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Epidemiological inference for emerging viruses using segregating sites

Author

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  • Yeongseon Park

    (Emory University)

  • Michael A. Martin

    (Emory University
    Johns Hopkins University School of Medicine)

  • Katia Koelle

    (Emory University
    Emory Center of Excellence for Influenza Research and Response (CEIRR))

Abstract

Epidemiological models are commonly fit to case and pathogen sequence data to estimate parameters and to infer unobserved disease dynamics. Here, we present an inference approach based on sequence data that is well suited for model fitting early on during the expansion of a viral lineage. Our approach relies on a trajectory of segregating sites to infer epidemiological parameters within a Sequential Monte Carlo framework. Using simulated data, we first show that our approach accurately recovers key epidemiological quantities under a single-introduction scenario. We then apply our approach to SARS-CoV-2 sequence data from France, estimating a basic reproduction number of approximately 2.3-2.7 under an epidemiological model that allows for multiple introductions. Our approach presented here indicates that inference approaches that rely on simple population genetic summary statistics can be informative of epidemiological parameters and can be used for reconstructing infectious disease dynamics during the early expansion of a viral lineage.

Suggested Citation

  • Yeongseon Park & Michael A. Martin & Katia Koelle, 2023. "Epidemiological inference for emerging viruses using segregating sites," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-38809-7
    DOI: 10.1038/s41467-023-38809-7
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    References listed on IDEAS

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