ArticleEpidemics2020
Ensemble forecast and parameter inference of childhood diarrhea in Chobe District, Botswana.
Article in Epidemics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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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
5 citing papers in PubMed.
- Predictive modeling for infectious diarrheal disease in pediatric populations: A systematic review.Learning health systems · 2024Article
- Analytical reference framework to analyze non-COVID-19 events.Population health metrics · 2023Article
- Review
- Differential effects of intervention timing on COVID-19 spread in the United States.Science advances · 2020Article
- Differential Effects of Intervention Timing on COVID-19 Spread in the United States.medRxiv : the preprint server for health sciences · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Diarrheal disease is the second largest cause of mortality in children younger than 5, yet our ability to anticipate and prepare for outbreaks remains limited. Here, we develop and test an epidemiological forecast model for childhood diarrheal disease in Chobe District, Botswana. Our prediction system uses a compartmental susceptible-infected-recovered-susceptible (SIRS) model coupled with Bayesian data assimilation to infer relevant epidemiological parameter values and generate retrospective forecasts. Our model inferred two system parameters and accurately simulated weekly observed diarrhea cases from 2007-2017. Accurate retrospective forecasts for diarrhea outbreaks were generated up to six weeks before the predicted peak of the outbreak, and accuracy increased over the progression of the outbreak. Many forecasts generated by our model system were more accurate than predictions made using only historical data trends. Accurate real-time forecasts have the potential to increase local preparedness for coming outbreaks through improved resource allocation and healthcare worker distribution.
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Registered trials
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