ArticleeLife2025
Forecasting the spatial spread of an Ebola epidemic in real time: Comparing predictions of mathematical models and experts.
Article in eLife, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- A generalized multi-population model for Ebola virus transmission incorporating environmental reservoir dynamics.Infectious medicine · 2026Article
- Evaluation of respiratory disease hospitalisation forecasts using synthetic outbreak data.Communications medicine · 2026Article
- Local Influenza Forecasts Outperform State-Level Forecasts in the United States.medRxiv : the preprint server for health sciences · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Background: Ebola virus disease outbreaks can often be controlled, but require rapid response efforts frequently with profound operational complexities. Mathematical models can be used to support response planning, but it is unclear if models improve the prior understanding of experts. Methods: We performed repeated surveys of Ebola response experts during an outbreak. From each expert, we elicited the probability of cases exceeding four thresholds between 2 and 20 cases in a set of small geographical areas in the following calendar month. We compared the predictive performance of these forecasts to those of two mathematical models with different spatial interaction components. Results: An ensemble combining the forecasts of all experts performed similarly to the two models. Experts showed stronger bias than models forecasting two-case threshold exceedance. Experts and models both performed better when predicting exceedance of higher thresholds. The models also tended to be better at risk-ranking areas than experts. Conclusions: Our results support the use of models in outbreak contexts, offering a convenient and scalable route to a quantified situational awareness, which can provide confidence in or to call into question existing advice of experts. There could be value in combining expert opinion and modelled forecasts to support the response to future outbreaks. Funding: This study was partly funded by the Department of Health and Social Care using UK Aid funding 47 and is managed by the National Institute for Health and Care Research (VEEPED: PR-OD-1017- 48 20002; AR and WJE). This study was partly funded by the Wellcome Trust (210758/Z/18/Z : JDM 49 and SF). The views expressed in this publication are those of the authors and not necessarily 50 those of the funders.
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