Ensemble forecast and parameter inference of childhood diarrhea in Chobe District, Botswana
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 syst...
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doaj-181dd7f4d9a349c6a0b6ec5c1e08bd502020-11-25T03:08:08ZengElsevierEpidemics1755-43652020-03-0130Ensemble forecast and parameter inference of childhood diarrhea in Chobe District, BotswanaAlexandra K. Heaney0Kathleen A. Alexander1Jeffrey Shaman2Environmental Health Sciences Department, University of California Berkeley, United States; Corresponding author.Department of Fish and Wildlife Conservation, Virginia Tech, United States; Chobe Research Institute, CARACAL, BotswanaEnvironmental Health Sciences Department, Columbia University, United StatesDiarrheal 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. Keywords: Childhood diarrhea, Forecasting, Bayesian inference, Dynamic modelinghttp://www.sciencedirect.com/science/article/pii/S1755436518301658 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Alexandra K. Heaney Kathleen A. Alexander Jeffrey Shaman |
spellingShingle |
Alexandra K. Heaney Kathleen A. Alexander Jeffrey Shaman Ensemble forecast and parameter inference of childhood diarrhea in Chobe District, Botswana Epidemics |
author_facet |
Alexandra K. Heaney Kathleen A. Alexander Jeffrey Shaman |
author_sort |
Alexandra K. Heaney |
title |
Ensemble forecast and parameter inference of childhood diarrhea in Chobe District, Botswana |
title_short |
Ensemble forecast and parameter inference of childhood diarrhea in Chobe District, Botswana |
title_full |
Ensemble forecast and parameter inference of childhood diarrhea in Chobe District, Botswana |
title_fullStr |
Ensemble forecast and parameter inference of childhood diarrhea in Chobe District, Botswana |
title_full_unstemmed |
Ensemble forecast and parameter inference of childhood diarrhea in Chobe District, Botswana |
title_sort |
ensemble forecast and parameter inference of childhood diarrhea in chobe district, botswana |
publisher |
Elsevier |
series |
Epidemics |
issn |
1755-4365 |
publishDate |
2020-03-01 |
description |
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. Keywords: Childhood diarrhea, Forecasting, Bayesian inference, Dynamic modeling |
url |
http://www.sciencedirect.com/science/article/pii/S1755436518301658 |
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