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Contento, L.* ; Castelletti, N.* ; Raimundez, E.* ; Le Gleut, R. ; Schälte, Y. ; Stapor, P. ; Hinske, L.C.* ; Hoelscher, M.* ; Wieser, A.* ; Radon, K.* ; Fuchs, C. ; Hasenauer, J.

Integrative modelling of reported case numbers and seroprevalence reveals time-dependent test efficiency and infectious contacts.

Epidemics 43:100681 (2023)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 1755-4365
e-ISSN 1878-0067
Zeitschrift Epidemics
Quellenangaben Band: 43, Heft: , Seiten: , Artikelnummer: 100681 Supplement: ,
Verlag Elsevier
Verlagsort Radarweg 29, 1043 Nx Amsterdam, Netherlands
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
CF Statistical Consulting (CF-STATCON)
POF Topic(s) 30205 - Bioengineering and Digital Health
30505 - New Technologies for Biomedical Discoveries
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-553800-001
G-503800-001
A-632200-001
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
DOI 10.1016/j.epidem.2023.100681
WOS ID 000960932900001
Scopus ID 85150026665
PubMed ID 36931114
Erfassungsdatum 2023-10-06
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