Abstract
BackgroundCommon cold viruses create significant health and financial burdens, and understanding key loci of transmission would help focus control strategies. This study (1) examines factors that influence when individuals transition from a negative to positive test (acquisition) or a positive to negative test (loss) of rhinovirus (HRV) and other respiratory tract viruses in 26 households followed weekly for one year, (2) investigates evidence for intrahousehold and interhousehold transmission and the characteristics of individuals implicated in transmission, and (3) builds data-based simulation models to identify factors that most strongly affect patterns of prevalence.MethodsWe detected HRV, coronavirus, paramyxovirus, influenza and bocavirus with the FilmArray polymerase chain reaction (PCR) platform (BioFire Diagnostics, LLC). We used logistic regression to find covariates affecting acquisition or loss of HRV including demographic characteristics of individuals, their household, their current infection status, and prevalence within their household and across the population. We apply generalized linear mixed models to test robustness of results.ResultsAcquisition of HRV was less probable in older individuals and those infected with a coronavirus, and higher with a higher proportion of other household members infected. Loss of HRV is reduced with a higher proportion of other household members infected. Within households, only children and symptomatic individuals show evidence for transmission, while between households only a higher number of infected older children (ages 5-19) increases the probability of acquisition. Coronaviruses, paramyxoviruses and bocavirus also show evidence of intrahousehold transmission. Simulations show that age-dependent susceptibility and transmission have the largest effects on mean HRV prevalence.ConclusionsChildren are most likely to acquire and most likely to transmit HRV both within and between households, with infectiousness concentrated in symptomatic children. Simulations predict that the spread of HRV and other respiratory tract viruses can be reduced but not eliminated by practices within the home.
Highlights
The spread, prevalence and persistence of infectious diseases depends on the heterogeneity in the host population
This study (1) examines factors that influence when individuals transition from a negative to positive test or a positive to negative test of rhinovirus (HRV) and other respiratory tract viruses in 26 households followed weekly for one year, (2) investigates evidence for intrahousehold and interhousehold transmission and the characteristics of individuals implicated in transmission, and (3) builds data-based simulation models to identify factors that most strongly affect patterns of prevalence
Acquisition of human rhinovirus (HRV) was less probable in older individuals and those infected with a coronavirus, and higher with a higher proportion of other household members infected
Summary
The spread, prevalence and persistence of infectious diseases depends on the heterogeneity in the host population This heterogeneity manifests as differences in host susceptibility, infectiousness, contact patterns, and duration of infection. This study uses longitudinal data on a set of Utah households to detect host heterogeneity in susceptibility, transmission potential, and duration of rhinovirus and other respiratory tract infections. These infections are probably the most common symptomatic infections experienced by people in developed nations [1, 2], with human rhinovirus (HRV) generally the most common cause both in North America [1, 3, 4] and in the tropics and the southern hemisphere [5]. This study (1) examines factors that influence when individuals transition from a negative to positive test (acquisition) or a positive to negative test (loss) of rhinovirus (HRV) and other respiratory tract viruses in 26 households followed weekly for one year, (2) investigates evidence for intrahousehold and interhousehold transmission and the characteristics of individuals implicated in transmission, and (3) builds data-based simulation models to identify factors that most strongly affect patterns of prevalence
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