Modeling the Distribution of the West Nile and Rift Valley Fever Vector Culex Pipiens in Arid and Semi-arid Regions of the Middle East and North Africa

Background: The Middle East North Africa (MENA) region is under continuous threat of the re-emergence of West Nile virus (WNV) and Rift Valley Fever virus (RVF), two pathogens transmitted by the vector species Culex pipiens. Predicting areas at high risk for disease transmission requires an accurate model of vector distribution, however, most Cx. pipiens distribution modeling has been confined to temperate, forested habitats. Modeling species distributions across a heterogeneous landscape structure requires a flexible modeling method to capture variation in mosquito response to predictors as well as occurrence data points taken from a sufficient range of habitat types.

Methods: We used presence-only data from Egypt and Lebanon to model the population distribution of Cx. pipiens across a portion of the MENA that also encompasses Jordan, Syria, and Israel. Models were created with a set of environmental predictors including bioclimatic data, human population density, hydrological data, and vegetation indices, and built using maximum entropy (Maxent) and boosted regression tree (BRT) methods. Models were created with and without the inclusion of human population density.

Results: Predictions of Maxent and BRT models were strongly correlated in habitats with high probability of occurrence (Pearson's r=0.774, r=0.734), and more moderately correlated when predicting into regions that exceeded the range of the training data (r=0.666,r=0.558). All models agreed in predicting high probability of occupancy around major urban areas, along the banks of the Nile, the valleys of Israel, Lebanon, and Jordan, and southwestern Saudi Arabia. The most powerful predictors of Cx. pipiens habitat were human population density (60.6% Maxent models, 34.9% BRT models) and the seasonality of the enhanced vegetation index (EVI) (44.7% Maxent, 16.3% BRT). Maxent models tended to be dominated by a single predictor. Areas of high probability corresponded with sites of independent surveys or previous disease outbreaks.

Conclusions: Cx. pipiens occurrence was positively associated with areas of high human population density and consistent vegetation cover, but was not significantly driven by temperature and rainfall, suggesting human-induced habitat change such as irrigation and urban infrastructure has a greater influence on vector distribution in this region than in temperate zones.