Near real-time forecasting for cholera decision making in Haiti after Hurricane Matthew.

PLoS Computational Biology
Damiano PasettoAndrea Rinaldo

Abstract

Computational models of cholera transmission can provide objective insights into the course of an ongoing epidemic and aid decision making on allocation of health care resources. However, models are typically designed, calibrated and interpreted post-hoc. Here, we report the efforts of a team from academia, field research and humanitarian organizations to model in near real-time the Haitian cholera outbreak after Hurricane Matthew in October 2016, to assess risk and to quantitatively estimate the efficacy of a then ongoing vaccination campaign. A rainfall-driven, spatially-explicit meta-community model of cholera transmission was coupled to a data assimilation scheme for computing short-term projections of the epidemic in near real-time. The model was used to forecast cholera incidence for the months after the passage of the hurricane (October-December 2016) and to predict the impact of a planned oral cholera vaccination campaign. Our first projection, from October 29 to December 31, predicted the highest incidence in the departments of Grande Anse and Sud, accounting for about 45% of the total cases in Haiti. The projection included a second peak in cholera incidence in early December largely driven by heavy rainfall forecasts...Continue Reading

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Citations

Jul 15, 2020·PLoS Neglected Tropical Diseases·Alexandra SmirnovaGerardo Chowell
Apr 25, 2020·Proceedings of the National Academy of Sciences of the United States of America·Marino GattoAndrea Rinaldo
May 6, 2019·Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences·G ChowellC Perrings
Jun 21, 2019·Royal Society Open Science·Lorenzo MariMarino Gatto
May 14, 2021·Nature Communications·Lorenzo MariMarino Gatto

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Software Mentioned

Differential Evolution Adaptive Metropolis ( DREAM )
DREAM
Epicentre

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