Speaker
Description
Accurate assessment of the infection scale is essential for determining the end of Ebola virus disease (EVD) outbreaks in West Africa. Previous compartmental modeling studies often assume the total population to be the effective population size or simply preset the initial number of infections. These simplifying assumptions can distort transmission dynamics and reduce prediction accuracy.
This study aims to (1) directly estimate the effective population size and pre-surveillance spread period within a stochastic compartment model using surveillance data, and (2) quantify the end-of-outbreak probability by accounting for stochastic uncertainty through stochastic differential equations.
Using 2014–2016 West African Ebola data, we jointly estimated transmission parameters, revealing substantial differences between the estimated effective size and total census population—a discrepancy that improved overall model fit. Building on prior analyses, our stochastic differential equation framework confirmed that the current WHO 42-day waiting criterion may be insufficient to ensure a 95% confident level for declaring an end of outbreak \cite{thompson_rigorous_2019}. These findings suggest that the adequacy of the WHO 42-day criterion may vary depending on country-specific dynamics and surveillance capacity, indicating the need for more sophisticated approaches to end-of-outbreak criteria.
Bibliography
@article{thompson_rigorous_2019,
title = {Rigorous surveillance is necessary for high confidence in end-of-outbreak declarations for {Ebola} and other infectious diseases},
volume = {374},
issn = {0962-8436, 1471-2970},
url = {https://royalsocietypublishing.org/doi/10.1098/rstb.2018.0431},
doi = {10.1098/rstb.2018.0431},
abstract = {The World Health Organization considers an Ebola outbreak to have ended once 42 days have passed since the last possible exposure to a confirmed case. Benefits of a quick end-of-outbreak declaration, such as reductions in trade/travel restrictions, must be balanced against the chance of flare-ups from undetected residual cases. We show how epidemiological modelling can be used to estimate the surveillance level required for decision-makers to be confident that an outbreak is over. Results from a simple model characterizing an Ebola outbreak suggest that a surveillance sensitivity (i.e. case reporting percentage) of 79\% is necessary for 95\% confidence that an outbreak is over after 42 days without symptomatic cases. With weaker surveillance, unrecognized transmission may still occur: if the surveillance sensitivity is only 40\%, then 62 days must be waited for 95\% certainty. By quantifying the certainty in end-of-outbreak declarations, public health decision-makers can plan and communicate more effectively.
This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This issue is linked with the earlier theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.},
language = {en},
number = {1776},
urldate = {2026-03-14},
journal = {Philosophical Transactions of the Royal Society B: Biological Sciences},
author = {Thompson, Robin N. and Morgan, Oliver W. and Jalava, Katri},
month = jul,
year = {2019},
pages = {20180431},
}
