Departamento de Física, Universidade Federal do Paraná, 81531-980 Curitiba, PR, Brazil.
Escola Normal Superior, Universidade do Estado do Amazonas, 69050-010 Manaus, AM, Brazil.
Phys Biol. 2021 Feb 20;18(2):025002. doi: 10.1088/1478-3975/abd0dc.
After the spread of COVID-19 out of China, the evolution of the pandemic has shown remarkable similarities and differences between countries around the world. Eventually, such characteristics are also observed between different regions of the same country. Herewith, we introduce a general method that allows us to compare the evolution of the pandemic in different localities inside a large territorial country: in the case of the present study, Brazil. To evaluate our method, we study the heterogeneous spreading of the COVID-19 outbreak until May 30th, 2020, in Brazil and its 27 federative units, which has been seen as the current epicenter of the pandemic in South America. Each one of the federative units may be considered a cluster of interacting people with similar habits and distributed to a highly heterogeneous demographic density over the entire country. Our first set of results regarding the time-series analysis shows that: (i) a power-law growth of the cumulative number of infected people is observed for federative units of the five regions of Brazil; and (ii) the distance correlation calculated between the time series of the most affected federative units and the curve that describes the evolution of the pandemic in Brazil remains about 1 over most of the time, while such quantity calculated for the federative units with a low incidence of newly infected people remains about 0.95. In the second set of results, we focus on the heterogeneous distribution of the confirmed cases and deaths. By applying the epidemiological susceptible-infected-recovered-dead model we estimated the effective reproduction number (ERN) [Formula: see text] during the pandemic evolution and found that: (i) the mean value of [Formula: see text] for the eight most affected federative units in Brazil is about 2; (ii) the current value of [Formula: see text] for Brazil is greater than 1, which indicates that the epidemic peak is far off; and (iii) Ceará was the only federative unit for which the current [Formula: see text]. Based on these findings, we projected the effects of increase or decrease of the ERN and concluded that if the value of [Formula: see text] increases 20%, not only the peak might grow at least 40% but also its occurrence might be anticipated, which hastens the collapse of the public health-care system. In all cases, keeping the ERN 20% below the current value can save thousands of people in the long term.
在中国境外 COVID-19 传播之后,全球各国的疫情演变表现出显著的相似性和差异性。最终,在同一国家的不同地区也观察到了这些特征。在此,我们介绍一种通用方法,可用于比较大型国家内部不同地区的疫情演变:在本研究中,使用的国家是巴西。为了评估该方法,我们研究了截至 2020 年 5 月 30 日,巴西及其 27 个联邦单位的 COVID-19 爆发的异质传播情况,巴西目前被认为是南美洲疫情的中心。每个联邦单位都可以被视为具有相似习惯的相互作用人群的聚类,并分布在全国高度异质的人口密度中。我们对时间序列分析的第一组结果表明:(i)观察到 5 个地区的巴西联邦单位的感染人数累积呈幂律增长;(ii)计算最受影响的联邦单位的时间序列与描述巴西疫情演变的曲线之间的距离相关系数在大部分时间内保持在 1 左右,而对于新感染人数低的联邦单位,该数量保持在 0.95 左右。在第二组结果中,我们重点研究了确诊病例和死亡人数的异质分布。通过应用流行病学易感染-感染-恢复-死亡模型,我们估计了疫情演变过程中的有效繁殖数(ERN)[公式:见正文],发现:(i)巴西 8 个受影响最严重的联邦单位的 [公式:见正文]平均值约为 2;(ii)巴西目前的 [公式:见正文]大于 1,这表明疫情高峰期还远未到来;(iii)塞阿拉州是唯一的联邦单位,目前的 [公式:见正文]。基于这些发现,我们预测了 ERN 增加或减少的影响,并得出结论,如果 [公式:见正文]增加 20%,不仅高峰期可能至少增长 40%,而且高峰期可能提前到来,从而加速公共卫生保健系统的崩溃。在所有情况下,长期来看,将 ERN 保持在当前值的 20%以下都可以挽救数千人的生命。