Quantifying hospital flows and occupancy due to COVID-19 outbreak in France. Was French lockdown effective?
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Abstract
Context
The spread of SARS-CoV-2 led to a rapid and deadly pandemic which reached almost all countries in the world in a few months. In most countries, rigorous measures of mitigation, including national or subnational lockdowns were established. The present work aimed at quantifying the effect of national lockdown in France on hospital occupancy.
Methods
A compartmental model describing patient hospital flows was developed, where the effect of lockdown was quantified on the hospitalization rate. Model parameters were estimated using nonlinear mixed-effects (NLME) modelling.
Results
French lockdown led to a hospitalization rate decreased by thrice from 12 days after its beginning. However, lockdown may not to have decreased either hospital occupancy or deaths in hospital, which would have been both decreased by 30% and 85% in average if lockdown was started 20 and 30 days before this date, respectively.
Conclusion
The present work showed an intrinsic effect of lockdown to decrease hospital burden, but its efficacy on hospital burden may have been increased if established sooner.
Article activity feed
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SciScore for 10.1101/2020.06.08.20125765: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This work has nevertheless limitations. First, all French sub-regions were not considered, in order to keep N0 sufficiently high in each region unit and to avoid very long time of computation. In particular, overseas territories were not considered, except for Corse. Second, even if accurate values of model parameters were obtained in …
SciScore for 10.1101/2020.06.08.20125765: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This work has nevertheless limitations. First, all French sub-regions were not considered, in order to keep N0 sufficiently high in each region unit and to avoid very long time of computation. In particular, overseas territories were not considered, except for Corse. Second, even if accurate values of model parameters were obtained in each region, notably the time of the first hospitalized patient, no spatial description of disease spread was made. Third, this model does not pretend to be an epidemic model: the hospital admission growth was described using a Kermack-McKendrick input function, which may appear oversimplistic. This also concerns the “on/off” model for lockdown effect, which may not account for the complexity of the real effect of lockdown. Therefore, this model cannot test lockdown ending strategies and their consequences on the risk and amplitude of epidemic rebounds. To describe or forecast these rebounds, a mechanistic epidemic model as Roux et al. [9] could elegantly replace the present input function. Last but not least, this model is “macroscopic”, i.e. does not account for patient specificities (as age distribution or proportion of comorbidities). Taking into account these factors may improve the description of inter-region variability [14]. Given these limitations, the lockdown effect should be considered with caution, this study providing trends more than a sound quantification. In conclusion, the present model has shown an intrinsic effect of lockdown...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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