National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, United States of America.
PLoS One. 2021 Jul 13;16(7):e0254145. doi: 10.1371/journal.pone.0254145. eCollection 2021.
In a compartmental epidemic model, the initial exponential phase reflects a fixed interaction between an infectious agent and a susceptible population in steady state, so it determines the basic reproduction number R0 on its own. After the exponential phase, dynamic complexities like societal responses muddy the practical interpretation of many estimated parameters. The computer program ARRP, already available from sequence alignment applications, automatically estimated the end of the exponential phase in COVID-19 and extracted the exponential growth rate r for 160 countries. By positing a gamma-distributed generation time, the exponential growth method then yielded R0 estimates for COVID-19 in 160 countries. The use of ARRP ensured that the R0 estimates were largely freed from any dependency outside the exponential phase. The Prem matrices quantify rates of effective contact for infectious disease. Without using any age-stratified COVID-19 data, but under strong assumptions about the homogeneity of susceptibility, infectiousness, etc., across different age-groups, the Prem contact matrices also yielded theoretical R0 estimates for COVID-19 in 152 countries, generally in quantitative conflict with the R0 estimates derived from the exponential growth method. An exploratory analysis manipulating only the Prem contact matrices reduced the conflict, suggesting that age-groups under 20 years did not promote the initial exponential growth of COVID-19 as much as other age-groups. The analysis therefore supports tentatively and tardily, but independently of age-stratified COVID-19 data, the low priority given to vaccinating younger age groups. It also supports the judicious reopening of schools. The exploratory analysis also supports the possibility of suspecting differences in epidemic spread among different age-groups, even before substantial amounts of age-stratified data become available.
在隔室传染病模型中，初始指数阶段反映了感染源与稳定状态下易感人群之间的固定相互作用，因此它本身就决定了基本繁殖数 R0。在指数阶段之后，像社会反应这样的动态复杂性会使许多估计参数的实际解释变得复杂。已经可以从序列比对应用程序中获得的计算机程序 ARRP 自动估计了 COVID-19 的指数阶段结束，并为 160 个国家提取了指数增长率 r。通过假设伽马分布的生成时间，指数增长方法为 160 个国家的 COVID-19 产生了 R0 估计值。使用 ARRP 确保了 R0 估计值在很大程度上不受指数阶段之外的任何因素的影响。Prem 矩阵量化了传染病的有效接触率。Prem 接触矩阵在不使用任何分层 COVID-19 数据的情况下，根据不同年龄组之间的易感性、传染性等的同质性做出强烈假设，也为 152 个国家的 COVID-19 产生了理论 R0 估计值，这些值通常与从指数增长方法得出的 R0 估计值存在定量冲突。仅操作 Prem 接触矩阵的探索性分析减少了冲突，这表明 20 岁以下年龄组并没有像其他年龄组那样促进 COVID-19 的初始指数增长。因此，该分析在没有分层 COVID-19 数据的情况下，支持了对年轻年龄组接种疫苗的优先级较低的观点。它还支持学校的谨慎重新开放。探索性分析还支持在获得大量分层年龄数据之前，怀疑不同年龄组之间的疫情传播存在差异的可能性。
