ArticleScience advances2024
Optimizing the timing of an end-of-outbreak declaration: Ebola virus disease in the Democratic Republic of the Congo.
Article in Science advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Optimal algorithms for controlling infectious diseases in real time using noisy infection data.PLoS computational biology · 2025Article
- Real-time inference of the end of an outbreak: Temporally aggregated disease incidence data and under-reporting.Infectious Disease Modelling · 2025Article
- Reducing transmission in multiple settings is required to eliminate the risk of major Ebola outbreaks: a mathematical modelling study.Journal of the Royal Society, Interface · 2025Article
- Estimation of end-of-outbreak probabilities in the presence of delayed and incomplete case reporting.Proceedings. Biological sciences · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
Following the apparent final case in an Ebola virus disease (EVD) outbreak, the decision to declare the outbreak over must balance societal benefits of relaxing interventions against the risk of resurgence. Estimates of the end-of-outbreak probability (the probability that no future cases will occur) provide quantitative evidence that can inform the timing of an end-of-outbreak declaration. An existing modeling approach for estimating the end-of-outbreak probability requires comprehensive contact tracing data describing who infected whom to be available, but such data are often unavailable or incomplete during outbreaks. Here, we develop a Markov chain Monte Carlo-based approach that extends the previous method and does not require contact tracing data. Considering data from two EVD outbreaks in the Democratic Republic of the Congo, we find that data describing who infected whom are not required to resolve uncertainty about when to declare an outbreak over.
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