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

Duchemin, L.; Veber, P.; Boussau, B.

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 a Bayesian model of the number of deaths through time to study the epidemic in 13 regions of France. We found that the model accurately predicts the number of deaths 2 to 3 weeks in advance, and recovers estimates that are in agreement with recent models that rely on a different structure and different input data. We found that the lockdown had a similar effect on the reproduction rate in all 13 regions, decreasing it by 80 %. Our model predicts that 2.09 (95% CI : 1.69-2.66) million people had been infected by May 11, and that there were 2793 (95% CI : 1761-4543) new infections on May 11. We found no evidence that the reproduction rates differ between week-ends and week days, and no evidence that the reproduction rates increased on the election day. Finally, we evaluated counterfactual scenarios showing that ordering the lockdown 1 to 7 days sooner would have resulted in 18% to 85% fewer deaths, but that ordering it 1 to 7 days later would have resulted in 21% to 262% 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.