Predicted effects of summer holidays and seasonality on the SARS-Cov-2 epidemic in France
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Abstract
The SARS-CoV-2 epidemic in France has had a large death toll. It has not affected all regions similarly, since the death rate can vary several folds between regions where the epidemic has remained at a low level and regions where it got an early burst. The epidemic has been slowed down by a lockdown that lasted for almost eight weeks, and individuals can now move between metropolitan French regions without restriction. In this report we investigate the effect on the epidemic of summer holidays, during which millions of individuals will move between French regions. Additionally, we evaluate the effect of strong or weak seasonality and of several values for the reproduction number on the epidemic, in particular on the timing, the height and the spread of a second wave. To do so, we extend a SEIR model to simulate the effect of summer migrations between regions on the number and distribution of new infections. We find that the model predicts little effect of summer migrations on the epidemic, because the number of migrating infectious individuals are low as a consequence of the lockdown. However, all the reproduction numbers above 1.0 and the seasonality parameters we tried result in a second epidemic wave, with a peak date that can vary between October 2020 and April 2021. If the sanitary measures currently in place manage to keep the reproduction number below 1.0, the second wave will be avoided. If they keep the reproduction number at a low value, for instance at 1.1 as in one of our simulations, the second wave is flattened and could be similar to the first wave.
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SciScore for 10.1101/2020.07.06.20147660: (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
Software and Algorithms Sentences Resources The SEIR model was initialized with the parameter values that the Bayesian model has inferred for March 1 2020. 3.4 Implementation and availability: The simulation is implemented in Python and is available at https://gitlab.in2p3.fr/boussau/futurecorona Pythonsuggested: (IPython, RRID:SCR_001658)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: An explicit section about the limitations of the techniques employed in this study was not found. We …SciScore for 10.1101/2020.07.06.20147660: (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
Software and Algorithms Sentences Resources The SEIR model was initialized with the parameter values that the Bayesian model has inferred for March 1 2020. 3.4 Implementation and availability: The simulation is implemented in Python and is available at https://gitlab.in2p3.fr/boussau/futurecorona Pythonsuggested: (IPython, RRID:SCR_001658)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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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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