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

Hdl Handle:
http://hdl.handle.net/10144/619170
Title:
Near real-time forecasting for cholera decision making in Haiti after Hurricane Matthew
Authors:
Pasetto, D; Finger, F; Camacho, A; Grandesso, F; Cohuet, S; Lemaitre, JC; Azman, AS; Luquero, FJ; Bertuzzo, E; Rinaldo, A
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
Issue Date:
16-May-2018
URI:
http://hdl.handle.net/10144/619170
DOI:
10.1371/journal.pcbi.1006127
PubMed ID:
29768401
Submitted date:
2018-06-04
Language:
en
ISSN:
1553-7358
Appears in Collections:
Other Diseases

Full metadata record

DC FieldValue Language
dc.contributor.authorPasetto, Den
dc.contributor.authorFinger, Fen
dc.contributor.authorCamacho, Aen
dc.contributor.authorGrandesso, Fen
dc.contributor.authorCohuet, Sen
dc.contributor.authorLemaitre, JCen
dc.contributor.authorAzman, ASen
dc.contributor.authorLuquero, FJen
dc.contributor.authorBertuzzo, Een
dc.contributor.authorRinaldo, Aen
dc.date.accessioned2018-06-12T14:51:07Z-
dc.date.available2018-06-12T14:51:07Z-
dc.date.issued2018-05-16-
dc.date.submitted2018-06-04-
dc.identifier.citationNear real-time forecasting for cholera decision making in Haiti after Hurricane Matthew. 2018, 14 (5):e1006127 PLoS Comput. Biol.en
dc.identifier.issn1553-7358-
dc.identifier.pmid29768401-
dc.identifier.doi10.1371/journal.pcbi.1006127-
dc.identifier.urihttp://hdl.handle.net/10144/619170-
dc.description.abstractComputational 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.en
dc.language.isoenen
dc.publisherPublic Library of Scienceen
dc.rightsArchived with thanks to PLoS Computational Biologyen
dc.titleNear real-time forecasting for cholera decision making in Haiti after Hurricane Matthewen
dc.identifier.journalPLoS Computational Biologyen

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