Quantifying individual-level heterogeneity in infectiousness and susceptibility through household studies
Thayer L. Anderson,
Anjalika Nande,
Carter Merenstein,
Brinkley Raynor,
Anisha Oommen,
Brendan J. Kelly,
Michael Z. Levy,
Alison L. Hill
Affiliations
Thayer L. Anderson
Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States of America
Anjalika Nande
Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States of America
Carter Merenstein
Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
Brinkley Raynor
Department of Biostatistics, Epidemiology, & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
Anisha Oommen
Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States of America; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States of America
Brendan J. Kelly
Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America; Department of Biostatistics, Epidemiology, & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America; Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
Michael Z. Levy
Department of Biostatistics, Epidemiology, & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
Alison L. Hill
Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States of America; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States of America; Corresponding author at: Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States of America.
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 given 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.
