arXiv subjects

Anna Wendler

Publications and source records attributed to Anna Wendler.

4 recordsLinked to original sources

arXiv2026

A full software stack for epidemic disease management: Unlocking the joint potential of software technology and supercomputing

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.

cs.CE↗

arXiv2026

MEmilio -- A high performance Modular EpideMIcs simuLatIOn software for multi-scale and comparative simulations of infectious disease dynamics

Epidemic and pandemic preparedness with rapid outbreak response rely on timely, trustworthy evidence. Mathematical models are crucial for supporting timely and reliable evidence generation for public health decision-making with models spanning approaches from compartmental and metapopulation models to detailed agent-based simulations. Yet, the accompanying software ecosystem remains fragmented across model types, spatial resolutions, and computational targets, making models harder to compare, extend, and deploy at scale. Here we present MEmilio, a modular, high-performance framework for epidemic simulation that harmonizes the specification and execution of diverse dynamic epidemiological models within a unified and harmonized architecture. MEmilio couples an efficient C++ simulation core with coherent model descriptions and a user-friendly Python interface, enabling workflows that run on laptops as well as high-performance computing systems. Standardized representations of space, demography, and mobility support straightforward adaptations in resolution and population size, facilitating systematic inter-model comparisons and ensemble studies. The framework integrates readily with established tools for uncertainty quantification and parameter inference, supporting a broad range of applications from scenario exploration to calibration. Finally, strict software-engineering practices, including extensive unit and continuous integration testing, promote robustness and minimize the risk of errors as the framework evolves. By unifying implementations across modeling paradigms, MEmilio aims to lower barriers to reuse and generalize models, enable principled comparisons of implicit assumptions, and accelerate the development of novel approaches that strengthen modeling-based outbreak preparedness.

q-bio.PE↗

arXiv2024

Revisiting the Linear Chain Trick in epidemiological models: Implications of underlying assumptions for numerical solutions

In order to simulate the spread of infectious diseases, many epidemiological models use systems of ordinary differential equations (ODEs) to describe the underlying dynamics. These models incorporate the implicit assumption, that the stay time in each disease state follows an exponential distribution. However, a substantial number of epidemiological, data-based studies indicate that this assumption is not plausible. One method to alleviate this limitation is to employ the Linear Chain Trick (LCT) for ODE systems, which realizes the use of Erlang distributed stay times. As indicated by data, this approach allows for more realistic models while maintaining the advantages of using ODEs. In this work, we propose an advanced LCT SECIR-type model incorporating eight infection states with demographic stratification. We review key properties of the corresponding LCT model and demonstrate that predictions derived from a simple ODE-based model can be significantly distorted, potentially leading to wrong political decisions. Our findings demonstrate that the influence of distribution assumptions on the behavior at change points and on the prediction of epidemic peaks is substantial, while the assumption has no effect on the final size of the epidemic. With respect to prior findings in literature, we demonstrate that the influence of the number of subcompartments on the timing and size of the epidemic peak is nontrivial and that a general statement cannot be obtained. We, then, show how these age-resolved LCT SECIR-type models capture the spread of SARS-CoV-2 in Germany in 2020. Eventually, we study the implications on the time-to-solution for different LCT models using fixed and adaptive step-size Runge-Kutta methods and provide computational performance for these models in the MEmilio software framework, also using distributed memory parallelism to speed up ensemble runs.

math.DS↗

arXiv2024

A nonstandard numerical scheme for a novel SECIR integro-differential equation-based model allowing nonexponentially distributed stay times

Ordinary differential equations (ODE) are a popular tool to model the spread of infectious diseases, yet they implicitly assume an exponential distribution to describe the flow from one infection state to another. However, scientific experience yields more plausible distributions where the likelihood of disease progression changes accordingly with the duration spent in a particular state of the disease. Furthermore, transmission dynamics depend heavily on the infectiousness of individuals. The corresponding nonlinear variation with the time individuals have already spent in an infectious state requires more realistic models. The previously mentioned items are particularly crucial when modeling dynamics at change points such as the implementation of nonpharmaceutical interventions. In order to capture these aspects and to enhance the accuracy of simulations, integro-differential equations (IDE) can be used. In this paper, we propose a generalized model based on IDEs with eight infection states. The model allows for variable stay time distributions and generalizes the concept of ODE-based models as well as IDE-based age-of-infection models. In this, we include particular infection states for severe and critical cases to allow for surveillance of the clinical sector, avoiding bottlenecks and overloads in critical epidemic situations. On the other hand, a drawback of IDE-based models is that efficient numerical solvers are not as widely available. We extend a recently introduced nonstandard numerical scheme. This scheme is adapted to our more advanced model and we prove important mathematical and biological properties. Furthermore, we validate our approach numerically by demonstrating the convergence rate. Eventually, we also show that our novel model is intrinsically capable of better assessing disease dynamics upon the introduction of nonpharmaceutical interventions.

math.NA↗