Adaptive Mesh Refinement and Coarsening for Diffusion-Reaction Epidemiological Models

TL;DR

This paper extends traditional SEIRD epidemiological models to a diffusion-reaction PDE framework, incorporating spatial dynamics, travel-related sources, and adaptive mesh refinement to better simulate COVID-19 spread.

Contribution

It introduces a PDE-based SEIRD model with travel and anisotropic diffusion, implemented with adaptive mesh refinement in libMesh for multi-scale spatial analysis.

Findings

Model captures spatial heterogeneity of COVID-19 spread.

Adaptive mesh improves computational efficiency and resolution.

Demonstrates new capabilities over standard SEIRD models.

Abstract

The outbreak of COVID-19 in 2020 has led to a surge in the interest in the mathematical modeling of infectious diseases. Disease transmission may be modeled as compartmental models, in which the population under study is divided into compartments and has assumptions about the nature and time rate of transfer from one compartment to another. Usually, they are composed of a system of ordinary differential equations (ODE's) in time. A class of such models considers the Susceptible, Exposed, Infected, Recovered, and Deceased populations, the SEIRD model. However, these models do not always account for the movement of individuals from one region to another. In this work, we extend the formulation of SEIRD compartmental models to diffusion-reaction systems of partial differential equations to capture the continuous spatio-temporal dynamics of COVID-19. Since the virus spread is not only through diffusion, we introduce a source term to the equation system, representing exposed people who return from travel. We also add the possibility of anisotropic non-homogeneous diffusion. We implement the whole model in \texttt{libMesh}, an open finite element library that provides a framework for multiphysics, considering adaptive mesh refinement and coarsening. Therefore, the model can represent several spatial scales, adapting the resolution to the disease dynamics. We verify our model with standard SEIRD models and show several examples highlighting the present model's new capabilities.