Association between non-pharmaceutical interventions and the spread of infectious disease during the Covid-19 outbreak in Germany: Application of a hierarchical Bayesian Approach

Association between non-pharmaceutical interventions and the spread of infectious disease during the Covid-19 outbreak in Germany: Application of a hierarchical Bayesian Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Help Center Sign In Submit a Preprint Cite Share Download PDF Article Association between non-pharmaceutical interventions and the spread of infectious disease during the Covid-19 outbreak in Germany: Application of a hierarchical Bayesian Approach Yeganeh Khazaei, Helmut Küchenhoff, Sabine Hoffmann, Diella Syliqi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3087153/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Nov, 2023 Read the published version in Scientific Reports → Version 1 posted 9 You are reading this latest preprint version Abstract Background : 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. Methods : 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”. Results : 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. Conclusions : 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. Biological sciences/Microbiology Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Full Text Additional Declarations No competing interests reported. Supplementary Files CovidNPIestimationGermanyBayes11end.pdf Cite Share Download PDF Status: Published Journal Publication published 02 Nov, 2023 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Major revision 03 Oct, 2023 Reviewers agreed at journal 03 Sep, 2023 Reviews received at journal 02 Sep, 2023 Reviewers agreed at journal 22 Aug, 2023 Reviewers invited by journal 20 Aug, 2023 Editor assigned by journal 20 Aug, 2023 Editor invited by journal 20 Jul, 2023 Submission checks completed at journal 20 Jul, 2023 First submitted to journal 20 Jun, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3087153","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":211553453,"identity":"b39d944d-e60b-44c4-bc4a-ac0899b7420b","order_by":0,"name":"Yeganeh 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