Real-time parameter estimation of Zika outbreaks using model averaging

Abstract

Early prediction of the final size of any epidemic and in particular for Zika disease outbreaks can be useful for health authorities in order to plan the response to the outbreak. The Richards model is often been used to estimate epidemiological parameters for arboviral diseases based on the reported cumulative cases in single-and multi-wave outbreaks. However, other non-linear models can also fit the data as well. Typically, one follows the so called post selection estimation procedure, i.e., selects the best fitting model out of the set of candidate models and ignores the model uncertainty in both estimation and inference since these procedures are based on a single model. In this paper we focus on the estimation of the final size and the turning point of the epidemic and conduct a real-time prediction for the final size of the outbreak using several nonlinear models in which these parameters are estimated via model averaging. The proposed method is applied to Zika outbreak data in four cities from Colombia, during the outbreak ocurred in 2015-2016.