Integrated mapping of establishment risk for emerging vector-borne infections: a case study of canine leishmaniasis in southwest France

Abstract

Background: Zoonotic visceral leishmaniasis is endemic in the Mediterranean Basin, where the dog is the main reservoir host. The disease's causative agent, Leishmania infantum, is transmitted by blood-feeding female sandflies. This paper reports an integrative study of canine leishmaniasis in a region of France spanning the southwest Massif Central and the northeast Pyrenees, where the vectors are the sandflies Phlebotomus ariasi and P. perniciosus.

Methods: Sandflies were sampled in 2005 using sticky traps placed uniformly over an area of approximately 100 by 150 km. High- and low-resolution satellite data for the area were combined to construct a model of the sandfly data, which was then used to predict sandfly abundance throughout the area on a pixel by pixel basis (resolution of c. 1 km). Using literature- and expert-derived estimates of other variables and parameters, a spatially explicit R(0) map for leishmaniasis was constructed within a Geographical Information System. R(0) is a measure of the risk of establishment of a disease in an area, and it also correlates with the amount of control needed to stop transmission.

Conclusions: To our knowledge, this is the first analysis that combines a vector abundance prediction model, based on remotely-sensed variables measured at different levels of spatial resolution, with a fully mechanistic process-based temperature-dependent R(0) model. The resulting maps should be considered as proofs-of-principle rather than as ready-to-use risk maps, since validation is currently not possible. The described approach, based on integrating several modeling methods, provides a useful new set of tools for the study of the risk of outbreaks of vector-borne diseases.

Figure 1. Schematic overview of the approach.

Figure 2. Temperature map of the study region: average temperature in July.

Figure 3. Relationship between temperature and known temperature-dependent parameters.

Figure 4. Predicted.abundance of P. perniciosus (upper panel) and of P. ariasi (lower panel), based on the integrated model.

Figure 5. R ₀ maps based on the predicted vector abundance maps, the dog density map, and the parameter point estimates in Table 1 .

R ₀ maps are depicted for different values of the multiplication factor: f = 500 (a), f = 1000 (b), and f = 5000 (c). Resolution is 1 km².

Figure 6. R ₀ maps resulting from sampling from parameter ranges with the Latin Hypercube sampling method.

Uniform sampling from the ranges in Table 1 yielded 1000 different sets of parameter values and hence 1000 values of R ₀ per pixel. The 5% percentile of the 1000 values is depicted in a map (a), as well as the mean values (b) and the 95% percentile, (c). Resolution is 1 km².