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Title (Primary) Data driven high resolution modeling and spatial analyses of the COVID-19 pandemic in Germany
Author Schüler, L.; Calabrese, J.M.; Attinger, S.;
Journal PLOS ONE
Year 2021
Department OESA; CHS;
Volume 16
Issue 8
Page From e0254660
Language englisch;
Topic T5 Future Landscapes
Supplements https://doi.org/10.1371/journal.pone.0254660.s001
https://doi.org/10.1371/journal.pone.0254660.s002
Abstract The SARS-CoV-2 virus has spread around the world with over 100 million infections to date, and currently many countries are fighting the second wave of infections. With neither sufficient vaccination capacity nor effective medication, non-pharmaceutical interventions (NPIs) remain the measure of choice. However, NPIs place a great burden on society, the mental health of individuals, and economics. Therefore the cost/benefit ratio must be carefully balanced and a target-oriented small-scale implementation of these NPIs could help achieve this balance. To this end, we introduce a modified SEIRD-class compartment model and parametrize it locally for all 412 districts of Germany. The NPIs are modeled at district level by time varying contact rates. This high spatial resolution makes it possible to apply geostatistical methods to analyse the spatial patterns of the pandemic in Germany and to compare the results of different spatial resolutions. We find that the modified SEIRD model can successfully be fitted to the COVID-19 cases in German districts, states, and also nationwide. We propose the correlation length as a further measure, besides the weekly incidence rates, to describe the current situation of the epidemic.
ID 25013
Persistent UFZ Identifier https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=25013
Schüler, L., Calabrese, J.M., Attinger, S. (2021):
Data driven high resolution modeling and spatial analyses of the COVID-19 pandemic in Germany
PLOS One 16 (8), e0254660