Multi-model forecasts of the ongoing Ebola epidemic in the Democratic Republic of Congo, March - October 2019

The 2018-20 Ebola outbreak in the Democratic Republic of the Congo is the first to occur in an armed conflict zone. The resulting impact on population movement, treatment centers, and surveillance has created an unprecedented challenge for real-time epidemic forecasting. Most standard mathematical models cannot capture the observed incidence trajectory when it deviates from a traditional epidemic logistic curve. We fit seven dynamic models of increasing complexity to the incidence data published in the World Health Organization Situation Reports, after adjusting for reporting delays. These models include a simple logistic model, a Richards model, an endemic Richards model, a double logistic growth model, a multi-model approach, and two sub-epidemic models. We analyze model fit to the data and compare real-time forecasts throughout the ongoing epidemic across 29 weeks from March 11 to September 23, 2019. We observe that the modest extensions presented allow for capturing a wide range of epidemic behavior. The multi-model approach yields the most reliable forecasts on average for this application, and the presented extensions improve model flexibility and forecasting accuracy, even in the context of limited epidemiological data.