A geographical information system-based multicriteria evaluation to map areas at risk for Rift Valley fever vector-borne transmission in Italy.

Rift Valley fever (RVF) is a severe mosquito-borne disease that is caused by a Phlebovirus (Bunyaviridae) and affects domestic ruminants and humans. Recently, its distribution widened, threatening Europe. The probability of the introduction and large-scale spread of Rift Valley fever virus (RVFV) in Europe is low, but localized RVF outbreaks may occur in areas where populations of ruminants and potential vectors are present. In this study, we assumed the introduction of the virus into Italy and focused on the risk of vector-borne transmission of RVFV to three main European potential hosts (cattle, sheep and goats). Five main potential mosquito vectors belonging to the Culex and Aedes genera that are present in Italy were identified in a literature review. We first modelled the geographical distribution of these five species based on expert knowledge and using land cover as a proxy of mosquito presence. The mosquito distribution maps were compared with field mosquito collections from Italy to validate the model. Next, the risk of RVFV transmission was modelled using a multicriteria evaluation (MCE) approach, integrating expert knowledge and the results of a literature review on host sensitivity and vector competence, feeding behaviour and abundance. A sensitivity analysis was performed to assess the robustness of the results with respect to expert choices. The resulting maps include (i) five maps of the vector distribution, (ii) a map of suitable areas for vector-borne transmission of RVFV and (iii) a map of the risk of RVFV vector-borne transmission to sensitive hosts given a viral introduction. Good agreement was found between the modelled presence probability and the observed presence or absence of each vector species. The resulting RVF risk map highlighted strong spatial heterogeneity and could be used to target surveillance. In conclusion, the geographical information system (GIS)-based MCE served as a valuable framework and a flexible tool for mapping the areas at risk of a pathogen that is currently absent from a region.