Estimating the reproduction number and transmission heterogeneity from the size distribution of clusters of identical pathogen sequences

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Abstract

Quantifying transmission intensity and heterogeneity is crucial to ascertain the threat posed by infectious diseases and inform the design of interventions. Methods that jointly estimate the reproduction number R and the dispersion parameter k have however mainly remained limited to the analysis of epidemiological clusters or contact tracing data, whose collection often proves difficult. Here, we show that clusters of identical sequences are imprinted by the pathogen offspring distribution, and we derive an analytical formula for the distribution of the size of these clusters. We develop and evaluate an inference framework to jointly estimate the reproduction number and the dispersion parameter from the size distribution of clusters of identical sequences. We then illustrate its application across a range of epidemiological situations. Finally, we develop a hypothesis testing framework relying on clusters of identical sequences to determine whether a given pathogen genetic subpopulation is associated with increased or reduced transmissibility. Our work provides tools to estimate the reproduction number and transmission heterogeneity from pathogen sequences without building a phylogenetic tree, thus making it easily scalable to large pathogen genome datasets.

Article activity feed

  1. Jessica Stockdale, Shabnam Molan

    Review 5: "Estimating the Reproduction Number and Transmission Heterogeneity from the Size Distribution of Clusters of Identical Pathogen Sequences"

    Reviewers find the proposed method to be novel and validated with synthetic and historical epidemic data. However, they expressed concerns about the uncertainty in quantifying the magnitude of the estimation bias and the validity of this method in the case of an outbreak.

  2. Satyaki Roy

    Review 4: "Estimating the Reproduction Number and Transmission Heterogeneity from the Size Distribution of Clusters of Identical Pathogen Sequences"

    Reviewers find the proposed method to be novel and validated with synthetic and historical epidemic data. However, they expressed concerns about the uncertainty in quantifying the magnitude of the estimation bias and the validity of this method in the case of an outbreak.

  3. Max Lau

    Review 3: "Estimating the Reproduction Number and Transmission Heterogeneity from the Size Distribution of Clusters of Identical Pathogen Sequences"

    Reviewers find the proposed method to be novel and validated with synthetic and historical epidemic data. However, they expressed concerns about the uncertainty in quantifying the magnitude of the estimation bias and the validity of this method in the case of an outbreak.

  4. Laura White

    Review 2: "Estimating the Reproduction Number and Transmission Heterogeneity from the Size Distribution of Clusters of Identical Pathogen Sequences"

    Reviewers find the proposed method to be novel and validated with synthetic and historical epidemic data. However, they expressed concerns about the uncertainty in quantifying the magnitude of the estimation bias and the validity of this method in the case of an outbreak.

  5. Yunjun Zhang

    Review 1: "Estimating the Reproduction Number and Transmission Heterogeneity from the Size Distribution of Clusters of Identical Pathogen Sequences"

    Reviewers find the proposed method to be novel and validated with synthetic and historical epidemic data. However, they expressed concerns about the uncertainty in quantifying the magnitude of the estimation bias and the validity of this method in the case of an outbreak.

  6. Strength of evidence

    Reviewers: Y Zhang (Peking University) | πŸ“’πŸ“’πŸ“’ ◻️◻️
    L White (Boston University) | πŸ“—πŸ“—πŸ“—πŸ“—β—»οΈ
    M Lau (Emory University) | πŸ“’πŸ“’πŸ“’β—»οΈβ—»οΈ
    S Roy (University of Alabama in Huntsville) | πŸ“’πŸ“’πŸ“’β—»οΈβ—»οΈ
    J Stockdale & S Molan (Simon Fraser University) | πŸ“—πŸ“—πŸ“—πŸ“—β—»οΈ