Genetic determination of regional connectivity in modelling the spread of COVID-19 outbreak for more efficient mitigation strategies

  1. Leonidas Salichos
  2. Jonathan Warrell
  3. Hannah Cevasco
  4. Alvin Chung
  5. Mark Gerstein

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Abstract

For the COVID-19 pandemic, viral transmission has been documented in many historical and geographical contexts. Nevertheless, few studies have explicitly modeled the spatiotemporal flow based on genetic sequences, to develop mitigation strategies. Additionally, thousands of SARS-CoV-2 genomes have been sequenced with associated records, potentially providing a rich source for such spatiotemporal analysis, an unprecedented amount during a single outbreak. Here, in a case study of seven states, we model the first wave of the outbreak by determining regional connectivity from phylogenetic sequence information (i.e. “genetic connectivity”), in addition to traditional epidemiologic and demographic parameters. Our study shows nearly all of the initial outbreak can be traced to a few lineages, rather than disconnected outbreaks, indicative of a mostly continuous initial viral flow. While the geographic distance from hotspots is initially important in the modeling, genetic connectivity becomes increasingly significant later in the first wave. Moreover, our model predicts that isolated local strategies (e.g. relying on herd immunity) can negatively impact neighboring regions, suggesting more efficient mitigation is possible with unified, cross-border interventions. Finally, our results suggest that a few targeted interventions based on connectivity can have an effect similar to that of an overall lockdown. They also suggest that while successful lockdowns are very effective in mitigating an outbreak, less disciplined lockdowns quickly decrease in effectiveness. Our study provides a framework for combining phylodynamic and computational methods to identify targeted interventions.

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  1. Version published to 10.1038/s41598-023-34959-2
  2. ScreenIT

    SciScore for 10.1101/2021.01.30.21250785: (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

    Experimental Models: Organisms/Strains
    SentencesResources
    We then recalculate the distances , i.e. the distance between states i and j, given the link between s1 and s2 has been broken.
    s1
    suggested: None
    Software and Algorithms
    SentencesResources
    Phylogenetic Analysis: By retrieving the genomic sequences from GISAID (Supplementary table), we used MAFFT (45) to build multiple sequence alignments for every state based on nucleotide sequence data.
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    The BEAST suite also includes multiple software tools that aid in selecting appropriate models and parameters (BEAUti) to infer a phylogenetic tree using Bayesian inference, coalescent theory and speciation with respect to the time of sequence collection.
    BEAST
    suggested: (BEAST, RRID:SCR_010228)
    We evaluated the efficacy of these models using Tracer v1.7.1(46).
    Tracer
    suggested: (Tracer, RRID:SCR_019121)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.

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  3. Version published to 10.1101/2021.01.30.21250785v1 on medRxiv