A generic framework for spatial quantitative risk assessments of infectious diseases: lumpy skin disease case study.

The increase in availability of spatial data and the technological advances to handle such data allow for subsequent improvements in our ability to assess risk in a spatial setting. We provide a generic framework for quantitative risk assessments of disease introduction that capitalizes on these new data. It can be adopted across multiple spatial scales, for any pathogen, method of transmission or location. The framework incorporates the risk of initial infection in a previously uninfected location due to registered movement (e.g., trade) and unregistered movement (e.g., daily movements of wild animals). We discuss the steps of the framework and the data required to compute it. We then outline how this framework is applied for a single pathway using lumpy skin disease as a case study, a disease which had an outbreak in the Balkans in 2016. We calculate the risk of initial infection for the rest of Europe in 2016 due to trade. We perform the risk assessment on 3 spatial scales-countries, regions within countries and individual farms. We find that Croatia (assuming no vaccination occurred) has the highest mean probability of infection, with Italy, Hungary and Spain following. Including import detection of infected trade does reduce risk but this reduction is proportionally lower for countries with highest risk. The risk assessment results are consistent across the spatial scales, while in addition, at the finer spatial scales, it highlights specific areas or individual locations of countries on which to focus surveillance.