Quantifying individual-level heterogeneity in infectiousness and susceptibility through household studies

Abstract

The spread of SARS-CoV-2, like that of many other pathogens, is governed by heterogeneity. "Superspreading," or "over-dispersion," is an important factor in transmission, yet it is hard to quantify. Estimates from contact tracing data are prone to potential biases due to the increased likelihood of detecting large clusters of cases, and may reflect variation in contact behavior more than biological heterogeneity. In contrast, the average number of secondary infections per contact is routinely estimated from household surveys, and these studies can minimize biases by testing all members of a household. However, the models used to analyze household transmission data typically assume that infectiousness and susceptibility are the same for all individuals or vary only with predetermined traits such as age. Here we develop and apply a combined forward simulation and inference method to quantify the degree of inter-individual variation in both infectiousness and susceptibility from observations of the distribution of infections in household surveys. First, analyzing simulated data, we show our method can reliably ascertain the presence, type, and amount of these heterogeneities with data from a sufficiently large sample of households. We then analyze a collection of household studies of COVID-19 from diverse settings around the world, and find strong evidence for large heterogeneity in both the infectiousness and susceptibility of individuals. Our results also provide a framework to improve the design of studies to evaluate household interventions in the presence of realistic heterogeneity between individuals.

Detecting and quantifying heterogeneity in susceptibility using contact tracing data

“…Our method estimates heterogeneity in underlying susceptibility, and the remaining heterogeneity in contact rate can be determined from the contact network data. Anderson et al (2023) used household study data to estimate heterogeneity in susceptibility. While this method is suitable for use in real time, and can be applied to human infectious diseases, the method notably is designed to estimate heterogeneity within households, which is not the same as the population-level heterogeneity that drives population-level disease dynamics.…”

“…Gomes et al (2022) also used curve fitting with mortality data that could be implemented once at least four months of data were available, but their method is heavily dependent on the underlying model and assumptions. With the recent increased interest in real-time estimation, Anderson et al (2023) developed a method to estimate within-household heterogeneity in susceptibility, but this is not the same as the population-level heterogeneity that drives population-level disease dynamics. Here we develop a novel method to identify and quantify host heterogeneity in susceptibility using contact tracing data, which can be collected early in an epidemic.…”