期刊
EPIDEMICS
卷 22, 期 -, 页码 50-55出版社
ELSEVIER
DOI: 10.1016/j.epidem.2017.02.009
关键词
Ebola; Forecasting; Mathematical modeling; Bayesian inference
We describe a relatively simple stochastic model of Ebola transmission that was used to produce forecasts with the lowest mean absolute error among Ebola Forecasting Challenge participants. The model enabled prediction of peak incidence, the timing of this peak, and final size of the outbreak. The underlying discrete-time compartmental model used a time-varying reproductive rate modeled as a multiplicative random walk driven by the number of infectious individuals. This structure generalizes traditional-Susceptible-Infected-Recovered (SIR) disease modeling approaches and allows for the flexible consideration of outbreaks with complex trajectories of disease dynamics. (c) 2017 The Authors. Published by Elsevier B.V.
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