A full software stack for epidemic disease management: Unlocking the joint potential of software technology and supercomputing
Authors:
Jonas Gilg,
Johann Fredrik Jadebeck,
Mariama Jaiteh,
David Kerkmann,
Niklas Medinger,
Shahbaz Memon,
Anna Wendler,
Moritz Zeumer,
Henrik Zunker,
Maximilian Betz,
Ralf Hannemann-Tamas,
Jonas Immanuel Heinicke,
Julian Litz,
Achim Basermann,
Cas Cremers,
Manuel Dahmen,
Andreas Gerndt,
Jens Henrik Göbbert,
Björn Hagemeier,
Carolina J. Klett-Tammen,
Berit Lange,
Katharina Nöh,
Sarah Straßburger,
Michael Meyer-Hermann,
Martin J. Kühn
Abstract:
Infectious diseases remain a major threat to human societies. During the recent COVID-19 pandemic, mathematical modeling and extensive computer simulations proved highly effective in supporting public health experts and decision makers.
Despite these advances, the full potential of modern modeling approaches and digital technologies has not yet been realized. Many critical tasks -- including exp…
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Infectious diseases remain a major threat to human societies. During the recent COVID-19 pandemic, mathematical modeling and extensive computer simulations proved highly effective in supporting public health experts and decision makers.
Despite these advances, the full potential of modern modeling approaches and digital technologies has not yet been realized. Many critical tasks -- including expert consultations, model execution, scenario analyses, report preparation, and result communication -- still relied heavily on manual, human-driven processes with each manual interaction introducing avoidable delays and limiting responsiveness during rapidly evolving outbreaks.
Pandemic preparedness should opt for automated workflows and seamlessly integrated software modules that can improve pandemic mitigation capabilities by substantially reducing response times. For this step, we require robust and flexible computational infrastructure capable of supporting heterogeneous hardware and continuously evolving infectious-disease models. In addition, data sources need to be dynamically integrated. Managing such demands needs infrastructure that supports automated high-performance computing (HPC) workflows. Beyond computational performance, software infrastructure must ensure secure user and data management to comply with data-protection regulations and provide clear, transparent presentation of results to both decision makers and the public.
Meeting the aforementioned challenges requires tight integration of state-of-the-art scientific software with modern, scalable infrastructure that can leverage supercomputing resources when necessary. For rapid deployment in future epidemic or pandemic scenarios, adherence to the FAIR principles for research software is critical to ensure reusability and sustainability.
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Submitted 27 March, 2026;
originally announced August 2026.
Simulation-based inference for rapid Bayesian parameter estimation in epidemiological models: a comparison with MCMC
Authors:
Alina Bazarova,
Johann Fredrik Jadebeck,
Henrik Zunker,
Carolina J. Klett-Tammen,
Torben Heinsohn,
Wolfgang Wiechert,
Katharina Noeh,
Stefan Kesselheim
Abstract:
Mechanistic epidemiological models are widely used to support infectious disease forecasting and public-health decision making. Bayesian calibration of such models is commonly performed using Markov chain Monte Carlo (MCMC), which can become computationally expensive for high-dimensional nonlinear systems and repeated near-real-time analyses. Here, we investigate simulation-based inference (SBI) u…
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Mechanistic epidemiological models are widely used to support infectious disease forecasting and public-health decision making. Bayesian calibration of such models is commonly performed using Markov chain Monte Carlo (MCMC), which can become computationally expensive for high-dimensional nonlinear systems and repeated near-real-time analyses. Here, we investigate simulation-based inference (SBI) using neural posterior estimation as a scalable alternative for Bayesian calibration of a mechanistic SECIR epidemiological model using COVID-19 intensive care unit (ICU) occupancy data from Germany during 2020. We compared SBI and MCMC across multiple epidemic phases using both 31-day inference windows and a substantially more challenging 201-day reconstruction problem involving multiple transmission change points. Posterior agreement was evaluated quantitatively using Wasserstein distances and Kullback-Leibler divergences together with posterior predictive checks. Across the 31-day windows, SBI recovered posterior distributions in strong agreement with MCMC while accurately reproducing observed ICU trajectories. In the 201-day setting, SBI preserved the dominant posterior structure despite increased uncertainty. SBI, by combining CPU and GPU resources, substantially reduced computational runtime compared with MCMC, which was restricted to running on CPUs. Whereas MCMC required approximately 1000 seconds for the 31-day inference problems, SBI achieved comparable posterior and predictive performance in approximately 60-70 seconds on a single GPU. For the 201-day inference problem, SBI required an average of 157 seconds, while the MCMC runs took over 19,000 seconds. Our results demonstrate that SBI provides a rapid and computationally efficient framework for Bayesian calibration of mechanistic epidemiological models, supporting repeated near-real-time inference and rapid outbreak analysis.
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Submitted 25 June, 2026;
originally announced June 2026.