TY - JOUR
AU - Sharma, Sudhi
AU - Dolean, Victorita
AU - Jolivet, Pierre
AU - Robinson, Brandon
AU - Edwards, Jodi D.
AU - Kendzerska, Tetyana
AU - Sarkar, Abhijit
PY - 2023
DA - July
TI - Scalable computational algorithms for geospatial COVID-19 spread using high performance computing
JO - Mathematical Biosciences and Engineering
EP - 14674
VL - 20
IS - 8
DO - https://doi.org/10.3934/mbe.2023655
AB - A nonlinear partial differential equation (PDE) based compartmental model of COVID-19 provides a continuous trace of infection over space and time. Finer resolutions in the spatial discretization, the inclusion of additional model compartments and model stratifications based on clinically relevant categories contribute to an increase in the number of unknowns to the order of millions. We adopt a parallel scalable solver that permits faster solutions for these high fidelity models. The solver combines domain decomposition and algebraic multigrid preconditioners at multiple levels to achieve the desired strong and weak scalabilities. As a numerical illustration of this general methodology, a five-compartment susceptible-exposed-infected-recovered-deceased (SEIRD) model of COVID-19 is used to demonstrate the scalability and effectiveness of the proposed solver for a large geographical domain (Southern Ontario). It is possible to predict the infections for a period of three months for a system size of 186 million (using 3200 processes) within 12 hours saving months of computational effort needed for the conventional solvers.
PB - AIMS Press
UR - https://strathprints.strath.ac.uk/86198/
KW - Coronavirus (COVID-19)
KW - Digital health and technology
ER