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Bayesian investigation of SARS-CoV-2-related mortality in Franceuse asterix (*) to get italics
Louis Duchemin, Philippe Veber, Bastien BoussauPlease use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"
2020
<p>The SARS-CoV-2 epidemic in France has focused a lot of attention as it hashad one of the largest death tolls in Europe. It provides an opportunity to examine the effect of the lockdown and of other events on the dynamics of the epidemic. In particular, it has been suggested that municipal elections held just before lockdown was ordered may have helped spread the virus. In this manuscript we use Bayesian models of the number of deaths through time to study the epidemic in 13 regions of France. We found that the models accurately predict the number of deaths 2 to 3 weeks in advance, and recover estimates that are in agreement with recent models that rely on a different structure and different input data. In particular, the lockdown reduced the viral reproduction number by ≈ 80%. However, using a mixture model, we found that the lockdown had had different effectiveness depending on the region, and that it had been slightly more effective in decreasing the reproduction number in denser regions. The mixture model predicts that 2.08 (95% CI : 1.85-2.47) million people had been infected by May 11, and that there were 2567 (95% CI : 1781-5182) new infections on May 10. We found no evidence that the reproduction numbers differ between week-ends and week days, and no evidence that the reproduction numbers increased on the election day. Finally, we evaluated counterfactual scenarios showing that ordering the lockdown 1 to 7 days sooner would have resulted in 19% to 76% fewer deaths, but that ordering it 1 to 7 days later would have resulted in 21% to 266% more deaths. Overall, the predictions of the model indicate that holding the elections on March 15 did not have a detectable impact on the total number of deaths, unless it motivated a delay in imposing the lockdown.</p>
https://zenodo.org/record/4019099You should fill this box only if you chose 'All or part of the results presented in this preprint are based on data'. URL must start with http:// or https://
https://www.medrxiv.org/content/10.1101/2020.06.09.20126862v4.supplementary-materialYou should fill this box only if you chose 'Scripts were used to obtain or analyze the results'. URL must start with http:// or https://
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SARS-CoV-2 ; Covid-19 ; Bayesian model ; mixture model ; French elections ; epidemiology
NonePlease indicate the methods that may require specialised expertise during the peer review process (use a comma to separate various required expertises).
Probability and statistics
No need for them to be recommenders of PCI Math Comp Biol. Please do not suggest reviewers for whom there might be a conflict of interest. Reviewers are not allowed to review preprints written by close colleagues (with whom they have published in the last four years, with whom they have received joint funding in the last four years, or with whom they are currently writing a manuscript, or submitting a grant proposal), or by family members, friends, or anyone for whom bias might affect the nature of the review - see the code of conduct
e.g. John Doe [john@doe.com]
2020-07-08 17:29:46
Valery Forbes