Integrated Mapping of Establishment Risk for Emerging Vector-Borne Infections: A Case Study of Canine Leishmaniasis in Southwest France

Abstract

BackgroundZoonotic 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.MethodsSandflies 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.ConclusionsTo 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.

Importance of individual analysis of environmental and climatic factors affecting the density of Leishmania vectors living in the same geographical area: the example of Phlebotomus ariasi and P. perniciosus in northeast Spain

“…The two species also showed different positive correlation with flora: P. ariasi with mountain pine and oak, while P. perniciosus preferred Aleppo pine and garrigue shrubs. This is related to their altitudinal and bioclimatic distribution, which has been described for the South of France (Rioux et al, 1969;Hartemink et al, 2011). Sand fly abundance prediction maps showing a different predominance of both species according to altitude and vegetation have been obtained in southern France, P. ariasi being predominant in the forested foothills and P. perniciosus at lower altitudes (Hartemink et al, 2011).…”

“…The influence of temperature has been considered not only on the biology and ecology of sand flies (rate of egg production, development of the juvenile stages, number of annual generations, feeding activity, period of activity and survival of adults), but also on the development of the parasite inside the vector (Rioux et al, 1969;Elnaiem et al, 1998;Ready, 2008;Martín Sánchez et al, 2009;Hartemink et al, 2011). Rainfall has been associated with the activity period of sand flies, particularly in some South American foci (Queiroz et al, 2012;Quintana et al, 2012).…”

Progress in the Mathematical Modelling of Visceral Leishmaniasis

“…In reality there is a complex relationship between vector mortality/emergence, EIP, human biting rate and temperature (as discussed by Hartemink et al (2011)), however to facilitate the modelling process, seasonality is usually associated with either change in vector emergence rate, or instead by varying the transmission rate but keeping constant vector population size. Figure 11 shows how using a sinusoidal forcing function of the form (1 + α 1 cos(2πt)) on either the birth rate or the ROI affects the disease about equilibrium for both host and vector populations.…”

“…However, several of the parameters associated with the vector are hard to ascertain and consequently there are great uncertainties in the parameterisation of models, in particular with those which explicitly model vector populations. The density of sandflies and their abun-dance relative to both human and animal hosts are not only difficult to determine but are known to vary greatly between different regions, for example, see Hartemink et al (2011) who used geographic sticky trap data to estimate densities and map R 0 in France. Likewise the seasonal fluctuations of sandfly populations are hard to pin down and so are not featured in many transmission models of VL at present.…”

The role of Culex pipiens mosquitoes in transmission of West Nile virus in Europe

“…The mosquito-to-host ratio is an important determinant of the value of R 0 (Hartemink et al 2011). The underlying assumption for including the mosquito-to-host ratio in the R 0 formula is that the mosquitoes in a certain area will distribute their bites over the hosts available in that area and that the mosquito-to-host ratio thus determines the number of bites that a host will receive.…”