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Partial lockdown on unvaccinated individuals promises breaking of fourth COVID-19 wave in Bavaria (preprint)
medrxiv; 2021.
Preprint Dans Anglais | medRxiv | ID: ppzbmed-10.1101.2021.11.28.21266959
ABSTRACT
Purpose of this report The aim of this rapid communication is a projection of the development of the fourth COVID-19 wave in the federal state of Bavaria in Germany, taking into account different lockdown scenarios especially for unvaccinated individuals. In particular, the number of infections and the occupancy of intensive care facilities are considered. Applied

Methods:

We use the agent-based epidemiological simulator Covasim for discussing various epidemiological scenarios. Firstly, we adapt and calibrate our model to reproduce the historical course of the COVID-19 pandemic in Bavaria. For this, we model and integrate numerous public health interventions imposed on the local population. As for some of the political actions rigorous quantification is currently not available, we fit those unknown (free) model parameters to published data on the measured epidemiological dynamics. Finally, we define and analyse scenarios of different lockdown scenarios with restrictions for unvaccinated individuals in different areas of life. Key message The results of our simulations show that in all scenarios considered, the number of infections, but also the number of severe cases, exceed previous maximum values. Interventions, especially restrictions on contacts of unvaccinated persons, can still mitigate the impact of the fourth COVID-19 wave on populations substantially. Excluding unvaccinated students from attending classes has only a small impact on the public health burden. However, many severe cases can be prevented by reducing community and/or work related contacts of unvaccinated people, e.g, by achieving high home office rates.
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Texte intégral: Disponible Collection: Preprints Base de données: medRxiv Sujet Principal: COVID-19 langue: Anglais Année: 2021 Type de document: Preprint

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Texte intégral: Disponible Collection: Preprints Base de données: medRxiv Sujet Principal: COVID-19 langue: Anglais Année: 2021 Type de document: Preprint