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COVID-19 outbreak in Wuhan demonstrates the limitations of publicly available case numbers for epidemiological modeling.
Raimúndez, Elba; Dudkin, Erika; Vanhoefer, Jakob; Alamoudi, Emad; Merkt, Simon; Fuhrmann, Lara; Bai, Fan; Hasenauer, Jan.
  • Raimúndez E; Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany; Technische Universität München, Center for Mathematics, Garching, Germany.
  • Dudkin E; Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany.
  • Vanhoefer J; Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany.
  • Alamoudi E; Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany.
  • Merkt S; Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany.
  • Fuhrmann L; Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany.
  • Bai F; Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany.
  • Hasenauer J; Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany; Technische Universität München, Center for Mathematics, Garching, Germany; Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany. Electronic
Epidemics ; 34: 100439, 2021 03.
Article in English | MEDLINE | ID: covidwho-1068904
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ABSTRACT
Epidemiological models are widely used to analyze the spread of diseases such as the global COVID-19 pandemic caused by SARS-CoV-2. However, all models are based on simplifying assumptions and often on sparse data. This limits the reliability of parameter estimates and predictions. In this manuscript, we demonstrate the relevance of these limitations and the pitfalls associated with the use of overly simplistic models. We considered the data for the early phase of the COVID-19 outbreak in Wuhan, China, as an example, and perform parameter estimation, uncertainty analysis and model selection for a range of established epidemiological models. Amongst others, we employ Markov chain Monte Carlo sampling, parameter and prediction profile calculation algorithms. Our results show that parameter estimates and predictions obtained for several established models on the basis of reported case numbers can be subject to substantial uncertainty. More importantly, estimates were often unrealistic and the confidence/credibility intervals did not cover plausible values of critical parameters obtained using different approaches. These findings suggest, amongst others, that standard compartmental models can be overly simplistic and that the reported case numbers provide often insufficient information for obtaining reliable and realistic parameter values, and for forecasting the evolution of epidemics.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Models, Statistical / Pandemics / COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Country/Region as subject: Asia Language: English Journal: Epidemics Year: 2021 Document Type: Article Affiliation country: J.epidem.2021.100439

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Models, Statistical / Pandemics / COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Country/Region as subject: Asia Language: English Journal: Epidemics Year: 2021 Document Type: Article Affiliation country: J.epidem.2021.100439