Using a Bayesian hierarchical approach to study the association between non-pharmaceutical interventions and the spread of Covid-19 in Germany

Khazaei, Yeganeh; Küchenhoff, Helmut ; Hoffmann, Sabine ; Syliqi, Diella und Rehms, Raphael (2023): Using a Bayesian hierarchical approach to study the association between non-pharmaceutical interventions and the spread of Covid-19 in Germany. In: Scientific Reports, Bd. 13, 18900: S. 1-9

Abstract

Non-Pharmaceutical Interventions (NPIs) are community mitigation strategies, aimed at reducing the spread of illnesses like the coronavirus pandemic, without relying on pharmaceutical drug treatments. This study aims to evaluate the effectiveness of different NPIs across sixteen states of Germany, for a time period of 21 months of the pandemic. We used a Bayesian hierarchical approach that combines different sub-models and merges information from complementary sources, to estimate the true and unknown number of infections. In this framework, we used data on reported cases, hospitalizations, intensive care unit occupancy, and deaths to estimate the effect of NPIs. The list of NPIs includes: “contact restriction (up to 5 people)”, “strict contact restriction”, “curfew”, “events permitted up to 100 people”, “mask requirement in shopping malls”, “restaurant closure”, “restaurants permitted only with test”, “school closure” and “general behavioral changes”. We found a considerable reduction in the instantaneous reproduction number by “general behavioral changes”, “strict contact restriction”, “restaurants permitted only with test”, “contact restriction (up to 5 people)”, “restaurant closure” and “curfew”. No association with school closures could be found. This study suggests that some public health measures, including general behavioral changes, strict contact restrictions, and restaurants permitted only with tests are associated with containing the Covid-19 pandemic. Future research is needed to better understand the effectiveness of NPIs in the context of Covid-19 vaccination.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Mathematik, Informatik und Statistik > Statistik ; Medizin > Institut für Medizinische Informationsverarbeitung, Biometrie und Epidemiologie
• Themengebiete: 300 Sozialwissenschaften > 310 Statistiken ; 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• ISSN: 2045-2322
• Sprache: Englisch