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November 30, 2025Proceedings of the National Academy of SciencesOpen Access

Uncovering heterogeneous intercommunity disease transmission from neutral allele frequency time series

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Authors

TOTakashi OkadaGIGiulio IsacchiniQYQinQin Yu

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Overview

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

Key Points

  • 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.

Cite This Study

Okada et al. (2025) studied this question.

synapsesocial.com/papers/692b944c1d383f2b2a378befhttps://doi.org/10.1073/pnas.2500663122
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  1. 1Uncovering heterogeneous intercommunity disease transmission from neutral allele frequency time series2025
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