Model-based estimates of age-structured SARS-CoV-2 epidemiology in households
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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 the individual participants showed serologic evidence of prior infection or reported a prior positive test on the survey, and 17.4% of the participating households showed evidence of at least one SARS-CoV-2 importation. 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. We found lower HSAR in households with more members, independent of age differences. Our findings from age-structured transmission analysis suggest that age groups contact each other at different rates within households, a key insight for understanding community outbreak patterns and mechanisms of differential infection risk.
Author Summary
Infectious diseases can spread through human communities in irregular patterns, partly because different demographic groups, such as age groups, experience different transmission risks due to contact or other behavioral or physiological differences. Understanding the factors driving age differences in transmission can help predict patterns of disease spread and suggest efficient public health strategies to mitigate outbreaks. Households are inter-age mixing locations where age differences in transmission can be studied. In early 2021, we collected blood samples from all members of thousands of households in Utah and tested them for SARS-CoV-2 antibodies, from which prior COVID-19 infection can be inferred. We paired these data with mathematical models that quantify probabilities that different combinations of household members end up infected for different assumptions about non-household infection and within-household transmission. Our estimates suggest that adolescents and young adults acquired infection outside the household more frequently than did other age groups. After a household importation occurred, middle-aged and older adults living together transmitted to each other more readily than all other age pairings for a given household size. The age patterns of household transmission we found suggest that within-household contact rate differences play a significant role in driving household transmission epidemiology.
