Spatial and integrated modelling of the transmission of vector-borne and zoonotic infections

(eng) Several vector-borne and zoonotic diseases have emerged or re-emerged in Europe over these last decades. Besides climate change that influences disease risk at a regional scale, landscape changes could be responsible for local heterogeneities in disease risk. Spatial epidemiology tries to understand and predict spatial variations in disease risk by using spatial tools and spatially-explicit modelling methods. This study investigated the impact of fine-grained landscape patterns on the transmission of vector-borne and zoonotic infections in terms of habitat suitability for vectors and/or hosts and of exposure of people to infectious agents. This was studied through three human diseases emerging or at risk of re-emergence in Europe: the rodent-borne Puumala hantavirus, the tick-borne Lyme borreliosis and the mosquito-borne malaria infections. Statistical models were first used to study the relationships between environmental variables and host abundance, host prevalence, and human cases of Puumala hantavirus. Environmental factors were also combined with socio-economic factors to explain Puumala hantavirus and Lyme borreliosis incidence rates. The combination of factors explaining disease transmission and the complexity of such systems led to the development of an innovative, spatially-explicit modelling method: multi-agent simulation (MAS). The MALCAM simulation model was developed to assess the risk of malaria re-emergence in southern France and simulates spatial and temporal variations in contact rate between people and potential malaria vectors. The effect of changes in potential drivers of malaria re-emergence was also simulated. The different case studies showed that fine-grained landscape patterns influence the presence and abundance of vectors and hosts. Moreover, environmental conditions may also influence disease transmission through pathogen dispersal and the exposure of people to infectious agents. Finally, this study showed that people-vector contacts not only depend on the spatial distribution of people and potential vectors, but also on their behaviours and interactions.