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

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

SUBMITTER: Pasetto D 

PROVIDER: S-EPMC5973636 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature