EPITIME: A Computational Framework for Integral Epidemic Models with Structure-Preserving Discretizations

TL;DR

EPITIME is a computational framework that enables accurate simulation of integral epidemic models, preserving key properties and supporting analysis of disease dynamics and behavioral responses.

Contribution

It introduces structure-preserving discretizations and modular implementations in MATLAB and Python for integral epidemic models, ensuring qualitative property preservation.

Findings

Preserves positivity, boundedness, and invariant regions regardless of time step.

Demonstrates accurate long-term behavior and convergence in simulations.

Enables inverse reconstruction of infectivity kernels from COVID-19 data.

Abstract

We present EPITIME (EPidemic Integral models TIMe profile Explorer), a computational framework for the simulation of two classes of integral epidemic models: an age of infection model and an information dependent behavioural model. The framework combines structure preserving Non-Standard Finite Difference discretizations with modular implementations in MATLAB and Python, together with routines for parameter handling, input validation, performance assessment, and graphical interaction. The proposed methods preserve key qualitative properties of the continuous problems, including positivity, boundedness, invariant regions, and correct long term behaviour, independently of the time step. We outline the numerical schemes for both model classes and their main analytical properties, including first order convergence. We then describe the software architecture and illustrate its use through numerical experiments on asymptotic behaviour, inverse reconstruction of an infectivity kernel from COVID 19 incidence data, and behavioural dynamics under different memory kernels. Overall, EPITIME provides a reliable and accessible computational environment for the numerical study of renewal epidemic models.