Modeling the effect of lockdown and other events on the dynamics of SARS-CoV-2 in France
Bayesian investigation of SARS-CoV-2-related mortality in France
Recommendation: posted 04 September 2020, validated 09 September 2020
This study  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.
 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
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
The recommender in charge of the evaluation of the article and the reviewers declared that they have no conflict of interest (as defined in the code of conduct of PCI) with the authors or with the content of the article.
Evaluation round #1
DOI or URL of the preprint: https://www.medrxiv.org/content/10.1101/2020.06.09.20126862v2
Author's Reply, 01 Sep 2020
Decision by Valery Forbes, posted 03 Aug 2020
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.