In this paper, we use a Bayesian method to estimate the effective reproduction number (R(t)), 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(t) 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(t) 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(t) 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.

翻译：本文采用贝叶斯方法估计有效再生数（R(t)），以监测巴西不同地理层级COVID-19大流行的时间演化。本研究旨在探讨国家以下各级此类指标演化趋势与国家层面观测趋势之间的相似性。其核心问题是：此类变量的全国性监测是否足以描绘该国疫情演化全貌，抑或可能掩盖重要的局部趋势。这一问题在卫生当局利用此类指标设计非药物干预政策以控制疫情的情境下尤为关键。此外，本文还将R(t)估计值与每日报告感染数的移动平均值（MA）进行对比——后者是另一类常用监测变量。研究基于公共存储库提供的确诊病例数据开展，评估了不同地理层级R(t)与MA时间序列之间的相关性。相较于MA时间序列，R(t)估计值的时间序列在国家层面与次国家层面趋势间展现出更高相关性。然而，两类指标在国家与次国家层面均存在差异，这表明地方级流行病学监测更适合作为巴西非药物干预政策设计的输入依据，尤其对于人口最少的州而言。