Abstract
Sand flies are responsible for the transmission of leishmaniasis, a neglected tropical disease claiming more than 50,000 lives annually. Leishmaniasis is an emerging health risk in tropical and Mediterranean countries as well as temperate regions in North America and Europe. There is an increasing demand for predicting population dynamics and spreading of sand flies to support management and control, yet phenotypic diversity and complex environmental dependence hamper model development. Here, we present the principles for developing predictive species-specific population dynamics models for important disease vectors. Based on these principles, we developed a sand fly population dynamics model with a generic structure where model parameters are inferred using a surveillance dataset collected from Greece and Cyprus. The model incorporates distinct life stages and explicit dependence on a carefully selected set of environmental variables. The model successfully replicates the observations and demonstrates high predictive capacity on the validation dataset from Turkey. The surveillance datasets inform about biological processes, even in the absence of laboratory experiments. Our findings suggest that the methodology can be applied to other vector species to predict abundance, control dispersion, and help to manage the global burden of vector-borne diseases.
Highlights
Leishmaniasis is a neglected tropical disease caused by protozoan flagellates vectored by phlebotomine sand flies
We focus on three important vector species: Phlebotomus neglectus, Phlebotomus tobbi and Phlebotomus papatasi
We assessed the inferability of a biologically-plausible environmentally driven population dynamics model using only sand fly surveillance data as the basis of inference
Summary
Leishmaniasis is a neglected tropical disease caused by protozoan flagellates vectored by phlebotomine sand flies. The diseases caused by the Leishmania parasite are dynamic because sand flies are dependent on environmental, demographic and human behavioural factors Such factors bring about changes in the habitat of the vectors and of their natural hosts. Parameters derived from controlled experimental conditions may not be readily applicable for field populations especially while microclimate conditions around potential breeding sites cannot be reliably predicted with existing computational capacity and climate models For these reasons, we recently proposed a Bayesian approach to develop population dynamics and disease transmission models by combining experimental data with field observations and observing the dynamics under the influence of large-scale environmental variables[17,18]. The amount of experimental data on a vector or disease of interest is often limited due to the small number of experimental studies on model systems, the variety of vector and pathogen species, and the lack of host specificity[7]
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