Two approaches to forecast Ebola synthetic epidemics.
We use two modelling approaches to forecast synthetic Ebola epidemics in the context of the RAPIDD Ebola Forecasting Challenge. The first approach is a standard stochastic compartmental model that aims to forecast incidence, hospitalization and deaths among both the general population and health care workers. The second is a model based on the renewal equation with latent variables that forecasts incidence in the whole population only. We describe fitting and forecasting procedures for each model and discuss their advantages and drawbacks. We did not find that one model was consistently better in forecasting than the other.
McMaster University, Hamilton, Ontario, Canada; York University, Toronto, Ontario, Canada. Electronic address: david.champredon@gmail.com.
David
D
Champredon
McMaster University, Hamilton, Ontario, Canada.
Michael
M
Li
McMaster University, Hamilton, Ontario, Canada.
Benjamin M
BM
Bolker
McMaster University, Hamilton, Ontario, Canada.
Jonathan
J
Dushoff
D001499:Bayes Theorem; D058872:Epidemics / Q000706:statistics & numerical data*; D005544:Forecasting; D019142:Hemorrhagic Fever, Ebola / Q000453:epidemiology*; D006801:Humans; D015994:Incidence; D015233:Models, Statistical; D009010:Monte Carlo Method
Compartmental model; Epidemics; Forecasting; Renewal equation