Projections of epidemic transmission and estimation of vaccination impact during an ongoing Ebola virus disease outbreak in Northeastern Democratic Republic of Congo, as of Feb. 25, 2019

Abstract

Background: As of February 25, 2019, 875 cases of Ebola virus disease (EVD) were reported in North Kivu and Ituri Provinces, Democratic Republic of Congo. Since the beginning of October 2018, the outbreak has largely shifted into regions in which active armed conflict has occurred, and in which EVD cases and their contacts have been difficult for health workers to reach. We used available data on the current outbreak, with case-count time series from prior outbreaks, to project the short-term and long-term course of the outbreak.

Methods: For short- and long-term projections, we modeled Ebola virus transmission using a stochastic branching process that assumes gradually quenching transmission rates estimated from past EVD outbreaks, with outbreak trajectories conditioned on agreement with the course of the current outbreak, and with multiple levels of vaccination coverage. We used two regression models to estimate similar projection periods. Short- and long-term projections were estimated using negative binomial autoregression and Theil-Sen regression, respectively. We also used Gott's rule to estimate a baseline minimum-information projection. We then constructed an ensemble of forecasts to be compared and recorded for future evaluation against final outcomes. From August 20, 2018 to February 25, 2019, short-term model projections were validated against known case counts.

Results: During validation of short-term projections, from one week to four weeks, we found models consistently scored higher on shorter-term forecasts. Based on case counts as of February 25, the stochastic model projected a median case count of 933 cases by February 18 (95% prediction interval: 872-1054) and 955 cases by March 4 (95% prediction interval: 874-1105), while the auto-regression model projects median case counts of 889 (95% prediction interval: 876-933) and 898 (95% prediction interval: 877-983) cases for those dates, respectively. Projected median final counts range from 953 to 1,749. Although the outbreak is already larger than all past Ebola outbreaks other than the 2013-2016 outbreak of over 26,000 cases, our models do not project that it is likely to grow to that scale. The stochastic model estimates that vaccination coverage in this outbreak is lower than reported in its trial setting in Sierra Leone.

Conclusions: Our projections are concentrated in a range up to about 300 cases beyond those already reported. While a catastrophic outbreak is not projected, it is not ruled out, and prevention and vigilance are warranted. Prospective validation of our models in real time allowed us to generate more accurate short-term forecasts, and this process may prove useful for future real-time short-term forecasting. We estimate that transmission rates are higher than would be seen under target levels of 62% coverage due to contact tracing and vaccination, and this model estimate may offer a surrogate indicator for the outbreak response challenges.

Fig 1. Map of health districts in which confirmed and/or probable cases have occurred in Northeastern DRC.

Case counts as of Feb. 25 are indicated by color by district. Districts in which active conflict has occurred as of Feb. 25 are marked by a blue outline. Inset provides magnification and labels for smaller districts. Data sourced from Référentiel Géographique Commun [7], made available under the Open Data Commons Open Database License.

Fig 2. Prospective validation of probabilistic projections of auto-regression and stochastic models, by comparing projected cumulative case counts from past data to known case counts.

Vertical bars indicate known case counts (height not to scale).

Fig 3. Log-likelihood scores of stochastic and auto-regression projections in prospective validation.

Fig 4. Short-term probabilistic projections of case counts based on reported counts as of February 25, 2019.

Stochastic and auto-regression models were used. Vertical scale of probabilities, on right side of plot, applies to all dates.

Fig 5. Medians and prediction intervals from short-term projections of case counts based on reported counts as of February 25, 2019.

Solid dots indicate median count.

Fig 6. Probabilistic projections of final case counts based on reported counts as of February 25, 2019.

Stochastic, Theil-Sen regression, and Gott’s rule models were used.

Fig 7. Medians and prediction intervals from projections of final case counts based on reported counts as of February 25, 2019.

Solid dots indicate median size. Inset provides detail at smaller numbers.