On the Coevolution Between Social Network Structure and Diffusion of the Coronavirus (COVID-19) in Spatial Compartmental Epidemic Models

Giorgio Fagiolo

doi:10.3389/fhumd.2022.825665

Abstract

In this article, the author studies epidemic diffusion in a spatial compartmental model, where individuals are initially connected in a social or geographical network. As the virus spreads in the network, the structure of interactions between people may endogenously change over time, due to quarantining measures and/or spatial-distancing (SD) policies. The author explores via simulations the dynamic properties of the coevolutionary process linking disease diffusion and network properties. Results suggest that, in order to predict how epidemic phenomena evolve in networked populations, it is not enough to focus on the properties of initial interaction structures. Indeed, the coevolution of network structures and compartment shares strongly shape the process of epidemic diffusion, especially in terms of its speed. Furthermore, the author shows that the timing and features of SD policies may dramatically influence their effectiveness.

Highlights

In the last two years, the still ongoing diffusion of the Coronavirus disease 2019 (COVID-19) pandemic has spurred a large body of scientific contributions, attempting to explore how compartmental models (Keeling and Rohani, 2008; Pastor-Satorras et al, 2015; Kiss et al, 2017) can reproduce and predict the spread of the epidemics in different countries and regions (Adam, 2020; Kousha and Thelwall, 2020).Most of this work has been focusing on models in which the mixing process between people in different states or compartments does not depend on the social or geographical space where they are embedded in
As the virus spreads in the network, the structure of interactions between people may change over time, due to quarantining measures and/or SD policies, which may possibly introduce a coevolutionary effect dynamically linking disease diffusion and network properties (Achterberg et al, 2020; Horstmeyer et al, 2020; Corcoran and Clark, 2021)
Irrespective of the initial network structure, the population converges to a similar share of deaths, but in ER and SF networks, a small percentage of S people still remains

Summary

Introduction

In the last two years, the still ongoing diffusion of the Coronavirus disease 2019 (COVID-19) pandemic has spurred a large body of scientific contributions, attempting to explore how compartmental models (Keeling and Rohani, 2008; Pastor-Satorras et al, 2015; Kiss et al, 2017) can reproduce and predict the spread of the epidemics in different countries and regions (Adam, 2020; Kousha and Thelwall, 2020).Most of this work has been focusing on models in which the mixing process between people in different states or compartments does not depend on the social or geographical space where they are embedded in. As the virus spreads in the network, the structure of interactions between people may change over time, due to quarantining measures and/or SD policies, which may possibly introduce a coevolutionary effect dynamically linking disease diffusion and network properties (Achterberg et al, 2020; Horstmeyer et al, 2020; Corcoran and Clark, 2021). Motivated by these observations, the paper introduces a generalized spatial susceptible, exposed, infected, recovered, dead (SEIRD) model that, besides the standard four compartments (susceptible, exposed, infected, recovered, dead), considers an additional “quarantined” state, i.e., a susceptible, exposed, infected, quarantined, recovered, dead (SEIQRD) model.

Methods

Results

Conclusion