TY  - JOUR
AB  - 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.
AU  - Contento, L.*
AU  - Castelletti, N.*
AU  - Raimundez, E.*
AU  - Le Gleut, R.
AU  - Schälte, Y.
AU  - Stapor, P.
AU  - Hinske, L.C.*
AU  - Hoelscher, M.*
AU  - Wieser, A.*
AU  - Radon, K.*
AU  - Fuchs, C.
AU  - Hasenauer, J.
C1  - 67716
C2  - 54024
CY  - Radarweg 29, 1043 Nx Amsterdam, Netherlands
TI  - Integrative modelling of reported case numbers and seroprevalence reveals time-dependent test efficiency and infectious contacts.
JO  - Epidemics
VL  - 43
PB  - Elsevier
PY  - 2023
SN  - 1755-4365
ER  - 

TY  - JOUR
AB  - 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.
AU  - Raimúndez, E.*
AU  - Dudkin, E.*
AU  - Vanhoefer, J.*
AU  - Alamoudi, E.*
AU  - Merkt, S.*
AU  - Fuhrmann, L.*
AU  - Bai, F.*
AU  - Hasenauer, J.
C1  - 61250
C2  - 50109
CY  - Radarweg 29, 1043 Nx Amsterdam, Netherlands
TI  - COVID-19 outbreak in Wuhan demonstrates the limitations of publicly available case numbers for epidemiological modeling.
JO  - Epidemics
VL  - 34
PB  - Elsevier
PY  - 2021
SN  - 1755-4365
ER  -