Real-time projections of Ebola outbreak size and duration with and without vaccine use in Équateur, Democratic Republic of Congo, as of May 27, 2018

Abstract

Background: As of May 27, 2018, 54 cases of Ebola virus disease (EVD) were reported in Équateur Province, Democratic Republic of Congo. We used reported case counts and time series from prior outbreaks to estimate the current outbreak size and duration with and without vaccine use. Methods:We modeled Ebola virus transmission using a stochastic branching process model with a negative binomial distribution, using both estimates of reproduction number R declining from supercritical to subcritical derived from past Ebola outbreaks, as well as a particle filtering method to generate a probabilistic projection of the future course of the outbreak conditioned on its reported trajectory to date; modeled using 0%, 44%, and 62% estimates of vaccination coverage. Additionally, we used the time series for 18 prior Ebola outbreaks from 1976 to 2016 to parameterize a regression model predicting the outbreak size from the number of observed cases from April 4 to May 27. Results:With the stochastic transmission model, we projected a median outbreak size of 78 EVD cases (95% credible interval: 52, 125.4), 86 cases (95% credible interval: 53, 174.3), and 91 cases (95% credible interval: 52, 843.5), using 62%, 44%, and 0% estimates of vaccination coverage. With the regression All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. . https://doi.org/10.1101/331447 doi: bioRxiv preprint model, we estimated a median size of 85.0 cases (95% prediction interval: 53.5, 216.6).Conclusions: This outbreak has the potential to be the largest outbreak in DRC since 2007. Vaccines are projected to limit outbreak size and duration but are only part of prevention, control, and care strategies.

Real-time projections of epidemic transmission and estimation of vaccination impact during an Ebola virus disease outbreak in Northeastern Democratic Republic of Congo

“…This model is similar to one described in previous work 10 , with the addition of a smoothing step allowing transmission rates intermediate between those estimated from previous outbreaks.…”

“…We used this model to generate a set of probabilistic projections of the size and duration of simulated outbreaks in the current setting. This model is similar to one described in previous work 10 , with the addition of a smoothing step allowing transmission rates intermediate between those estimated from previous outbreaks.…”

“…Finally, we reported the median and 95% central coverage intervals for the prediction distribution, conditional on the predicted value being no smaller than the observed value for each day. Full details are given in 10 . All analyses were conducted using R 3.4.2 (R Foundation for Statistical Computing, Vienna, Austria).…”

Real-time predictions of the 2018–2019 Ebola virus disease outbreak in the Democratic Republic of the Congo using Hawkes point process models

“…There is an increasing body of evidence suggesting that short-term forecasts with few parameters are more reliable than long-term forecasts (particularly early in an outbreak) that determine the final outbreak size ( Worden et al, 2018 ; Funk et al, 2018 ; Viboud et al, 2017 ; Chowell et al, 2017 ). In the context of an ongoing outbreak, many published statistical models have focused on long-term or final outbreak size ( Meltzer et al, 2014 ; Kelly et al, 2018 ; Valdez et al, 2015 ; Chretien et al, 2015 ; Siettos et al, 2015 ). Given the advantages of the Hawkes model and the limitations of other statistical models in the ongoing EVD outbreak setting ( Chowell et al, 2017 ), we fit the Hawkes point process model to daily EVD case counts to forecast case counts over subsequent weeks.…”

The opportunities and challenges of an Ebola modeling research coordination group

“…The use of real-time modeling and analytics to support public health decision-making (also known as "outbreak science") has been an important capability that has grown during previous outbreaks [1][2][3]. Despite the informative role that infectious disease models played in the recent DRC outbreak [4][5][6][7], cross-talk within the infectious disease modeling community and between infectious disease modelers and model stakeholders, such as health agencies, may be limited. Lack of communication can reduce the potential use of modeling capability to inform outbreak prevention and mitigation strategies.…”