Recommendation

Modeling the effect of lockdown and other events on the dynamics of SARS-CoV-2 in France

based on reviews by Wayne Landis and 1 anonymous reviewer
A recommendation of:
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Bayesian investigation of SARS-CoV-2-related mortality in France

Data used for results
Scripts used to obtain or analyze results
Abstract
Submitted: 08 July 2020, Recommended: 04 September 2020

Recommendation

This study [1] used Bayesian models of the number of deaths through time across different regions of France to explore the effects of lockdown and other events (i.e., holding elections) on the dynamics of the SARS-CoV-2 epidemic. The models accurately predicted the number of deaths 2 to 3 weeks in advance, and results were similar to other recent models using different structure and input data. Viral reproduction numbers were not found to be different between weekends and week days, and there was no evidence that holding elections affected the number of deaths directly. However, exploring different scenarios of the timing of the lockdown showed that this had a substantial impact on the number of deaths. This is an interesting and important paper that can inform adaptive management strategies for controlling the spread of this virus, not just in France, but in other geographic areas. For example, the results found that there was a lag period between a change in management strategies (lockdown, social distancing, and the relaxing of controls) and the observed change in mortality. Also, there was a large variation in the impact of mitigation measures on the viral reproduction number depending on region, with lockdown being slightly more effective in denser regions. The authors provide an extensive amount of additional data and code as supplemental material, which increase the value of this contribution to the rapidly growing literature on SARS-CoV-2.

References

[1] Duchemin, L., Veber, P. and Boussau, B. (2020) Bayesian investigation of SARS-CoV-2-related mortality in France. medRxiv 2020.06.09.20126862, ver. 5 peer-reviewed and recommended by PCI Mathematical & Computational Biology. doi: 10.1101/2020.06.09.20126862

Cite this recommendation as:
Valery Forbes (2020) Modeling the effect of lockdown and other events on the dynamics of SARS-CoV-2 in France. Peer Community in Mathematical and Computational Biology, 100001. 10.24072/pci.mcb.100001

Evaluation round #1

DOI or URL of the preprint: https://www.medrxiv.org/content/10.1101/2020.06.09.20126862v2

Author's Reply, None

Decision by , 03 Aug 2020

Dear Author,

Your preprint received two generally positive reviews. Both reviewers provided thoughtful comments and suggestions that, if addressed, will strengthen your preprint. I would ask you to please address the reviewers' concerns and resubmit your revised preprint with an indication of how you have addressed their comments.

Sincerely,

Valery Forbes

Reviewed by , 03 Aug 2020

Reviewed by anonymous reviewer, 28 Jul 2020

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