ArticleEpidemics2023
Integrative modelling of reported case numbers and seroprevalence reveals time-dependent test efficiency and infectious contacts.
Article in Epidemics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Coupled epidemiological and wastewater modeling at the urban scale: A case study for Munich.iScience · 2026Article
- Improving epidemiological projections for infectious diseases in Ghana: addressing methodological challenges.Global health research and policy · 2025Article
- Assessment of uncertainty quantification in universal differential equations.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Novel travel time aware metapopulation models and multi-layer waning immunity for late-phase epidemic and endemic scenarios.PLoS computational biology · 2024Article
- How European Research Projects Can Support Vaccination Strategies: The Case of the ORCHESTRA Project for SARS-CoV-2.Vaccines · 2023Article
- Regional opening strategies with commuter testing and containment of new SARS-CoV-2 variants in Germany.BMC infectious diseases · 2022Article
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Authors and funding
13 authors.
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Abstract
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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