Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States of America.
Department of Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, United States of America.
BMC Public Health. 2024 Oct 25;24(1):2965. doi: 10.1186/s12889-024-20308-z.
Understanding how infectious disease transmission varies from person to person, including associations with age and contact behavior, can help design effective control strategies. Within households, transmission may be highly variable because of differing transmission risks by age, household size, and individual contagiousness. Our aim was to disentangle those factors by fitting mathematical models to SARS-CoV-2 household survey and serologic data.
We surveyed members of 3,381 Utah households from January-April 2021 and performed SARS-CoV-2 antibody testing on all available members. We paired these data with a probabilistic model of household importation and transmission composed of a novel combination of transmission variability and age- and size-structured heterogeneity. We calculated maximum likelihood estimates of mean and variability of household transmission probability between household members in different age groups and different household sizes, simultaneously with importation probability and probabilities of false negative and false positive test results.
12.8% of individual participants, residing in 17.4% of the participating households, showed serologic evidence of prior infection or reported a prior positive test on the survey. Serologically positive individuals in younger age groups were less likely than older adults to have tested positive during their infection according to our survey results. Our model results suggested that adolescents and young adults (ages 13-24) acquired SARS-CoV-2 infection outside the household at a rate substantially higher than younger children and older adults. Our estimate of the household secondary attack rate (HSAR) among adults aged 45 and older exceeded HSARs to and/or from younger age groups at a given household size. We found lower HSAR in households with more members, independent of age differences. The age-specific HSAR patterns we found could not be explained by age-dependent biological susceptibility and transmissibility alone, suggesting that age groups contacted each other at different rates within households.
We disentangled several factors contributing to age-specific infection risk, including non-household exposure, within-household exposure to specific age groups, and household size. Within-household contact rate differences played a significant role in driving household transmission epidemiology. These findings provide nuanced insights for understanding community outbreak patterns and mechanisms of differential infection risk.
了解传染病在人与人之间的传播方式，包括与年龄和接触行为的关联，有助于设计有效的控制策略。在家庭中，由于年龄、家庭规模和个体传染性的不同，传播可能存在高度的变异性。我们的目标是通过拟合数学模型来分离这些因素，这些模型基于 SARS-CoV-2 家庭调查和血清学数据。
我们于 2021 年 1 月至 4 月对犹他州的 3381 户家庭的成员进行了调查，并对所有可用成员进行了 SARS-CoV-2 抗体检测。我们将这些数据与一个家庭输入和传播的概率模型进行配对，该模型由一个新颖的组合组成，包括传播变异性和年龄和大小结构异质性。我们同时计算了不同年龄组和不同家庭规模的家庭成员之间家庭传播概率的均值和变异性、输入概率以及假阴性和假阳性测试结果的概率的最大似然估计。
12.8%的个体参与者，居住在 17.4%的参与家庭中，显示出先前感染的血清学证据或在调查中报告了先前的阳性检测结果。根据我们的调查结果，年轻年龄组的血清学阳性个体在感染期间检测为阳性的可能性低于老年人。我们的模型结果表明，青少年和年轻人（13-24 岁）在家庭外感染 SARS-CoV-2 的速度明显高于年幼的儿童和老年人。我们对 45 岁及以上成年人的家庭二次攻击率（HSAR）的估计值超过了给定家庭规模下来自和/或到年轻年龄组的 HSAR。我们发现，家庭成员较多的家庭的 HSAR 较低，与年龄差异无关。我们发现的年龄特异性 HSAR 模式不能仅用年龄相关的生物学易感性和传染性来解释，这表明年龄组在家庭内以不同的速度相互接触。
我们分离了导致特定年龄组感染风险的几个因素，包括非家庭暴露、家庭内特定年龄组的暴露以及家庭规模。家庭内接触率的差异在驱动家庭传播流行病学方面发挥了重要作用。这些发现为理解社区暴发模式和不同感染风险的机制提供了细致入微的见解。
