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Scalable computational algorithms for geospatial COVID-19 spread using high performance computing.

Sudhi Sharma1, Victorita Dolean2,3, Pierre Jolivet4

  • 1Department of Civil and Environmental Engineering, Carleton University, Ottawa, Ontario, Canada.

Mathematical Biosciences and Engineering : MBE
|September 7, 2023
PubMed
Summary
This summary is machine-generated.

A novel parallel solver accelerates high-fidelity COVID-19 modeling by efficiently handling millions of unknowns in complex compartmental models, enabling faster infection predictions.

Keywords:
COVID-19high performance computingoverlapping Schwarz methodspatio-temporal model

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Area of Science:

  • Computational epidemiology
  • Mathematical modeling of infectious diseases

Background:

  • COVID-19 modeling requires high-resolution spatial and compartmental detail.
  • Complex models with millions of unknowns pose significant computational challenges.

Purpose of the Study:

  • To develop and validate a parallel scalable solver for high-fidelity COVID-19 compartmental models.
  • To demonstrate the solver's effectiveness for large-scale geographical predictions.

Main Methods:

  • Utilized a nonlinear partial differential equation (PDE) based compartmental model.
  • Implemented a parallel scalable solver combining domain decomposition and algebraic multigrid preconditioners.
  • Applied a five-compartment susceptible-exposed-infected-recovered-deceased (SEIRD) model for numerical illustration.

Main Results:

  • The solver achieved strong and weak scalability for high-fidelity models.
  • Successfully predicted COVID-19 infections over three months for a large geographical domain (Southern Ontario).
  • A system size of 186 million unknowns was solved using 3200 processes within 12 hours.

Conclusions:

  • The proposed parallel solver significantly reduces computational time for complex COVID-19 models.
  • Enables efficient, large-scale epidemiological forecasting with high spatial and compartmental resolution.
  • Offers a powerful tool for public health preparedness and response strategies.