Forecasting Ebola with a regression transmission model.
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.
Leidos Supporting the Department of Health and Human Services (HHS), Biomedical Advanced Research and Development Authority (BARDA), United States. Electronic address: jason.m.asher@gmail.com.
Jason
J
Asher
D001499:Bayes Theorem; D058872:Epidemics / Q000706:statistics & numerical data*; D005544:Forecasting; D019142:Hemorrhagic Fever, Ebola / Q000453:epidemiology* / Q000635:transmission*; D006801:Humans; D015994:Incidence; D007988:Liberia / Q000453:epidemiology; D015233:Models, Statistical; D013995:Time
Bayesian inference; Ebola; Forecasting; Mathematical modeling