Uncovering heterogeneous intercommunity disease transmission from neutral allele frequency time series.

ABSTRACT: The COVID-19 pandemic has underscored the need for accurate epidemic forecasting to predict pathogen spread, evolution, and evaluate intervention strategies. Forecast reliability hinges on detailed knowledge of disease transmission across population segments, which may be inferred from contact surveys or mobility data. However, these indirect approaches make it difficult to estimate rare transmissions between socially or geographically distant communities. We show that the steep ramp-up of genome sequencing surveillance during the pandemic can be leveraged to directly identify transmission patterns between geographically defined communities. Our approach uses a hidden Markov model to infer the fraction of infections a community imports from others based on how rapidly allele frequencies in

SUBMITTER: Okada T 

PROVIDER: S-EPMC12684928 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature