Sorting out assortativity: when can we assess the contributions of different population groups to epidemic transmission?

Characterising the transmission dynamics between various population groups is critical for implementing effective outbreak control measures whilst minimising financial costs and societal disruption. Traditionally, mathematical models have primarily relied on assumptions of contact patterns to characterise transmission between groups. Thanks to technological and methodological advances, transmission chain data is increasingly available, providing information about individual-level transmission. However, it remains unclear how effectively and under what conditions such data can inform on transmission patterns between groups. In this paper, we introduce a novel metric that leverages transmission chain data to estimate group transmission assortativity; this quantifies the extent to which individuals transmit within their own group compared to others. Through extensive simulations, we assessed the conditions under which our estimator performs effectively and established guidelines for minimal data requirements. Notably, we demonstrate that detecting and quantifying transmission assortativity is most reliable when groups have reached their epidemic peaks, consist of at least 30 cases each, and represent at least 10% of the total population each.