National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.
National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.
Epidemics. 2023 Sep;44:100714. doi: 10.1016/j.epidem.2023.100714. Epub 2023 Aug 15.
In a pending pandemic, early knowledge of age-specific disease parameters, e.g., susceptibility, infectivity, and the clinical fraction (the fraction of infections coming to clinical attention), supports targeted public health responses like school closures or sequestration of the elderly. The earlier the knowledge, the more useful it is, so the present article examines an early phase of many epidemics, exponential growth. Using age-stratified COVID-19 case counts collected in Canada, China, Israel, Italy, the Netherlands, and the United Kingdom before April 23, 2020, we present a linear analysis of the exponential phase that attempts to estimate the age-specific disease parameters given above. Some combinations of the parameters can be estimated by requiring that they change smoothly with age. The estimation yielded: (1) the case susceptibility, defined for each age-group as the product of susceptibility to infection and the clinical fraction; (2) the mean number of transmissions of infection per contact within each age-group; and (3) the reproduction number of infection within each age-group, i.e., the diagonal of the age-stratified next-generation matrix. Our restriction to data from the exponential phase indicates the combinations of epidemic parameters that are intrinsically easiest to estimate with early age-stratified case counts. For example, conclusions concerning the age-dependence of case susceptibility appeared more robust than corresponding conclusions about infectivity. Generally, the analysis produced some results consistent with conclusions confirmed much later in the COVID-19 pandemic. Notably, our analysis showed that in some countries, the reproduction number of infection within the half-decade 70-75 was unusually large compared to other half-decades. Our analysis therefore could have anticipated that without countermeasures, COVID-19 would spread rapidly once seeded in homes for the elderly.
在即将发生的大流行中，尽早了解特定年龄段的疾病参数，例如易感性、传染性和临床比例（发病比例），有助于采取有针对性的公共卫生措施，例如关闭学校或隔离老年人。了解得越早，就越有用，因此本文研究了许多流行病的早期阶段，即指数增长阶段。利用 2020 年 4 月 23 日之前在加拿大、中国、以色列、意大利、荷兰和英国收集的按年龄分层的 COVID-19 病例数，我们对指数增长阶段进行了线性分析，试图估计上述特定年龄段的疾病参数。通过要求参数平滑变化，可以估计出一些参数组合。估计结果为：（1）病例易感性，定义为每个年龄组的感染易感性和临床比例的乘积；（2）每个年龄组内每个接触者的平均感染传播数；（3）每个年龄组内的感染繁殖数，即年龄分层下一代矩阵的对角线。我们将限制在指数阶段的数据，这表明了通过早期按年龄分层的病例数最容易估计的组合。例如，关于病例易感性的年龄依赖性的结论似乎比关于传染性的相应结论更可靠。一般来说，该分析产生了一些与在 COVID-19 大流行后期证实的结论一致的结果。值得注意的是，我们的分析表明，在一些国家，70-75 岁年龄段的感染繁殖数与其他年龄段相比异常大。因此，我们的分析本可以预测，如果没有对策，COVID-19 一旦在老年人的家中传播，就会迅速蔓延。
