{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T11:47:07Z","timestamp":1784720827921,"version":"3.55.0"},"reference-count":62,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,7,2]],"date-time":"2022-07-02T00:00:00Z","timestamp":1656720000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Free State of Saxony","award":["LO-342\/17-1"],"award-info":[{"award-number":["LO-342\/17-1"]}]},{"name":"Free State of Saxony","award":["01ZX1304A"],"award-info":[{"award-number":["01ZX1304A"]}]},{"name":"Free State of Saxony","award":["#031L0296A"],"award-info":[{"award-number":["#031L0296A"]}]},{"name":"NFDI4Health Task Force COVID-19","award":["LO-342\/17-1"],"award-info":[{"award-number":["LO-342\/17-1"]}]},{"name":"NFDI4Health Task Force COVID-19","award":["01ZX1304A"],"award-info":[{"award-number":["01ZX1304A"]}]},{"name":"NFDI4Health Task Force COVID-19","award":["#031L0296A"],"award-info":[{"award-number":["#031L0296A"]}]},{"name":"German Federal Ministry of Education and Research (BMBF)","award":["LO-342\/17-1"],"award-info":[{"award-number":["LO-342\/17-1"]}]},{"name":"German Federal Ministry of Education and Research (BMBF)","award":["01ZX1304A"],"award-info":[{"award-number":["01ZX1304A"]}]},{"name":"German Federal Ministry of Education and Research (BMBF)","award":["#031L0296A"],"award-info":[{"award-number":["#031L0296A"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Viruses"],"abstract":"<jats:p>Numerous prediction models of SARS-CoV-2 pandemic were proposed in the past. Unknown parameters of these models are often estimated based on observational data. However, lag in case-reporting, changing testing policy or incompleteness of data lead to biased estimates. Moreover, parametrization is time-dependent due to changing age-structures, emerging virus variants, non-pharmaceutical interventions, and vaccination programs. To cover these aspects, we propose a principled approach to parametrize a SIR-type epidemiologic model by embedding it as a hidden layer into an input-output non-linear dynamical system (IO-NLDS). Observable data are coupled to hidden states of the model by appropriate data models considering possible biases of the data. This includes data issues such as known delays or biases in reporting. We estimate model parameters including their time-dependence by a Bayesian knowledge synthesis process considering parameter ranges derived from external studies as prior information. We applied this approach on a specific SIR-type model and data of Germany and Saxony demonstrating good prediction performances. Our approach can estimate and compare the relative effectiveness of non-pharmaceutical interventions and provide scenarios of the future course of the epidemic under specified conditions. It can be translated to other data sets, i.e., other countries and other SIR-type models.<\/jats:p>","DOI":"10.3390\/v14071468","type":"journal-article","created":{"date-parts":[[2022,7,4]],"date-time":"2022-07-04T23:38:55Z","timestamp":1656977935000},"page":"1468","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["On the Parametrization of Epidemiologic Models\u2014Lessons from Modelling COVID-19 Epidemic"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8356-2724","authenticated-orcid":false,"given":"Yuri","family":"Kheifetz","sequence":"first","affiliation":[{"name":"Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Haertelstrasse 16-18, 04107 Leipzig, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3126-7950","authenticated-orcid":false,"given":"Holger","family":"Kirsten","sequence":"additional","affiliation":[{"name":"Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Haertelstrasse 16-18, 04107 Leipzig, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4059-1779","authenticated-orcid":false,"given":"Markus","family":"Scholz","sequence":"additional","affiliation":[{"name":"Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Haertelstrasse 16-18, 04107 Leipzig, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1007\/s41745-020-00200-6","article-title":"Mathematical Models for COVID-19 Pandemic: A Comparative Analysis","volume":"100","author":"Adiga","year":"2020","journal-title":"J. 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