EOH Events

EOH Journal Club

Combining weather patterns and cycles of population susceptibility to forecast dengue fever epidemic

Thursday 2/13 11:00AM - 12:00PM
4140 Public Health, Young Seminar Room

Presenter: Meghan Matlack

Paper: Combining weather patterns and cycles of population susceptibility to forecast dengue fever epidemic years in Brazil: a dynamic, ensemble learning approach

Authors: Sarah F. McGough, Cesar L. Clemente, J. Nathan Kutz, Mauricio Santillana

Abstract:
Transmission of dengue fever depends on a complex interplay of human, climate, and mosquito
dynamics, which often change in time and space. It is well known that disease dynamics are
highly influenced by a population’s susceptibility to infection and microclimates, small-area
climatic conditions which create environments favorable for the breeding and survival of the
mosquito vector. Here, we present a novel machine learning dengue forecasting approach, which, dynamically in time and adaptively in space, identifies local patterns in weather and population susceptibility to make epidemic predictions at the city-level in Brazil, months ahead of the occurrence of disease outbreaks. Weather-based predictions are improved when information on population susceptibility is incorporated, indicating that immunity is an important predictor neglected by most dengue forecast models. Given the generalizability of our methodology, it may prove valuable for public-health decision making aimed at mitigating the
effects of seasonal dengue outbreaks in locations globally.

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Last Updated On Wednesday, January 29, 2020 by Orbell, Adam W
Created On Wednesday, January 29, 2020