Genetic evidence for the association between COVID-19 epidemic severity and timing of non-pharmaceutical interventions

  1. Manon Ragonnet-Cronin
  2. Olivia Boyd
  3. Lily Geidelberg
  4. David Jorgensen
  5. Fabricia F. Nascimento
  6. Igor Siveroni
  7. Robert A. Johnson
  8. Marc Baguelin
  9. Zulma M. Cucunubá
  10. Elita Jauneikaite
  11. Swapnil Mishra
  12. Oliver J. Watson
  13. Neil Ferguson
  14. Anne Cori
  15. Christl A. Donnelly
  16. Erik Volz

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Abstract

Unprecedented public health interventions including travel restrictions and national lockdowns have been implemented to stem the COVID-19 epidemic, but the effectiveness of non-pharmaceutical interventions is still debated. We carried out a phylogenetic analysis of more than 29,000 publicly available whole genome SARS-CoV-2 sequences from 57 locations to estimate the time that the epidemic originated in different places. These estimates were examined in relation to the dates of the most stringent interventions in each location as well as to the number of cumulative COVID-19 deaths and phylodynamic estimates of epidemic size. Here we report that the time elapsed between epidemic origin and maximum intervention is associated with different measures of epidemic severity and explains 11% of the variance in reported deaths one month after the most stringent intervention. Locations where strong non-pharmaceutical interventions were implemented earlier experienced much less severe COVID-19 morbidity and mortality during the period of study.

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  1. Version published to 10.1038/s41467-021-22366-y
  2. ScreenIT

    SciScore for 10.1101/2020.09.15.20194258: (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
    Transmission model and comparative phylodynamic analysis: We utilized a compartmental structured coalescent model in the BEAST2 v6.1 PhyDyn package14,15 to estimate the effective reproduction number and the number of infections through time from SARS-CoV-2 genetic sequences.
    PhyDyn
    suggested: (PhyDyn, RRID:SCR_018544)
    For each site under investigation, we simultaneously reconstructed a phylogeny and estimated epidemiological parameters in BEAST2 (see Supplementary Methods for details).
    BEAST2
    suggested: (BEAST2, RRID:SCR_017307)
    Comparison with other sources of data and statistical analysis: For sites analysed using BEAST, we examined the relationship between mobility data provided by Google (google.com/covid19/mobility, analysis limited to “transit stations” only) and Rt 22 by calculating Pearson’s correlation coefficient.
    BEAST
    suggested: (BEAST, RRID:SCR_010228)
    Google
    suggested: (Google, RRID:SCR_017097)

    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: We detected the following sentences addressing limitations in the study:
    A limitation of our analysis is that the Bayesian MCMC for our phylodynamic model did not converge for all the locations. This can occur because one of the model assumptions is violated; for example, samples may not be collected at random, or the population is not randomly mixing. We addressed these concerns by excluding sites known to have prioritised sequencing from travellers16,33 or contact tracing and by focusing on smaller geographical units, such as cities and small regions where within-sample geographic structure is less biasing. Further optimisation of some parameters, such as the parameter for transmission overdispersion could have improved estimates, as has been recently demonstrated by Miller et al.16, but we chose to keep this parameter constant across sites to facilitate meta-analysis. The non-parametric phylodynamic analysis allowed us to include sites for which data were available but the Bayesian MCMC did not converge20,21 as well as to examine sensitivity of results to choice of modelling framework. Results echoed those from the compartmental phylodynamic model. Viral effective population size at maximum NPI was associated with deaths one month after maximum NPI and with time to maximum NPI. In conclusion, we have shown that across five continents, longer delays from viral introduction to lockdowns have led to more infections at lockdown and more deaths one month after lockdown. Our models were calibrated entirely using genetic data and thus provide an indep...

    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 10.1101/2020.09.15.20194258 on medRxiv