Assessing the Time Evolution of COVID-19 Effective Reproduction Number in Brazil

Abstract

In this paper, we use a Bayesian method to estimate the effective reproduction number ($R\left(t\right)$), in the context of monitoring the time evolution of the COVID-19 pandemic in Brazil at different geographic levels. The focus of this study is to investigate the similarities between the trends in the evolution of such indicators at different subnational levels with the trends observed nationally. The underlying question addressed is whether national surveillance of such variables is enough to provide a picture of the epidemic evolution in the country or if it may hide important localized trends. This is particularly relevant in the scenario where health authorities use information obtained from such indicators in the design of non-pharmaceutical intervention policies to control the epidemic. A comparison between $R\left(t\right)$ estimates and the moving average (MA) of daily reported infections is also presented, which is another commonly monitored variable. The analysis carried out in this paper is based on the data of confirmed infected cases provided by a public repository. The correlations between the time series of $R\left(t\right)$ and MA in different geographic levels are assessed. Comparing national with subnational trends, higher degrees of correlation are found for the time series of $R\left(t\right)$ estimates, compared to the MA time series. Nevertheless, differences between national and subnational trends are observed for both indicators, suggesting that local epidemiological surveillance would be more suitable as an input to the design of non-pharmaceutical intervention policies in Brazil, particularly for the least populated states.

Key words
COVID-19; reproduction numbers; epidemiological models; Bayesian estimation