Towards Graph Neural Network Surrogates Leveraging Mechanistic Expert Knowledge for Pandemic Response

• URL: http://arxiv.org/abs/2411.06500v1
• Date: Sun, 10 Nov 2024 15:54:09 GMT
• Title: Towards Graph Neural Network Surrogates Leveraging Mechanistic Expert Knowledge for Pandemic Response
• Authors: Agatha Schmidt, Henrik Zunker, Alexander Heinlein, Martin J. Kühn,
• Abstract summary: We build upon a spatially and demographically resolved infectious disease model and train a graph neural network for data sets representing early phases of the pandemic. The suggested approach yields potential for on-the-fly execution and, thus, integration of disease dynamics models in low-barrier website applications.
• Score: 41.94295877935867
• License:
• Abstract: During the COVID-19 crisis, mechanistic models have been proven fundamental to guide evidence-based decision making. However, time-critical decisions in a dynamically changing environment restrict the time available for modelers to gather supporting evidence. As infectious disease dynamics are often heterogeneous on a spatial or demographic scale, models should be resolved accordingly. In addition, with a large number of potential interventions, all scenarios can barely be computed on time, even when using supercomputing facilities. We suggest to combine complex mechanistic models with data-driven surrogate models to allow for on-the-fly model adaptations by public health experts. We build upon a spatially and demographically resolved infectious disease model and train a graph neural network for data sets representing early phases of the pandemic. The resulting networks reached an execution time of less than a second, a significant speedup compared to the metapopulation approach. The suggested approach yields potential for on-the-fly execution and, thus, integration of disease dynamics models in low-barrier website applications. For the approach to be used with decision-making, datasets with larger variance will have to be considered.

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