Ecological niche modeling of Aedes mosquito vectors of chikungunya virus in southeastern Senegal

Abstract

BackgroundChikungunya virus (CHIKV) originated in a sylvatic cycle of transmission between non-human animal hosts and vector mosquitoes in the forests of Africa. Subsequently the virus jumped out of this ancestral cycle into a human-endemic transmission cycle vectored by anthropophilic mosquitoes. Sylvatic CHIKV cycles persist in Africa and continue to spill over into humans, creating the potential for new CHIKV strains to enter human-endemic transmission. To mitigate such spillover, it is first necessary to delineate the distributions of the sylvatic mosquito vectors of CHIKV, to identify the environmental factors that shape these distributions, and to determine the association of mosquito presence with key drivers of virus spillover, including mosquito and CHIKV abundance. We therefore modeled the distribution of seven CHIKV mosquito vectors over two sequential rainy seasons in Kédougou, Senegal using Maxent.MethodsMosquito data were collected in fifty sites distributed in five land cover classes across the study area. Environmental data representing land cover, topographic, and climatic factors were included in the models. Models were compared and evaluated using area under the receiver operating characteristic curve (AUROC) statistics. The correlation of model outputs with abundance of individual mosquito species as well as CHIKV-positive mosquito pools was tested.ResultsFourteen models were produced and evaluated; the environmental variables most strongly associated with mosquito distributions were distance to large patches of forest, landscape patch size, rainfall, and the normalized difference vegetation index (NDVI). Seven models were positively correlated with mosquito abundance and one (Aedes taylori) was consistently, positively correlated with CHIKV-positive mosquito pools. Eight models predicted high relative occurrence rates of mosquitoes near the villages of Tenkoto and Ngary, the areas with the highest frequency of CHIKV-positive mosquito pools.ConclusionsOf the environmental factors considered here, landscape fragmentation and configuration had the strongest influence on mosquito distributions. Of the mosquito species modeled, the distribution of Ae. taylori correlated most strongly with abundance of CHIKV, suggesting that presence of this species will be a useful predictor of sylvatic CHIKV presence.Electronic supplementary materialThe online version of this article (10.1186/s13071-018-2832-6) contains supplementary material, which is available to authorized users.

Serological Data Shows Low Levels of Chikungunya Exposure in Senegalese Nomadic Pastoralists

“…The CHIKV was identified in Senegal in the 1960s [16,17], with defined outbreaks occurring in 2009, 2010, and 2015, and a known sylvatic cycle with infected monkeys identified in multiple studies [18,19,20,21]. Recent studies have confirmed current CHIKV presence in Senegal both by detection of active viremia and serological evidence [19,20,22].…”

High-resolution habitat suitability model for Phlebotomus pedifer, the vector of cutaneous leishmaniasis in southwestern Ethiopia

“…Other studies modeling the distribution of vectors commonly use a 1 km spatial resolution because climate layers are only available at this coarse resolution [65,66,[75][76][77]. Because climate variables are often the most important predictors for SDMs of disease vectors, these data should be very detailed [35,65,66,75].…”

High-resolution habitat suitability model for Phlebotomus pedifer, the vector of cutaneous leishmaniasis in southwestern Ethiopia

“…Other studies modeling the distribution of vectors commonly use a 1 km spatial resolution because climate layers are only available at this coarse resolution [66,67,[76][77][78]. Because climate variables are often the most important predictors for SDMs of disease vectors, these data should be very detailed [36,66,67,76].…”