Genomic epidemiology offers high resolution estimates of serial intervals for COVID-19

  1. Jessica E. Stockdale
  2. Kurnia Susvitasari
  3. Paul Tupper
  4. Benjamin Sobkowiak
  5. Nicola Mulberry
  6. Anders Gonçalves da Silva
  7. Anne E. Watt
  8. Norelle L. Sherry
  9. Corinna Minko
  10. Benjamin P. Howden
  11. Courtney R. Lane
  12. Caroline Colijn

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Abstract

Serial intervals – the time between symptom onset in infector and infectee – are a fundamental quantity in infectious disease control. However, their estimation requires knowledge of individuals’ exposures, typically obtained through resource-intensive contact tracing efforts. We introduce an alternate framework using virus sequences to inform who infected whom and thereby estimate serial intervals. We apply our technique to SARS-CoV-2 sequences from case clusters in the first two COVID-19 waves in Victoria, Australia. We find that our approach offers high resolution, cluster-specific serial interval estimates that are comparable with those obtained from contact data, despite requiring no knowledge of who infected whom and relying on incompletely-sampled data. Compared to a published serial interval, cluster-specific serial intervals can vary estimates of the effective reproduction number by a factor of 2–3. We find that serial interval estimates in settings such as schools and meat processing/packing plants are shorter than those in healthcare facilities.

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  1. Version published to 10.1038/s41467-023-40544-y
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    SciScore for 10.1101/2022.02.23.22271355: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Sequencing reads were mapped to the Wuhan-Hu-1 reference sequence (Genbank MN908947.3) and consensus sequences generated using the iVar pipeline [17].
    iVar
    suggested: None
    Using the ClusterPicker tool [19], clusters were defined as having at least two samples with the inferred ancestral node having at least 95% bootstrap support and the maximum distance within the cluster of 0.0004 expected substitutions/site.
    ClusterPicker
    suggested: None
    An approximate maximum-likelihood tree was built using FastTree [21] and the branch lengths optimised with RAxML-NG [22].
    FastTree
    suggested: (FastTree, RRID:SCR_015501)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    There are several limitations of our methodology. Due to the assumption of gamma distributed serial intervals (required for the construction of the mixture model), we assume serial intervals are strictly positive. There is evidence of negative serial intervals for COVID-19 [13] and in other diseases, and an extension of this model could allow for this. We perform transmission tree sampling from a cloud of plausible infector-infectee pairs, allowing for indirect and coprimary transmission, in order to take into account uncertainty in who infected whom from the genomic data. Although we do this in a probabilistic way which aims to approximate the judgement applied in public health (that cases closer in pathogen sequence and in time are more likely to infect one another), the tree sampling could be improved by incorporating additional epidemiological data e.g. known pairs from contact tracing or relative Ct values, if this were available. It may also be possible to incorporate existing methodologies for sampling of transmission trees, such as the outbreaker or TransPhylo platforms [25, 26], but these are not well positioned to estimate serial intervals. Lastly, our approach requires prior knowledge of the population sampling rate. This is really an innate identifiability problem with estimation of serial intervals using any model, in that short serial intervals with low sampling would be indistinguishable from long serial intervals with high sampling. However, the sensitivity an...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

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  3. Version published to 10.1101/2022.02.23.22271355 on medRxiv