Centre for Diseases Modeling (CDM), York University, Toronto, ON, Canada.
Department of Mathematics and Statistics, York University, Toronto, ON, Canada.
PLoS One. 2021 Jun 9;16(6):e0252373. doi: 10.1371/journal.pone.0252373. eCollection 2021.
To assess whether the basic reproduction number (R0) of COVID-19 is different across countries and what national-level demographic, social, and environmental factors other than interventions characterize initial vulnerability to the virus.
We fit logistic growth curves to reported daily case numbers, up to the first epidemic peak, for 58 countries for which 16 explanatory covariates are available. This fitting has been shown to robustly estimate R0 from the specified period. We then use a generalized additive model (GAM) to discern both linear and nonlinear effects, and include 5 random effect covariates to account for potential differences in testing and reporting that can bias the estimated R0.
We found that the mean R0 is 1.70 (S.D. 0.57), with a range between 1.10 (Ghana) and 3.52 (South Korea). We identified four factors-population between 20-34 years old (youth), population residing in urban agglomerates over 1 million (city), social media use to organize offline action (social media), and GINI income inequality-as having strong relationships with R0, across countries. An intermediate level of youth and GINI inequality are associated with high R0, (n-shape relationships), while high city population and high social media use are associated with high R0. Pollution, temperature, and humidity did not have strong relationships with R0 but were positive.
Countries have different characteristics that predispose them to greater intrinsic vulnerability to COVID-19. Studies that aim to measure the effectiveness of interventions across locations should account for these baseline differences in social and demographic characteristics.
评估 COVID-19 的基本繁殖数（R0）在各国之间是否存在差异，以及除干预措施之外，哪些国家层面的人口、社会和环境因素是导致病毒初始易感性的特征。
我们对 58 个国家/地区的报告日病例数进行了逻辑增长曲线拟合，拟合截至首次疫情高峰。这一拟合方法已被证明可以从指定时期稳健地估计 R0。然后，我们使用广义相加模型（GAM）来辨别线性和非线性效应，并包含 5 个随机效应协变量，以解释可能导致估计的 R0 存在偏差的检测和报告方面的潜在差异。
我们发现，平均 R0 为 1.70（标准差 0.57），范围在 1.10（加纳）至 3.52（韩国）之间。我们发现了四个因素与 R0 具有很强的关系：20-34 岁之间的人口（青年）、人口居住在超过 100 万的城市（城市）、利用社交媒体组织线下活动（社交媒体）和基尼收入不平等（基尼系数）。青年人口和基尼系数处于中等水平与高 R0 相关（n 形关系），而城市人口和社交媒体使用水平高与高 R0 相关。污染、温度和湿度与 R0 没有很强的关系，但呈正相关。
各国具有不同的特征，使其对 COVID-19 具有更大的内在易感性。旨在衡量不同地点干预措施有效性的研究应该考虑到这些社会和人口特征的基线差异。
