Effect of a one-month lockdown on the epidemic dynamics of COVID-19 in France

Roques, Lionel; Klein, Etienne K.; Papaïx, Julien; Sar, Antoine; Soubeyrand, Samuel

doi:10.1101/2020.04.21.20074054

Cited by 15 publications

(12 citation statements)

References 20 publications

(23 reference statements)

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“…Mindful of all the pitfalls in determining R o [17], the global estimate here of R o = 4.5 is roughly commensurate with other entirely independent estimates for COVID-19. The most recent update from the China study suggests an R o = 4.1 [25] whereas for France, the most recent estimate for early times is R o = 4.9 [26]. The initially reported and the much cited R o = 2.2 value [4] from Wuhan, China appears to be low [27].…”

Section: Resultsmentioning

confidence: 95%

Global convergence of COVID-19 basic reproduction number and estimation from early-time SIR dynamics

Katul

Mrad

Bonetti

et al. 2020

Preprint

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The SIR ('susceptible-infectious-recovered') formulation is used to uncover the generic spread mechanisms observed by COVID-19 dynamics globally, especially in the early phases of infectious spread. During this early period, potential controls were not effectively put in place or enforced in many countries. Hence, the early phases of COVID-19 spread in countries where controls were weak offer a unique perspective on the ensemble-behavior of COVID-19 basic reproduction number R o . The work here shows that there is global convergence (i.e. across many nations) to an uncontrolled R o = 4.5 that describes the early time spread of COVID-19. This value is in agreement with independent estimates from other sources reviewed here and adds to the growing consensus that the early estimate of R o = 2.2 adopted by the World Health Organization is low. A reconciliation between power-law and exponential growth predictions is also featured within the confines of the SIR formulation. Implications for evaluating potential control strategies from this uncontrolled R o are briefly discussed in the context of the maximum possible infected fraction of the population (needed for assessing health care capacity) and mortality (especially in the USA given diverging projections). Model results indicate that if intervention measures still result in R o > 2.7 within 49 days after first infection, intervention is unlikely to be effective in general for COVID-19. Current optimistic projections place mortality figures in the USA in the range of 100,000 fatalities. For fatalities to be confined to 100,000 requires a reduction in R o from 4.5 to 2.7 within 17 days of first infection assuming a mortality rate of 3.4%. : medRxiv preprint in epidemiology. This dispute moved inoculation from the domain of philosophy, 4 religion, and disjointed trials plagued by high uncertainty into a debate about 5 mathematical models -put forth by Daniel Bernoulli (in 1766) and Jean-Baptiste le 6 Rond D'Alembert (in 1761), both dealing with competing risks of death and 7 interpretation of trials [1]. Since then, the mathematical description of infectious 8 diseases continues to draw significant attention from researchers and practitioners in 9 governments and health agencies alike. Even news agencies are now seeking out 10 explanations to models so as to offer advice and clarity to their audiences during the 11 (near-continuous) coverage of the spread of COVID-19 [2]. The prospect of using 12 mathematical models in conjunction with data is succinctly summarized by the Nobel 13 laureate Ronald Ross, whose 1916 abstract [3] enlightens the role of mathematics in 14 epidemiology today. A quotation from this abstract below, which foreshadows the 15 requirements and challenges for mathematical models to describe emerging epidemics 16 such as 5], needs no further elaboration: 17

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Section: Resultsmentioning

confidence: 95%

Global convergence of COVID-19 basic reproduction number and estimation from early-time SIR dynamics

Katul

Mrad

Bonetti

et al. 2020

Preprint

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“…Indeed, down-scaling our iFRs resulted in an increase of the total number of infections to 2.71 millions (95% CI: 2.19 – 3.49) as of May 10 for model 1, closer to the estimate reported by (Salje et al ., 2020). Better estimates of regional IFRs might be obtained by updating the work of (Roques et al ., 2020) with more data. However, the better fit of the mixture model over model 1 suggests that the total number of infections is probably overestimated by model 1 and by (Salje et al ., 2020).…”

Section: Discussionmentioning

confidence: 99%

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

Duchemin

Veber

Boussau

2020

Preprint

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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.

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“…Roques et al. ( Roques, Klein, Papaix, Sar, & Soubeyrand, 2020a , 2020b ; Roques et al., 2020a , 2020b ) where an estimate of the fatality ratio has been developed). A remarkable feature of those methods is to provide mechanisms to correct some of the known biases in the observation of cases, such as the daily number of tests.…”

Section: Introductionmentioning

confidence: 99%