Granger causality and time series regression for modelling the migratory dynamics of infuenza into Brazil

Abstract

In this work we study the problem of modelling and forecasting the dynamics of the infuenza virus in Brazil at a given month, from data on reported cases and genetic diversity collected from previous months, in other locations. Granger causality is employed as a tool to assess possible predictive relationships between covariates. For modelling and forecasting purposes, a time series regression approach is applied considering lagged information regarding reported cases and genetic diversity in other regions. Three different models are analysed, including stepwise time series regression and LASSO.