Utilization of spatially resolved data sources for an established agent-based model of Germany and its impact on predicted SARS-CoV-2 dynamics
Data Management, Data-Intensive Systems, Computer Science Methods in Business Informatics
Final Report Abstract
Dynamic infection spread models are a key tool to support decision-makers in epidemically occurring infectious diseases. They synthesise the best currently available evidence with the aid of a mechanistic model to compare possible intervention strategies in realistic scenarios (so-called scenario modelling) or to obtain estimates of new infections to be expected within a defined period (so-called forecast modelling). Agent-based models offer the possibility of modelling infection events in a high degree of detail but require an equally detailed data basis. Due to the challenges involved, large agent-based models were rarely used for forecast modelling before and during the COVID-19 pandemic. In Space_impact, we used a global agent-based model developed for scenario modelling and extended it with an interface that allows the parameterisation of all spatially heterogeneous variables at the county level. The resulting modelling platform allows real-time daily forecasts of expected cases of new infections, hospitalisations, and intensive care patients over 7 to 30 days at the county level and allows decision-makers to interact directly via a newly developed dashboard. The developed model was extensively externally validated and demonstrated high regional prediction accuracy. However, incorporating regionally stratified data in real-time became increasingly difficult as, for example, mobility data became unavailable during the COVID-19 pandemic. As a result, we developed a dedicated model for deriving regional contact behaviour and mobility patterns based on contact studies conducted outside of Space_impact to provide contact and mobility behaviour in real-time, which is a core component of the higher-level modelling platform. Theoretical results of Space_impact were directly transferred into practical application in the BMBF projects OptimAgent and RespiNow, while several new methodological challenges were identified.
Publications
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Bridging the gap – estimation of 2022/2023 SARS-CoV-2 healthcare burden in Germany based on multidimensional data from a rapid epidemic panel. Cold Spring Harbor Laboratory.
Harries, M.; Jaeger, V.K.; Rodiah, I.; Hassenstein, M.J.; Ortmann, J.; Dreier, M.; von Holt, I.; Brinkmann, M.; Dulovic, A.; Gornyk, D.; Hovardovska, O.; Kuczewski, C.; Kurosinki, MA; Schlotz, M.; Schneiderhan-Marra, N.; Strengert, M.; Krause, G.; Sester, M.; Klein, F. ... & Lange, B.
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Effect of risk status for severe COVID-19 on individual contact behaviour during the SARS-CoV-2 pandemic in 2020/2021—an analysis based on the German COVIMOD study. BMC Infectious Diseases, 23(1).
Walde, Jasmin; Chaturvedi, Madhav; Berger, Tom; Bartz, Antonia; Killewald, Robin; Tomori, Damilola Victoria; Rübsamen, Nicole; Lange, Berit; Scholz, Stefan; Treskova, Marina; Bucksch, Karolin; Jarvis, Christopher I.; Mikolajczyk, Rafael; Karch, André & Jaeger, Veronika K.
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EpiPredict: Agent-Based Modeling of Infectious Diseases. KI -Künstliche Intelligenz, 38(3), 177-181.
Suer, Janik; Ponge, Johannes & Hellingrath, Bernd
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Estimating fine age structure and time trends in human contact patterns from coarse contact data: The Bayesian rate consistency model. PLOS Computational Biology, 19(6), e1011191.
Dan, Shozen; Chen, Yu; Chen, Yining; Monod, Melodie; Jaeger, Veronika K.; Bhatt, Samir; Karch, André & Ratmann, Oliver
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Social contact patterns during the COVID-19 pandemic in 21 European countries – evidence from a two-year study. BMC Infectious Diseases, 23(1).
Wong, Kerry L. M.; Gimma, Amy; Coletti, Pietro; Paolotti, Daniela; Tizzani, Michele; Cattuto, Ciro; Schmidt, Andrea; Gredinger, Gerald; Stumpfl, Sophie; Baruch, Joaquin; Melillo, Tanya; Hudeckova, Henrieta; Zibolenova, Jana; Chladna, Zuzana; Rosinska, Magdalena; Niedzwiedzka-Stadnik, Marta; Fischer, Krista; Vorobjov, Sigrid ... & Jarvis, Christopher I.
