olfactory cues from humans to female mosquitoes [17, 18].
There is great interest in identifying factors underlying the macro and microspatial and temporal distribution of anopheline vectors either mechanistically using mathematical models (e.g. [19–21]), or phenomenologically using statistical models (e.g. [22–24]). Studies are often motivated by projecting the effects of climate change on the distribution of anophelines [25–27] and endemic malaria [28]; improving targeted control on macro [29, 30] and microspatial [16, 31] scales, or developing models with capacity to offer reliable predictions of impending malaria epidemics [32]. The latter ‘early warning’ prediction models are underpinned by the lagged relationship between meteorological variables, vector density and malaria incidence [33]. For example, depending on temperature, it takes between 6 and 47 days for mosquito larvae to develop into adults; between 4 and 111 days for the completion of sporogony within a mosquito following ingestion of an infected bloodmeal (infected mosquitoes are not infectious before completion of sporogony), and a further 10 to 16day incubation period before an infected human develops symptoms of malaria (see Table 1 in [34]). Thus, increased anopheline densities [35, 36], and cases of malaria [34], are associated with prior increases in rainfall [37].
Despite an extensive literature, there remains a lack of general consensus on the relative importance and predictive capacity of different meteorological factors [35]. This is partly due to the biological and ecological complexities that underpin the associations. For example, the association between increased rainfall and increased vector abundance would appear ostensibly simple, being mediated by more numerous and larger aquatic habitats. However, the persistence of such habitats following rain also depends on rates of evaporation, which are themselves driven by a myriad of factors, including temperature, atmospheric pressure, windspeed, humidity and the surface area of the specific habitat [19]. Furthermore, temperaturedependent rates of larval development [38–40] ensure that the lag between increased rainfall and increased abundance of mosquitoes will vary with temperature; at an average temperature of 16°C larvae become adults in an average of 47 days, while at 30°C it takes an average of 10 days (see Table 1 in [34] and Figure 1 in [41]). Moreover, when inference is made using data on malaria incidence, temperaturedependent rates of sporogony and the incubation period in the human, add an additional and uncertain lag time.
Methodological issues related to the analysis of these complex associations are also likely to contribute to somewhat conflicting conclusions on the relative importance of different meteorological variables. The most common analytical approach has been to calculate pairwise correlations between malaria incidence and variables such as daily rainfall (e.g. [42]), temperature (e.g. [43]) and humidity (e.g. [44]) at different numbers of days in the past (lags). This univariate approach is not conducive to multivariate adaptation, making it prone to confounding by inevitably (highly) correlated meteorological variables. Furthermore, the method does not quantify the overall statistical significance nor the explanatory power of variables considered at different lags.
Polynomial distributed lag (PDL) models [45] provide a solution to these analytical difficulties, permitting the association of multiple explanatory (meteorological) variables over a continuum of lags to be estimated as part of a single, coherent model structure. Such models have been used previously to estimate the association of rainfall, and daily temperature with weekly incidence of malaria in Ethiopia distributed over a 10week lag period [34]. The PDL framework can be applied to explanatory variables in a generalized linear model (GLM) [46]. The resulting PDL GLM is adaptable to hierarchical data structures, becoming a polynomial distributed lag generalized linear mixed model (PDL GLMM, Table 2) [47, 48]. Such frameworks permit modelling of explanatory variables acting on a variety of scales; from climatic (meteorological) data acting at the macrospatial scale, to temporallydependent factors acting below the level of the (longitudinally sampled) units of observation. This potential of the PDL GLMM framework has not been applied to the analysis of data on vector abundance, and consequently little is known about the relative importance and predictive capacity of variables acting on multiple hierarchical scales.
In this paper, mixed (containing random effects) PDL models (Table 2) are used to analyse longitudinal data on the abundance of Anopheles gambiae and A. funestus, collected between 2000 and 2002 from households located in Kilifi district, adjoining the Kenyan coast, an area endemic for falciparum malaria. Research on anopheline mosquitoes in Kilifi, ongoing since the early 1990s [23, 49–54], has identified the importance of environmental heterogeneity in affecting the abundance and productivity of mosquito larval habitats [55], the distribution of adult mosquitoes, and the incidence of malaria due to Plasmodium falciparum[23, 56], the latter having declined in recent years [57, 58].
The PDL GLMM models presented here incorporate the (lagged) meteorological covariates of daily rainfall and maximum temperature, and the householdlevel covariates of distance from the nearest aquatic larval habitat and occupancy (number of people in the household) with the aim of: a) exemplifying the flexibility and power of the PDL GLMM framework to analyse longitudinal, hierarchical and overdispersed count data; b) identifying factors underlying the observed spatial and temporal patterns as a source of baseline information prior to the recent decline in malaria transmission and vector density in Kilifi [59], and c) providing information which may influence the design and application of targeted, costeffective and preventative antivectorial interventions for malaria control.
Methods
Study area, household sampling, and ethical considerations
The study was conducted in the villages of Jaribuni (39°44′E 3°37′S) (site 1), Majajani (39°74′E 3°40′S) (site 2), and Mtepeni (39°45′E 3°53′S) (site 3) in Kilifi District, along the Kenyan coast over the period from May 2000 through April 2002 (Figure 1A). Random samples of approximately 30% of all households in each community were selected for potential inclusion in the study. Written informed consent was sought from the heads of households to permit mosquito collection after the study was explained in their local language. Households that did not consent did not participate further. The initial study design was to perform mosquito catches in each participating household fortnightly throughout the twoyear study period. However, due to householders sometimes being absent and, more commonly, requesting additional catches, the mean number of catches performed per household varied between 41 and 66 (Table 1). Most households were visited and sampled on multiple occasions over the study, but not more than once per week. The maximum number of visits to a household was 96, the minimum was 1 (Table 1). The study was reviewed and approved by the Institutional Review Board of the Kenya Medical Research Institute (KEMRI), Nairobi, Kenya.
Entomological data
Anopheles mosquitoes were sampled using the pyrethrum spray catch (PSC) method [60]. Mosquitoes from each household were held separately in labelled paper cups, stored in a cool box, and transported to the laboratory. In the laboratory, all mosquitoes were immediately transferred to a 20°C freezer for approximately 10 minutes. Each mosquito sample was allocated a unique identification number and separated by morphospecies into A. gambiae and A. funestus using the morphological criteria of Gillies and Coetzee [61]. The legs and wings of each Anopheles female were then detached from the rest of the body and stored dry on silica gel in labelled vials before being processed for molecular differentiation of the A. gambiae and A. funestus sibling species using the methods of Scott et al.[62] and Koekemoer et al. [63]. Only A. gambiae sensu stricto (s.s.) (henceforth referred to simply as A. gambiae) was identified among the A. gambiae s.l. samples (i.e., these contained no A. arabiensis or A. merus, the other members of the gambiae complex that have been reported along the Kenyan coast [23]). Likewise, A. funestus was found to comprise A. funestus s.s. and is henceforth referred to as A. funestus.
Household covariates
All households participating in mosquito sampling were mapped using a hand held global positioning system (GPS) device (Garmin International Inc., Olathe, KS USA). Larval habitats within a 1 km distance from the study households were also mapped (Figures 1B, 1C, 1D). Samples of mosquito larvae were collected from each habitat and were morphologically identified as anophelines (as opposed to other culicines). Larvae were not disaggregated by Anopheles species (complexes). Distance from the larval habitats to the nearest household was calculated using R [64]. In addition, the number of individuals sleeping in the household the night before mosquito sampling was recorded on field data forms during mosquito sampling. Note that the number of individuals sleeping in a household changed over time and so was included at the observationlevel (rather than the householdlevel) in the statistical model.
Meteorological data
Daily meteorological data on the rainfall and minimum and maximum temperatures were obtained from the national weather station located at the Kilifi Institute of Agriculture (3°37′15″S 39°50′45″E) within the town of Kilifi (Figure 1A). The weather station lies 10.8, 11.5 and 34.1 km to the north east of Jaribuni, Majajani and Mtepeni respectively. This weather station serves the whole of Kilifi district and represents the most accurate source of data available on the climate in the three study communities. For the fewer than 1% of days that data were missing, values were imputed by linearly interpolating to the midpoint between the preceding and following days for which data were recorded. There were no instances of missing data on more than one consecutive day. It was assumed that the meteorological data represent accurately the climatic conditions in the surrounding study sites.
Statistical framework
Data on the number of A. gambiae or A. funestus were modelled separately using a generalized linear mixed model (GLMM). Let Y_{ ij } denote observation j (number of mosquitoes) collected from household i. It was assumed that Y_{ ij } was Poisson distributed with mean and variance, μ_{ ij }, given by the following loglinear mixed effects model,
Here x_{ ij } and z_{ i } are observationlevel and householdlevel vectors of covariates respectively. The covariate vector x_{ ij } comprises the number of people sleeping in the household the night before day t_{ ij } after the start of the study, and the daily rainfall and temperature lagged from p_{min} to p_{max} days into the past. For example, if p_{min} = 5 and p_{max} = 10, x_{ ij } would include the daily rainfall and temperatures 5–10 days prior to day t_{ ij }. Day t_{ ij } is also included as a continuous covariate in x_{ ij }. The inclusion of t_{ ij } accounts for any systematic (nonseasonal) linear changes in the density of the mosquito population over the study period. Furthermore, this relationship was permitted to vary among sites by including an appropriate interaction term. The householdlevel covariate vector, z_{ i }, comprises the distance of the household from the nearest mosquito larval habitat, and the location (site) of the household (Jaribuni, Majajani or Mtepeni). In the loglinear model the coefficients measure the multiplicative effect on the mean number of mosquitoes per household of the covariate in question.
The error term ϵ_{ i } in Eqn. (1) is a householdlevel, normally distributed random effects term that accounts for the possibility that mosquitoes cluster within households. On the natural logarithmic scale, the magnitude of this clustering is quantified by the variance {\delta}_{\mathrm{H}}^{2}. Converting to the scale of counts (as opposed to log counts), the clustering is quantified by {\sigma}_{\mathrm{H}}^{2}=\mathit{\text{exp}}\left(2{\delta}_{\mathrm{H}}^{2}\right)\u2013\mathit{\text{exp}}\left({\delta}_{\mathrm{H}}^{2}\right). Household clustering causes extraPoisson variation (overdispersion) in the numbers of mosquitoes per household. It was particularly important to account for household clustering in this analysis due to the potential for heavily infested households to have been sampled somewhat disproportionately (some householders received by request additional unplanned mosquito catches, see Methods, Study area, household sampling, and ethical considerations). The residual overdispersion that is not due to household clustering is accounted for by the random effects term e_{ ij } in Eqn. (1); this parameter is normally distributed, independent of ϵ_{ i } and specific to each observation. That is, it is an observationlevel random effect. On the natural logarithmic scale, the residual overdispersion is quantified by {\delta}_{\mathrm{O}}^{2}. On the original scale of the counts, it is quantified by {\sigma}_{\mathrm{O}}^{2}=\mathit{\text{exp}}\left(2{\delta}_{\mathrm{O}}^{2}\right)\u2013\mathit{\text{exp}}\left({\delta}_{\mathrm{O}}^{2}\right). Modelling residual (within household) overdispersion using an observationlevel random effect [65, 66] has two principal advantages over alternative methods such as negative binomial or quasiPoisson regression. First, the model is maintained as a GLMM, permitting likelihoodbased fitting and coefficient estimation within a welldeveloped framework [47, 48]. Second, overdispersion is modelled in a hierarchical manner (observations nested within households) permitting direct comparison of the relative contribution of random variation at observation and household levels (viz. comparison of {\sigma}_{\mathrm{H}}^{2} and {\sigma}_{\mathrm{O}}^{2}).
Polynomial distributed lag model
In the model described by Eqn. (1), the number of coefficients to estimate for a particular meteorological covariate is equal to the number of lags considered. For example, if daily rainfall and temperature were lagged from 5 to 15 days into the past (p_{min} = 5, p_{max} = 15), it would be necessary to estimate 10 coefficients for each meteorological covariate. Coefficients estimated using this socalled unconstrained lag structure are unstable because of collinearity among different lags of the same variable [45]. A solution to this is to constrain the coefficients by forcing them to take the shape of a function of the lag. The Almon lag model [67] uses an n^{th} degree polynomial as this functional form,
Here β_{ l } represents the coefficient of a variable at lag l. That is, β_{ l } is a coefficient of either rainfall, or maximum temperature l days in the past. Parameter θ_{ k } is the k^{th} coefficient of the n^{th} degree polynomial. Thus, the number of estimated coefficients (the θ_{ k }s) associated with each lagged meteorological covariate is reduced from the number of lag days to the order of the polynomial, n, giving rise to the PDL.
Model fitting
The model was fitted to the data using GLMM techniques [47, 48] implemented using the lme4 package for R [64]. Following Teklehaimanot et al.[34], the polynomial describing the relationship between the number of lag days and the coefficients of rainfall and temperature was set to order 4 (n = 4). Preliminary analyses indicated that this order polynomial gave ample flexibility to capture variously shaped relationships. The range of lags considered was motivated by mosquito biology. The time from oviposition of eggs in an aquatic habitat to the development of an adult mosquito depends on temperature, varying between about 7 days (1 week) at 40°C to 47 days (approximately 7 weeks) at 16°C [34, 68, 69].
Results
Descriptive statistics
Table 1 gives descriptive statistics of the three study sites; the villages of Jaribuni, Majajani and Mtepeni in Kilifi district, Kenya (Figure 1). Majajani was the most sampled village; 948 mosquito catches were performed in 23 households. The least sampled village was Jaribuni, with 786 catches undertaken in 12 households. The density of Anopheles mosquitoes was highest in Jaribuni; the mean number of A. gambiae and A. funestus per household was 3.1 and 6.6 respectively. Overall, Table 1 indicates that there was considerable heterogeneity in the mean Anopheles densities among the three sites. In particular, the mean density of A. funestus was thirteenfold and fifteenfold larger in Jaribuni compared with Majajani and Mtepeni respectively. The distances of households from the nearest mosquito larval habitats were also heterogeneous among sites. For instance, the average distance of a household from a larval habitat in Mtepeni was approximately half that of the corresponding mean distance in Jaribuni. The average number of people sleeping in a household the night prior to mosquito sampling was broadly similar among sites (Table 1).
Fitted model
Figure 2 depicts the observed and modelpredicted mean Anopheles densities per household in the three study sites. In general, the model fits the observed A. gambiae data well; the modelpredicted mean falls within the 95% confidence intervals of the majority of the observed data across all sites. Similarly, the model also fits well to the observed A. funestus data from Majajani and Mtepeni. The least good fit is to the data on the household densities of A. funestus from Jaribuni, where the mean density was particularly high compared with Majajani and Mtepeni (Table 1).
Meteorological covariates
Rainfall and maximum temperature from 7 weeks before the start of the study period, in April 2000, to its end in April 2002 are depicted in Figure 3. This timeframe is in accordance with the 7week range of lags considered for these meteorological covariates in the statistical model. The data indicate two pronounced rainy seasons, peaking around May or June. Temperatures tend to be highest between, approximately, November and May, and lowest between, approximately, June and October. It is also clear from Figure 3 that minimum and maximum daily temperature are strongly positively correlated (Pearson’s correlation coefficient = 0.77). This produced problems of multicollinearity [70] when both variables were included in the statistical model. Preliminary analysis indicated that maximum daily temperature was a better predictor of mosquito density than minimum daily temperature and so only the latter was included in the final model.
The strength and statistical significance of the association between the meteorological data (daily rainfall and maximum temperature) and Anopheles density is shown by the results of an analysis of deviance given in Table 3. These results indicate that daily rainfall and maximum temperature are strongly and statistically significantly associated with the density of A. gambiae and A. funestus.
The structure of the association between the meteorological covariates and mosquito density over the 7week lag is depicted in Figure 4. A statistically significant association is suggested at lags (days in the past) where the confidence interval for the multiplicative effect on mean mosquito density does not include 1. Bearing this in mind, rainfall between 4 and 47 days lag is positively (an increase in rainfall leads to an increase in mosquito density) and statistically significantly associated with the density of A. gambiae (4A). For the density of A. funestus, the association is also positive and statistically significant between 4 and 27 days lag (4C). Maximum temperature is negatively and statistically significantly associated with the density of A. gambiae between 5 and 24 days lag (4B), and with A. funestus density between 17 and 35 days lag (4D). A statistically significant positive association between maximum temperature and A. gambiae density occurs between 29 and 47 days in the past, and marginally between 45 and 48 days for the density of A. funestus.
Household covariates
The results in Table 3 indicate that the number of people sleeping in the household during the night prior to sampling—hereafter referred to as household occupancy—is strongly and statistically significantly associated with the density of A. funestus but not significantly associated with the density of A. gambiae. Figure 5 shows that the density of A funestus increases sharply from household occupancies of 1 to 2, thereafter remaining relatively constant. The relationship between household occupancy and the density of A gambiae is more linear (and positive), albeit not statistically significant (Table 3 and Figure 5A).
Overall, the distance of the household from the nearest mosquito larval habitat is not a statistically significantly predictor of the density of either Anopheles species (Table 3 and Figure 6). However, the density of A. funestus in households situated over 600 metres from the nearest larval habitat is statistically significantly lower (Pvalue = 0.0032) than that in households located in the baseline distance category of 101–200 metres for the nearest larval habitat (Figure 6B).
Household clustering and overdispersion
The total amount of extraPoisson variation (overdispersion) in the observed numbers of mosquitoes per household is quantified by the sum of the variance terms, {\sigma}_{\mathrm{H}}^{2} and {\sigma}_{\mathrm{O}}^{2}. Parameter {\sigma}_{\mathrm{H}}^{2} quantifies the amount of overdispersion induced by the clustering of mosquitoes within households. Parameter {\sigma}_{\mathrm{O}}^{2} quantifies the residual overdispersion having accounted for that induced by household clustering. Thus, from the estimates of {\sigma}_{\mathrm{H}}^{2} and {\sigma}_{\mathrm{O}}^{2} given in Table 4, it can be seen that 0.55% [0.4/(72 + 0.4)] and 0.06% [0.22/(380 + 0.22)] of the total overdispersion in, respectively, A gambiae and A. funestus numbers per household was accounted for by household clustering. Despite this low contribution to the overall degree of overdispersion, household clustering for both Anopheles species is statistically significant (Table 4).
Discussion
The mixed PDL models, or PDL GLMMs (Table 2), applied in this analysis have been used to elucidate factors influencing microspatial and temporal heterogeneity in the distribution of anopheline vectors of malaria caught within households in Kilifi district, Kenya. Temporal variation in the abundance of A. gambiae and A. funestus is driven by seasonal changes in climate, specifically the amount of rainfall from 4 to 47 days in the past (depending on Anopheles species), and maximum daily temperature. The PDL component of the statistical framework has enabled the distributed shape of these relationships to be elucidated for the first time. Microspatial variation is related to the distance of households from the nearest anopheline larval habitat, and whether such households have single or multipleoccupancy by people pernoctating in them on the night prior to sampling. Despite accounting for these explanatory variables, the abundance of mosquitoes is highly overdispersed and clustered among households, effects which were quantified by the GLMM component of the modelling framework.
Strong empirical associations between anopheline abundance and seasonal variations in rainfall and temperature have been demonstrated previously [5, 23, 72], as have differences in these associations among species [73], although not using a PDL GLMM approach. The PDL models used here permitted a detailed description of the temporally distributed (lagged) nature of these associations which have not been previously elucidated (although the distributed lagged relationship between meteorological variables and cases of clinical malaria have been investigated in a similar fashion [34]). For example, for A. gambiae, rainfall was positively associated with mosquito abundance between 4 and 47 days in the past, with a peak in magnitude at approximately 11 days, and for A. funestus, between 4 and 27 days, with a peak at approximately 14 days. Rainfall acts principally to create and maintain viable larval habitats [74], but the exact shapes of these associations (Figure 4A for A. gambiae, Figure 4C for A. funestus) are likely to be the products of a multitude of interacting factors such as the local topology, cumulative effects of rainfall and heterogeneity in mosquito biology, the type, size and shape of larval habitats, in addition to other meteorological variables such as temperature, humidity and evaporation rates.
The distributed association of anopheline abundance with maximum daily temperature is more complex (Figure 4B for A. gambiae, Figure 4D for A. funestus); a negative association is indicated at short to medium lags—as observed previously in this area [72]—while a positive association arises at longer lag times. The effect of temperature on mosquito abundance is well documented for the extremes of the species tolerance levels [75, 76]. This study sits within these extremes, where the mechanisms by which lagged temperatures may affect mosquito abundance are many and more nuanced, complicating interpretation of statistical (phenomenological) associations. High temperatures may reduce the size and abundance of larval habitats, not only affecting opportunities for breeding, but also altering local ecology, affecting regulatory factors such as the severity of densitydependent competition, the concentration of resources, and water quality among others. Furthermore, temperature influences the development time of anopheline aquatic stages [38–40], possibly interacting with the length of lags for other meteorological variables, such as rainfall. Temperature may also affect adult survival [77, 78] and behaviour [79], the latter potentially altering the chance of an individual mosquito being trapped.
Among the microspatial determinants of household anopheline abundance, distance to the nearest suitable larval habitat was not, overall, statistically significantly associated with either species (although the density of A. funestus was statistically significantly lower for households located over 600 m from a larval habitat compared with households situated closer to larval habits, see Figure 6). Previous studies have shown that distance to larval habitat is negatively associated with anopheline density [80, 81]. The lack of association in this analysis probably reflects, at least in part, a limitation of the GPS data, which were only recorded to an accuracy of approximately 31 metres. A further limitation is that the statistical framework does not include explicit spatial structure. That is, the distribution of larval habitats around individual households could not be considered. This led to the implicit modelling assumption that the dominant source of anophelines was the larval habitat nearest to a household. In principle, the PDL GLMM framework could be incorporated into spatial statistical models. This would permit more powerful interrogation of georeferenced data and improve the capacity of existing spatial frameworks to capture accurately the effects of meteorological variables.
The intensity of humanderived sensory cues, such as carbon dioxide [82], probably underlies the association between the number of household inhabitants the night prior to mosquito sampling (household occupancy) and the number of anophelines caught; more inhabitants generate more intense cues, attracting more mosquitoes. However, the observed strength of this association may be diluted somewhat because houses inhabited by more people tended to be larger (although most houses comprise 2–3 rooms only), catches were made in a single room, and mosquitoes presumably distribute, at least to a certain extent, throughout the house. Moreover, the association may be confounded by the number of occupants in nearby households, which serve to make clusters of highoccupancy households more attractive than lowoccupancy household clusters [31]. These factors may explain the lack of a statistically significant association with A. gambiae, a result which is surprising in the context of the highly anthropophilic, anthropophagic and endophagous nature of this species. A lack of association has been observed between the number of inhabitants in a house and malaria prevalence in the region [83].
The speciesspecific heterogeneity in abundance among the villages is consistent with previous studies in this area [72]. In particular, the markedly greater abundance of A. funestus in Jaribuni is attributable to local ecological heterogeneities [23]. Jaribuni lies next to a permanent river (Figure 1B), which provides plentiful and persistent larval habitats. Such habitats are more favourable to A. funestus which tolerate larger, more permanent larval habitats, whereas A. gambiae prefers more localised and transient fresh water habitats [2, 84].
Identifying factors driving microspatial and temporal heterogeneity in the distribution of anopheline mosquitoes facilitates understanding of the causal pathway which drives the coupled dynamics of malaria transmission. In this regard, this study would have benefited from collecting additional publichealth oriented outcome data. For example, measurement of standard entomological indices such as the entomological inoculation rate (EIR) during the study period would have improved our understanding of how the results relate to the incidence of falciparum malaria in the region. Nevertheless, in the context of malaria vector control, understanding spatial and temporal determinants of anopheline abundance can assist with judicious identification of highrisk communities [8, 85], or even households, improving the costeffectiveness of targeted interventions [81, 86]. Moreover, the lagged nature of meteorological predictors of anopheline abundance can be exploited to predict when malaria outbreaks are likely to occur [32], guiding preemptive interventions.
Malaria epidemiology in subSaharan Africa has undergone significant changes in recent years [87]. Kilifi, like many areas, has seen a large increase in the distribution of insecticidetreated bednets (ITNs) since these entomological surveys were undertaken [88, 89]. In other areas changes in bednet use has been associated with changes in entomological ecology, such as declines in A. gambiae s.s.[90]. The influence of the changing intervention landscape on A. funestus is less well understood. This study therefore provides an important baseline assessment of the entomological situation prior to scaling up of ITN distribution and provides a valuable comparative source for ongoing studies in the region.
Conclusions
The statistical framework applied in this analysis, created by bringing together PDL models and GLMMs, represents a powerful tool for understanding factors underpinning the temporal and spatial distribution of anopheline mosquitoes, vectors of falciparum malaria in the study area. The PDL component of the framework permits modelling of past (lagged) seasonal changes in climate, which affects current mosquito density by mediating the abundance of aquatic larval habitats on a large, macrospatial scale. The GLMM component permits simultaneous incorporation of finer, microspatial determinants of mosquito density while handling robustly any clustering of mosquito counts within units of observation (here, households) and residual overdispersion (extraPoisson variation).
Continued development and application of appropriate and powerful statistical methods to understand better the determinants of anopheline abundance and malaria incidence will facilitate refinement of public health interventions. Detailed understanding of the impact of spatially and temporally heterogeneous explanatory variables will help to identify and target communities most at risk of malaria, improving the efficiency and costeffectiveness of control efforts. In particular, a better understanding of the lagged effect of seasonal changes in climate on vector abundance will improve temporal targeting of vector control and other interventions. In turn, including this type of information into early warning systems may assist in the deployment of preventative antivectorial measures for malaria control, facilitating better allocation of resources in often resourcepoor settings.
Abbreviations
 DF:

Degrees of freedom
 EIR:

Entomological inoculation rate
 GLMM:

Generalized linear mixed model
 GPS:

Global positioning system
 ITN:

Insecticidetreated net
 KEMRI:

Kenya Medical Research Institute
 PDL:

Polynomial distributed lag
 LRS:

Likelihood ratio statistic
 PSC:

Pyrethrum spray cath
 s.l.:

Sensu lato
 s.s.:

Sensu stricto.
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Acknowledgements
We thank Punam Amratia, Thomas Churcher and Déirdre Hollingsworth for valuable discussions during earlier stages of the work. Malorie Perry conducted useful literature searches on the relationships between climatic variables and mosquito biology.
Funding statement
MW, MGB and JTM acknowledge funding from the Wellcome Trust; PW is supported by the Biology and Biotechnology Scientific Research Council (BBSRC) Training Grant, No. BB/I015957/1; JCB is supported by the National Institutes of Health under Award Numbers R01AI100968 and R01GM093345; MGB and JTM thank the European Commission FP7 Collaborative project TransMalariaBloc (HEALTHF32008223736). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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JTM and JMM coordinated the field work, data collection and data curation. JCB and CM provided overall project supervision and scientific guidance. MW, PW and MGB conceived the analytical approach. PW performed preliminary statistical analysis of the data. MW performed the final analysis. MW, PW, MGB and JTM wrote the manuscript. All authors read and approved the final, submitted version.
Martin Walker, Peter Winskill contributed equally to this work.
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Walker, M., Winskill, P., Basáñez, MG. et al. Temporal and microspatial heterogeneity in the distribution of Anopheles vectors of malaria along the Kenyan coast. Parasites Vectors 6, 311 (2013). https://doi.org/10.1186/175633056311
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DOI: https://doi.org/10.1186/175633056311
Keywords
 Anopheles gambiae
 Anopheles funestus
 Polynomial distributed lag generalized linear mixed models
 Rainfall
 Temperature
 Household occupancy
 Larval habitats
 Mosquito density
 Malaria
 Kilifi
 Kenya
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