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A nonlinear partial differential equation (PDE) based compartmental model ofCOVID-19 provides a continuous trace of infection over space and time. Finerresolutions in the spatial discretization, the inclusion of additional modelcompartments and model stratifications based on clinically relevant categoriescontribute to an increase in the number of unknowns to the order of millions.We adopt a parallel scalable solver allowing faster solutions for these highfidelity models. The solver combines domain decomposition and algebraicmultigrid preconditioners at multiple levels to achieve the desired strong andweak scalability. As a numerical illustration of this general methodology, afive-compartment susceptible-exposed-infected-recovered-deceased (SEIRD) modelof COVID-19 is used to demonstrate the scalability and effectiveness of theproposed solver for a large geographical domain (Southern Ontario). It ispossible to predict the infections up to three months for a system size of 92million (using 1780 processes) within 7 hours saving months of computationaleffort needed for the conventional solvers.

Scalable Computational Algorithms for Geo-spatial Covid-19 Spread in High Performance Computing