A Hybrid Compartmental Model with a Case Study of COVID-19 in Great Britain and Israel

TL;DR

This paper develops a flexible, network-based compartmental model to analyze COVID-19 dynamics under various restrictions, using data from Great Britain and Israel to inform policy decisions.

Contribution

It introduces a generalizable, decomposable model capable of simulating different restriction scenarios across diverse populations.

Findings

Model effectively captures COVID-19 spread dynamics.

Decomposition strategy reduces computational complexity.

Applicable to multiple countries with different social structures.

Abstract

Given the severe impact of COVID-19 on several societal levels, it is of crucial importance to model the impact of restriction measures on the pandemic evolution, so that governments are able to take informed decisions. Even though there have been countless attempts to propose diverse models since the raise of the outbreak, the increase in data availability and start of vaccination campaigns calls for updated models and studies. Furthermore, most of the works are focused on a very particular place or application and we strive to attain a more general model, resorting to data from different countries. In particular, we compare Great Britain and Israel, two highly different scenarios in terms of vaccination plans and social structure. We build a network-based model, complex enough to model different scenarios of government-mandated restrictions, but generic enough to be applied to any population. To ease the computational load we propose a decomposition strategy for our model.