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 c...

Simple discrete-time self-exciting models can describe complex dynamic processes: a case study of COVID-19

“…More-over, a prediction is only realistic in the short term and generally only at times where there is no evidence of abnormal behaviour. This is consistent with other models in the literature [33,34,54–56]. Thus we consider in-sample and out-of-sample posterior predictive checks in this study as a measure of model fit only.…”

Impact of prophylactic vaccination strategies on Ebola virus transmission: A modeling analysis

“…Multiple mathematical modeling analyses related to Ebola hemorrhagic fever have been undertaken to forecast peak incidence and size of outbreaks [15]. They have evaluated shifts in disease transmission dynamics during epidemics [16], identified factors contributing to the recurrence and persistence of outbreaks [17], assessed the population-level impact of quarantine on disease transmission dynamics [18], estimated size and duration of outbreaks with and without vaccine use [19], assessed the role of sexual transmission in spread of infection during outbreaks [20], captured real-time disease dynamics in the midst of outbreaks [21], projected the short-and long-term course of outbreaks [22], evaluated the effectiveness of control were employed by Janssen Vaccines & Prevention B.V., Leiden, The Netherlands and RP, AK, VM and HB were employed by SmartAnalyst Inc, New York, NY, USA or its subsidiaries. While Janssen Vaccines & Prevention and Janssen Vaccines & Prevention B.V. provided support in the form of salaries to authors CS, VOM, KL and BC, they did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.…”

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

“…In February 2019, a sharp increase in cases and transmission were observed, coinciding with deteriorating security, targeted attacks on response teams, and decreasing trust in the Ebola response efforts (31, 32). Previous studies have provided real-time forecasts at different time points of the 2018-20 Ebola epidemic in the DRC (Figure S1) using various approaches, including a semi-structured model that relies on nowcasting (21), stochastic and auto-regressive models that incorporate historical data (20), as well as a sub-epidemic wave framework (30), which we also use here. While each of these approaches performed well for fitting and forecasting the trajectory of the outbreak in 2018 and early 2019, each model failed to predict the case resurgence observed in February 2019, resulting in forecasts that drastically underestimated the true cumulative case count to date.…”