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Raimúndez, E.* ; Dudkin, E.* ; Vanhoefer, J.* ; Alamoudi, E.* ; Merkt, S.* ; Fuhrmann, L.* ; Bai, F.* ; Hasenauer, J.

COVID-19 outbreak in Wuhan demonstrates the limitations of publicly available case numbers for epidemiological modeling.

Epidemics 34:100439 (2021)

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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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Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords Compartment Model ; Model Selection ; Parameter Estimation ; Seird ; Uncertainty Analysis; Practical Identifiability Analysis; Parameter-estimation; Influenza; Systems; Likelihood; Predict; Spread; China

ISSN (print) / ISBN 1755-4365

e-ISSN 1878-0067

Journal Epidemics

Quellenangaben Volume: 34, Article Number: 100439

Publisher Elsevier

Publishing Place Radarweg 29, 1043 Nx Amsterdam, Netherlands

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Institute of Computational Biology (ICB)

Grants Deutsche Forschungsgemeinschaft (DFG, Ger-man Research Foundation) under Germany's Excellence Strategy
Federal Ministry of Economic Affairs and Energy, Germany
Federal Ministry of Education and Research of Ger-many
European Union's Horizon 2020 research and innovation program (CanPathPro)