Dynamics of Zika virus in Brazil: model calibration and uncertainty quantification
Résumé
In the last two decades multiple instances of Zika virus epidemic have been reported around the world, turning the related disease into a international concern. In this context, the use of mathematical models for epidemics is of great importance, since they are useful tools to predict the outbreaks underlying numbers, and allow one to test the effectiveness of different strategies to combat associated diseases. This work deals with the development and calibration of an epidemic model to describe Zika virus outbreak in Brazil, as well as the modeling and quantification of uncertainties on the initial conditions of the underlying dynamical system. In order to calibrate the model, an inverse problem is formulated and solved. A consistent stochastic model of uncertainties is constructed by means of a parametric probabilistic approach, which employs maximum entropy principle to specify the random parameters distributions and Monte Carlo method to compute the propagation of uncertainties.