Modeling the evolution of COVID-19 via compartmental and particle-based approaches: application to the Cyprus case

TL;DR

This paper introduces two modeling approaches for COVID-19 spread, one based on differential equations and the other on particle simulations, applied to Cyprus data to validate their effectiveness.

Contribution

It presents a novel combination of compartmental and particle-based models for COVID-19, tailored to incorporate testing levels and policy restrictions.

Findings

Both models produce similar results, validating their predictive capability.

Models effectively incorporate country-specific data and restrictions.

The approaches are adaptable to different population subgroups.

Abstract

We present two different approaches for modeling the spread of the COVID-19 pandemic. Both approaches are based on the population classes susceptible, exposed, infectious, quarantined, and recovered and allow for an arbitrary number of subgroups with different infection rates and different levels of testing. The first model is derived from a set of ordinary differential equations that incorporates the rates at which population transitions take place among classes. The other is a particle model, which is a specific case of crowd simulation model, in which the disease is transmitted through particle collisions and infection rates are varied by adjusting the particle velocities. The parameters of these two models are tuned using information on COVID-19 from the literature and country-specific data, including the effect of restrictions as they were imposed and lifted. We demonstrate the applicability of both models using data from Cyprus, for which we find that both models yield very similar results, giving confidence in the predictions.