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Integrative modelling of reported case numbers and seroprevalence reveals time-dependent test efficiency and infectious contacts.
Epidemics 43:100681 (2023)
Mathematical models have been widely used during the ongoing SARS-CoV-2 pandemic for data interpretation, forecasting, and policy making. However, most models are based on officially reported case numbers, which depend on test availability and test strategies. The time dependence of these factors renders interpretation difficult and might even result in estimation biases. Here, we present a computational modelling framework that allows for the integration of reported case numbers with seroprevalence estimates obtained from representative population cohorts. To account for the time dependence of infection and testing rates, we embed flexible splines in an epidemiological model. The parameters of these splines are estimated, along with the other parameters, from the available data using a Bayesian approach. The application of this approach to the official case numbers reported for Munich (Germany) and the seroprevalence reported by the prospective COVID-19 Cohort Munich (KoCo19) provides first estimates for the time dependence of the under-reporting factor. Furthermore, we estimate how the effectiveness of non-pharmaceutical interventions and of the testing strategy evolves over time. Overall, our results show that the integration of temporally highly resolved and representative data is beneficial for accurate epidemiological analyses.
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Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
Schlagwörter
Covid-19 ; Compartmental Model ; Parameter Estimation ; Uncertainty Quantification
ISSN (print) / ISBN
1755-4365
e-ISSN
1878-0067
Zeitschrift
Epidemics
Quellenangaben
Band: 43,
Artikelnummer: 100681
Verlag
Elsevier
Verlagsort
Radarweg 29, 1043 Nx Amsterdam, Netherlands
Nichtpatentliteratur
Publikationen
Begutachtungsstatus
Peer reviewed
Institut(e)
Institute of Computational Biology (ICB)
CF Statistical Consulting (CF-STATCON)
CF Statistical Consulting (CF-STATCON)
Förderungen
European Union's Horizon 2020 research and innovation programme
Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
Volkswagen Stiftung
German Research Foundation
German Ministry for Education and Research (MoKoCo19)
University of Bielefeld, Munich Center of Health (McHealth)
University Bonn, Germany (Transdiciplinary Research Areas)
Helmholtz Centre Munich, Germany
University Hospital of Ludwig-Maximilians-University Munich
Bavarian State Ministry of Science and the Arts
European Union's Horizon 2020 research and innovation programme
Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
Volkswagen Stiftung
German Research Foundation
German Ministry for Education and Research (MoKoCo19)
University of Bielefeld, Munich Center of Health (McHealth)
University Bonn, Germany (Transdiciplinary Research Areas)
Helmholtz Centre Munich, Germany
University Hospital of Ludwig-Maximilians-University Munich
Bavarian State Ministry of Science and the Arts