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Bayesian investigation of SARS-CoV-2-related mortality in France

Duchemin, Louis1; Veber, Philippe1; Boussau, Bastien1 
1 Université de Lyon, Université Lyon1, CNRS, Laboratoire de Biométrie et Biologie Evolutive, UMR5558, F-69622 Villeurbanne, France

10.24072/pcjournal.84 - Peer Community Journal, Volume 2 (2022), article no. e6.

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The SARS-CoV-2 epidemic in France has focused a lot of attention as it has had 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.
Published online:
DOI: 10.24072/pcjournal.84
Duchemin, Louis 1; Veber, Philippe 1; Boussau, Bastien 1

1 Université de Lyon, Université Lyon1, CNRS, Laboratoire de Biométrie et Biologie Evolutive, UMR5558, F-69622 Villeurbanne, France
License: CC-BY 4.0
Copyrights: The authors retain unrestricted copyrights and publishing rights 
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Duchemin, Louis; Veber, Philippe; Boussau, Bastien. Bayesian investigation of SARS-CoV-2-related mortality in France. Peer Community Journal, Volume 2 (2022), article  no. e6. doi : 10.24072/pcjournal.84. https://peercommunityjournal.org/articles/10.24072/pcjournal.84/

Peer reviewed and recommended by PCI : 10.24072/pci.mcb.100001

Conflict of interest of the recommender and peer reviewers:
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.

[1] Bernard Stoecklin, S.; Rolland, P.; Silue, Y.; Mailles, A.; Campese, C.; Simondon, A.; Mechain, M.; Meurice, L.; Nguyen, M.; Bassi, C.; Yamani, E.; Behillil, S.; Ismael, S.; Nguyen, D.; Malvy, D.; Lescure, F. X.; Georges, S.; Lazarus, C.; Tabaï, A.; Stempfelet, M.; Enouf, V.; Coignard, B.; Levy-Bruhl, D. First cases of coronavirus disease 2019 (COVID-19) in France: surveillance, investigations and control measures, January 2020, Eurosurveillance, Volume 25 (2020) no. 6 | DOI

[2] Pietralunga, C.; Lemarié, A.; Faye, O. Coronavirus: l’exécutif mis sous pression pour avoir maintenu le premier tour des élections municipales, 2020 (https://www.lemonde.fr/politique/article/2020/03/15/coronavirus-l-executif-mis-sous-pression-pour-avoir-maintenu-le-premier-tour-des-municipales_6033154_823448.html)

[3] Ponchon, E. Coronavirus à Paris: maintenant, on ne rigole plus avec le confinement, 2020 (http://www.leparisien.fr/paris-75/coronavirus-a-paris-maintenant-on-ne-rigole-plus-avec-le-confinement-20-03-2020-8284737.php)

[4] Flaxman, S.; Mishra, S.; Gandy, A.; Unwin, H. J. T.; Mellan, T. A.; Coupland, H.; Whittaker, C.; Zhu, H.; Berah, T.; Eaton, J. W.; Monod, M.; Perez-Guzman, P. N.; Schmit, N.; Cilloni, L.; Ainslie, K. E. C.; Baguelin, M.; Boonyasiri, A.; Boyd, O.; Cattarino, L.; Cooper, L. V.; Cucunubá, Z.; Cuomo-Dannenburg, G.; Dighe, A.; Djaafara, B.; Dorigatti, I.; van Elsland, S. L.; FitzJohn, R. G.; Gaythorpe, K. A. M.; Geidelberg, L.; Grassly, N. C.; Green, W. D.; Hallett, T.; Hamlet, A.; Hinsley, W.; Jeffrey, B.; Knock, E.; Laydon, D. J.; Nedjati-Gilani, G.; Nouvellet, P.; Parag, K. V.; Siveroni, I.; Thompson, H. A.; Verity, R.; Volz, E.; Walters, C. E.; Wang, H.; Wang, Y.; Watson, O. J.; Winskill, P.; Xi, X.; Walker, P. G. T.; Ghani, A. C.; Donnelly, C. A.; Riley, S.; Vollmer, M. A. C.; Ferguson, N. M.; Okell, L. C.; Bhatt, S. Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe, Nature, Volume 584 (2020) no. 7820, pp. 257-261 | DOI

[5] French Government COVID-19 map and data in France., 2020 (https://www.gouvernement.fr/info-coronavirus/carte-et-donnee)

[6] French Ministry of Health Données hospitalières relatives a l’épidémie de COVID-19, 2020 (https://www.data.gouv.fr/fr/datasets/donnees-hospitalieres-relatives-a-lepidemie-de-covid-19)

[7] Peillon, L. Coronavirus a Paris: maintenant, on ne rigole plus avec le confinement, 2020 (https://www.liberation.fr/checknews/2020/04/07/covid-19-pourquoi-les-chiffres-des-deces-et-des-hospitalisations-sont-toujours-plus-eleves-le-lundi_178446)

[8] Magal, P.; Webb, G. Predicting the number of reported and unreported cases for the COVID-19 epidemic in South Korea, Italy, France and Germany, medRxiv (2020) | DOI

[9] Massonnaud, C.; Roux, J.; Crépey, P. COVID-19: Forecasting short term hospital needs in France, medRxiv, 2020 | DOI

[10] Neher, R. A.; Dyrdak, R.; Druelle, V.; Hodcroft, E. B.; Albert, J. Potential impact of seasonal forcing on a SARS-CoV-2 pandemic, Swiss Medical Weekly, Volume 150 (2020) | DOI

[11] OpenCOVID19 contributors COVID19 epidemic french national data, 2020 (https://github.com/opencovid19-fr/dat)

[12] Roques, L.; Klein, E.; Papaïx, J.; Sar, A.; Soubeyrand, S. Using early data to estimate the actual infection fatality ratio from COVID-19 in France, medRxiv (2020) | DOI

[13] Roux, J.; Massonnaud, C.; Crépey, P. COVID-19: One-month impact of the French lockdown on the epidemic burden, medRxiv (2020) | DOI

[14] Salje, H.; Tran Kiem, C.; Lefrancq, N.; Courtejoie, N.; Bosetti, P.; Paireau, J.; Andronico, A.; Hozé, N.; Richet, J.; Dubost, C.-L.; Le Strat, Y.; Lessler, J.; Levy-Bruhl, D.; Fontanet, A.; Opatowski, L.; Boelle, P.-Y.; Cauchemez, S. Estimating the burden of SARS-CoV-2 in France, Science, Volume 369 (2020) no. 6500, pp. 208-211 | DOI

[15] Sofonea, M. T.; Reyné, B.; Elie, B.; Djidjou-Demasse, R.; Selinger, C.; Michalakis, Y.; Alizon, S. Epidemiological monitoring and control perspectives: application of a parsimonious modelling framework to the COVID-19 dynamics in France, medRxiv (2020) | DOI

[16] Stan Development Team RStan: the R interface to Stan. R package version 2.19.1, 2019 (http://mc-stan.org)

[17] Verity, R.; Okell, L. C.; Dorigatti, I.; Winskill, P.; Whittaker, C.; Imai, N.; Cuomo-Dannenburg, G.; Thompson, H.; Walker, P. G.; Fu, H.; Dighe, A.; Griffin, J. T.; Baguelin, M.; Bhatia, S.; Boonyasiri, A.; Cori, A.; Cucunubá, Z.; FitzJohn, R.; Gaythorpe, K.; Green, W.; Hamlet, A.; Hinsley, W.; Laydon, D.; Nedjati-Gilani, G.; Riley, S.; van Elsland, S.; Volz, E.; Wang, H.; Wang, Y.; Xi, X.; Donnelly, C. A.; Ghani, A. C.; Ferguson, N. M. Estimates of the severity of COVID-19 disease, medRxiv (2020) | DOI

[18] World Health Organization WHO Director-General’s opening remarks at the media briefing on COVID-19 - 11 March 2020., 2020 (https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-1911-march-202)

[19] Zeitoun, J.-D.; Faron, M.; Manternach, s.; Fourquet, J.; Lavielle, M.; lefevre, j. Reciprocal association between participation to a national election and the epidemic spread of COVID-19 in France: nationwide observational and dynamic modeling study, medRxiv (2020) | DOI

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