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    Apr 18, 2021
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    Near real-time forecasting for cholera decision making in Haiti after Hurricane Matthew

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    Pasetto et al - 2018 - Near real ...
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    Authors
    Pasetto, D
    Finger, F
    Camacho, A
    Grandesso, F
    Cohuet, S
    Lemaitre, JC
    Azman, AS
    Luquero, FJ
    Bertuzzo, E
    Rinaldo, A
    Issue Date
    2018-05-16
    Submitted date
    2018-06-04
    
    Metadata
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    Journal
    PLoS Computational Biology
    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, confirming the urgency for rapid intervention. A second projection (from November 12 to December 31) used updated rainfall forecasts to estimate that 835 cases would be averted by vaccinations in Grande Anse (90% Prediction Interval [PI] 476-1284) and 995 in Sud (90% PI 508-2043). The experience gained by this modeling effort shows that state-of-the-art computational modeling and data-assimilation methods can produce informative near real-time projections of cholera incidence. Collaboration among modelers and field epidemiologists is indispensable to gain fast access to field data and to translate model results into operational recommendations for emergency management during an outbreak. Future efforts should thus draw together multi-disciplinary teams to ensure model outputs are appropriately based, interpreted and communicated.
    Publisher
    Public Library of Science
    URI
    http://hdl.handle.net/10144/619170
    DOI
    10.1371/journal.pcbi.1006127
    PubMed ID
    29768401
    Language
    en
    ISSN
    1553-7358
    ae974a485f413a2113503eed53cd6c53
    10.1371/journal.pcbi.1006127
    Scopus Count
    Collections
    Other Diseases

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