A stochastic meta-population model of Ebola virus disease transmission for informing public health decisions

Ebola virus disease (EVD) remains a constant international public health threat. Developing models that integrate the complex transmission dynamics of EVD is essential for informing evidence-based strategies for outbreak preparedness and response. Here, we introduce a stochastic, meta-population, compartmental model of EVD epidemics which accounts for key stages of the disease transmission including ecologically-driven zoonotic introductions, person-to-person transmission, spatial spread, and potentially complex interventions. Our model can distinguish between different transmission modes (direct transmission from contact with infectious cases, funeral exposures, or sexual transmission from contact with convalescent individuals) as well as different intervention mechanisms (overall reduction of contacts, safe and dignified burials, and vaccination). We illustrate our approach by simulating EVD epidemics in an area at high risk of zoonotic introduction in the Democratic Republic of the Congo, and show how it can be used to identify potential future transmission hotspots and help assess the scaling of future responses. Our model is implemented in a computer-efficient, free, open-source software, and can be used for informing public health policies.