School of Statistics, Renmin University of China, Beijing, China.
PLoS One. 2021 Mar 25;16(3):e0249037. doi: 10.1371/journal.pone.0249037. eCollection 2021.
The novel coronavirus (COVID-19) that was first reported at the end of 2019 has impacted almost every aspect of life as we know it. This paper focuses on the incidence of the disease in Italy and Spain-two of the first and most affected European countries. Using two simple mathematical epidemiological models-the Susceptible-Infectious-Recovered model and the log-linear regression model, we model the daily and cumulative incidence of COVID-19 in the two countries during the early stage of the outbreak, and compute estimates for basic measures of the infectiousness of the disease including the basic reproduction number, growth rate, and doubling time. Estimates of the basic reproduction number were found to be larger than 1 in both countries, with values being between 2 and 3 for Italy, and 2.5 and 4 for Spain. Estimates were also computed for the more dynamic effective reproduction number, which showed that since the first cases were confirmed in the respective countries the severity has generally been decreasing. The predictive ability of the log-linear regression model was found to give a better fit and simple estimates of the daily incidence for both countries were computed.
新型冠状病毒（COVID-19）于 2019 年末首次报告，几乎影响了我们所知道的生活的方方面面。本文关注的是意大利和西班牙这两个最早和受影响最严重的欧洲国家的疾病发病率。我们使用了两种简单的数学传染病模型——易感-感染-恢复模型和对数线性回归模型，对这两个国家在疫情早期 COVID-19 的日发病率和累计发病率进行建模，并计算了疾病传染性的基本指标的估计值，包括基本繁殖数、增长率和倍增时间。在这两个国家，基本繁殖数的估计值都大于 1，意大利的值在 2 到 3 之间，西班牙的值在 2.5 到 4 之间。还计算了更具动态性的有效繁殖数的估计值，表明自各自国家首次确诊病例以来，严重程度总体上一直在下降。对数线性回归模型的预测能力被发现具有更好的拟合度，并且为这两个国家计算了简单的日发病率估计值。
