Analyzing and forecasting the Ebola incidence in North Kivu, the Democratic Republic of the Congo from 2018–19 in real time

During an Ebola virus disease (EVD)outbreak, the analysis and forecasting of the incidence in real time is challenged by reporting of cases, especially the reporting delay. It should be remembered that the latest count of cases is likely underestimated in real time, and moreover, the effective reproduction number, i.e. the average number of secondary cases generated by a single primary case at a given point in time, is also underestimated without proper adjustment. The present study aimed to adjust the reporting delay to appropriately estimate the latest incidence and obtain short-term forecasts from weekly reporting data of EVD in North Kivu, the Democratic Republic of the Congo (DRC). A semi-structured modeling approach was taken, accounting for reporting delay which can depend on time. The mean reporting delay was estimated at 11.6 days (95% CI: 11.3, 11.9)and the standard deviation was estimated to have changed from 26 November 2019 from 8.5–6.0 days. Nowcasting was successfully implemented by account for the time-dependent reporting delay: it mostly contained future observed values within the 95% confidence intervals, but there were failures when the reported incidence abruptly changed over time. Forecasting was also exercised in a similar manner to the nowcasting, while we imposed an extrapolation approach to the effective reproduction number for two future weeks. Moving average of the reproduction numbers for a few weeks prior the latest time of observation outperformed other extrapolations. The information that we can gain from real time (i.e. sequential)update of “situation report” can be considerably improved by integrating the proposed nowcasting and forecasting to the surveillance system.