Aedes koreicus is a highly invasive mosquito, recognised as zoonotic vector of infectious diseases. Native to East-Asia, it has recently become established in Europe. Only limited data is currently available on its ecology. Using data from LExEM, a project that aims to study Ae. koreicus, we created a presence/absence dataset of Ae. koreicus in Northeast Italy. We enriched this dataset with remotely sensed predictors (MODIS LST and NDWI), land use and topographic information to create a Bayesian SDM. Bayesian data analysis is particularly useful when distribution data of a species is sparse, as is often the case with new invasive species. It allows the inclusion of prior knowledge about the species, and thus provides more robust coefficient estimates. We used both mildly and strongly informed priors derived from the scientific literature. Data acquisition regarding the ecology of invasive species and the modelling of their potential distribution are critical in supporting public health policy. Indeed, the spread of new invasive mosquitoes is of increasing concern due to the risk of outbreaks of exotic vector-borne diseases that they can trigger