Uncovering heterogeneous intercommunity disease transmission from neutral allele frequency time series

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Analysis shows disease transmission dynamics in communities using a hidden Markov model, indicating implications for intervention strategies.

Genome sequencing data reveals patterns of disease transmission across communities, demonstrating significant long-range interactions.
The hidden Markov model infers intercommunity transmission dynamics from allele frequency convergence between different locations.
SARS-CoV-2 sequencing data from the US and England indicates that geographical relationships influence transmission networks.
This approach highlights the importance of time series data for understanding epidemiological interactions and improving intervention strategies.

Okada et al. (2025) studied this question.

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Analysis shows disease transmission dynamics in communities using a hidden Markov model, indicating implications for intervention strategies.