PYSIRTEM: An Efficient Modular Simulation Platform for the Analysis of Pandemic Scenarios

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Abstract
Conventional population-based ODE models struggle against increased level of resolution since incorporating many states exponentially increases computational costs, and demands robust calibration for numerous hyperparameters. PySIRTEM is a spatiotemporal SEIR-based epidemic simulation platform that provides high resolution analysis of viral disease progression and mitigation. Based on the authors-developed Matlab© simulator SIRTEM, PySIRTEM's modular design reflects key health processes, including infection, testing, immunity, and hospitalization, enabling flexible manipulation of transition rates. Unlike SIRTEM, PySIRTEM uses a Sequential Monte Carlo (SMC) particle filter to dynamically learn epidemiological parameters using historical COVID-19 data from several U.S. states. The improved accuracy (by orders of magnitude) make PySIRTEM ideal for informed decision-making by detecting outbreaks and fluctuations. We further demonstrate PySIRTEM 's usability performing a factorial analysis to assess the impact of different hyperparameter configurations on the predicted epidemic dynamics. Finally, we analyze containment scenarios with varying trends, showcasing PySIRTEM 's adaptability and effectiveness.
Funding Information
  • NSF DBI (2412115)

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