Cribari-Neto Francisco
Department of Statistics, Federal University of Pernambuco, Recife, PE, 50670-901, Brazil.
Infect Dis Model. 2023 Jun;8(2):309-317. doi: 10.1016/j.idm.2023.02.005. Epub 2023 Mar 7.
Brazil was one of the countries most impacted by the COVID-19 pandemic, with a cumulative total of nearly 700,000 deaths by early 2023. The country's federative units were unevenly affected by the pandemic and adopted mitigation measures of different scopes and intensity. There was intense conflict between the federal government and state governments over the relevance and extent of such measures. We build a simple regression model with good predictive power on state COVID-19 mortality rates in Brazil. Our results reveal that the federative units' urbanization rate and per capita income are important for determining their mean mortality rate and that the number of physicians per 100,000 inhabitants is important for modeling the mortality rate precision. Based on the fitted model, we obtain approximations for the levels of administrative efficiency of local governments in dealing with the pandemic.
巴西是受新冠疫情影响最严重的国家之一,截至2023年初累计死亡人数近70万。该国的联邦单位受疫情影响程度不均衡,并采取了不同范围和强度的缓解措施。联邦政府和州政府在这些措施的相关性和范围上存在激烈冲突。我们构建了一个对巴西各州新冠死亡率具有良好预测能力的简单回归模型。我们的结果表明,联邦单位的城市化率和人均收入对于确定其平均死亡率很重要,而每10万居民中的医生数量对于模拟死亡率精度很重要。基于拟合模型,我们得到了地方政府应对疫情行政效率水平的近似值。