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. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement CG is supported by a PhD studentship at Imperial College London funded by the National Institute for Health Research (NIHR) Health Protection Research Unit (HPRU) in Modelling and Health Economics, which is a partnership between the UK Health Security Agency (UKHSA), Imperial College London, and the London School of Hygiene & Tropical Medicine (grant code NIHR200908). AC, PJW are supported by the HPRU in Modelling and Health Economics. This work was supported by the UK Medical Research Council (MRC) Centre for Global Infectious Disease Analysis (grant number MR/X020258/1); this award comes under the Global Health EDCTP3 Joint Undertaking. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The analysis code is freely available on a GitHub repository: https://github.com/CyGei/o2groups-analysis. An R package has been developed for simulating outbreak scenarios and is also available on GitHub at: https://github.com/CyGei/o2groups. Package and analysis code have been archived on Zenodo ( analysis: https://zenodo.org/doi/10.5281/zenodo.10616176, package: https://zenodo.org/doi/10.5281/zenodo.10616155)