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

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 re...

Full description

Saved in:
Bibliographic Details
Published in:Epidemics 2023-06, Vol.43, p.100681, Article 100681
Main Authors: Contento, Lorenzo, Castelletti, Noemi, Raimúndez, Elba, Le Gleut, Ronan, Schälte, Yannik, Stapor, Paul, Hinske, Ludwig Christian, Hoelscher, Michael, Wieser, Andreas, Radon, Katja, Fuchs, Christiane, Hasenauer, Jan
Format: Article
Language:English
Subjects:
Bayes Theorem
Compartmental model
COVID-19
COVID-19 - epidemiology
Humans
Models, Theoretical
Parameter estimation
SARS-CoV-2
Seroepidemiologic Studies
Uncertainty quantification
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1755-4365
1878-0067
1878-0067
DOI:10.1016/j.epidem.2023.100681