Disentangling the drivers of heterogeneity in SARS-CoV-2 transmission from data on viral load and daily contact rates

SARS-CoV-2 spreads predominantly through superspreading, with a minority of individuals responsible for the majority of transmission, though the drivers of this heterogeneity are unclear. Here, we assess the contribution of variation in viral load and daily contact rates to this heterogeneity by combining viral load and contact survey data in a mathematical model to estimate the secondary infection distribution. Using data from the BBC Pandemic and CoMix contact surveys, we estimate the basic reproduction number ( R ₀ = 2.2, 95% CI 2.1-2.2) from first principles and the secondary infection distribution throughout the pandemic in the UK in 2020, and the effectiveness of frequent and pre-event rapid testing for reducing superspreading events. We find that individual heterogeneity in contacts – rather than individual heterogeneity in shedding – drives observed heterogeneity in the secondary infection distribution. Our results suggest that regular testing every 3 days, or pre-event testing with a minimum event size of 10, could reduce the mean reproduction number below 1 with moderate to high levels of uptake (60-80%) for pre-pandemic contact levels. This work demonstrates the potential for using viral load and contact data to predict heterogeneity in transmission and the effectiveness of rapid testing strategies for curbing transmission in future pandemics.