A metapopulation model for the 2018 Ebola virus disease outbreak in Equateur province in the Democratic Republic of the Congo

Ebola virus disease (EVD) is a viral haemorrhagic fever with high mortality that has caused a number of severe outbreaks in Central and West Africa. Although the majority previous outbreaks have been relatively small, the result of managing outbreaks places huge strains on already limited resources. Mathematical models matched to early case reporting data can be used to identify outbreaks that are at high risk of spreading. Here we consider the EVD outbreak in Equateur Province in the Democratic Republic of the Congo, which was declared on 8 May 2018. We use a simple stochastic metapopulation model to capture the dynamics in the three affected health zones: Bikoro, Iboko and Wangata. We are able to rapidly simulate a large number of realisations and use approximate Bayesian computation, a likelihood-free method, to determine parameters by matching between reported and simulated cases. This method has a number of advantages over more traditional likelihood-based methods as it is less sensitive to errors in the data and is a natural extension to the prediction framework. Using data from 8 to 25 May 2018 we are able to capture the exponential increases in the number of cases in three locations (Bikoro, Iboko and Wangata), although our estimated basic reproductive ratio is higher than for previous outbreaks. Using additional data until 08 July 2018 we are able to detect a decrease in transmission such that the reproductive ratio falls below one. We also estimate the probability of transmission to Kinshasa. We believe this method of fitting models to data offers a generic approach that can deliver rapid results in real time during a range of future outbreaks.