A stochastic nonlinear dynamic model for Zika virus outbreak in Brazil
Résumé
Several instances of Zika virus epidemic have been reported around the world in the last 20 years, causing Zika fever to become a disease of international concern. This work deals with the adaptation of a compartmental epidemic model to predict the evolution of Zika virus in Brazilian scenario and the posterior calibration of this predictive tool with respect to real data, from the recent outbreak of the disease, by solving an inverse problem. Model parameters variabilities are taken into account through parametric probabilistic approach, that employs an information-theoretic formalism (maximum entropy principle) to construct a consistent stochastic model and use Monte Carlo simulation for propagate the uncertainties. This development gives rise to a realistic epidemic model capable of making robust predictions about epidemic scenarios.