Robust trend estimation for COVID-19 in Brazil

Estimating patterns of occurrence of cases and deaths related to the COVID-19 pandemic is a complex problem. The incidence of cases presents a great spatial and temporal heterogeneity, and the mechanisms of accounting for occurrences adopted by health departments induce a process of measurement error that alters the dependence structure of the process. In this work we propose methods to estimate the trend in the cases of COVID-19, controlling for the presence of measurement error. This decomposition is presented in Bayesian time series and spatio-temporal models for counting processes with latent components, and compared to the empirical analysis based on moving averages. We applied time series decompositions for the total number of deaths in Brazil and for the states of São Paulo and Amazonas, and a spatio-temporal analysis for all occurrences of deaths at the state level in Brazil, using two alternative specifications with global and regional components.