Founder effects arising from gathering dynamics systematically bias emerging pathogen surveillance

Abstract

Understanding how epidemics spread within societies is key for establishing adequate infection control responses. Dynamical models provide a means to translate surveillance data into predictions of future disease spread, yet many epidemic models do not capture empirically observed features of socialization. Here, we utilize a connection between sampling processes and their macroscopic equations to build an epidemic model that incorporates transmission heterogeneity, homophily, and awareness of the risk of infection. Our risk-SIR model captures how diseases tend to spread initially within more social subpopulations and sequentially spread to less social subpopulations. This dynamic complicates surveillance as many metrics become dynamic when comorbidities vary with socialization. We also show fast-growing lineages that emerge within highly social subpopulations can be misidentified as potential variants of concern, and that these lineages will transiently grow in frequency as they remain distributed among more social individuals. Together, we provide a simple framework for analysis of how socialization affects disease emergence and spread, and argue that this is necessary to contextualize epidemiologic parameters like the basic and time varying reproductive numbers based on sampling and stage of the outbreak.

Leveraging global genomic sequencing data to estimate local variant dynamics

“…However, these mechanisms are unlikely to completely explain the consistency in the direction of the bias across the observed lineages and settings (Figures S6, S8, and S9). While beyond the scope of our method, we hypothesize that additional mechanisms may further drive these observed patterns such as network effects like crowding 50 and preferential infection in highly social subgroups 51 .…”