A Social Network Model of COVID-19

Abstract

I construct a dynamic social-network based model of the COVID-19 epidemic which embeds the SIR epidemiological model onto a graph of person-to-person interactions. The standard SIR framework assumes uniform mixing of infectious persons in the population. This abstracts from important elements of realism and locality: (i) people are more likely to interact with members of their social networks and (ii) health and economic policies can affect differentially the rate of viral transmission via a person's social network vs. the population as a whole. The proposed network-augmented (NSIR) model allows the evaluation, via simulations, of (i) health and economic policies and outcomes for all or subset of the population: herd immunity, testing, contact tracing, lockdown/distancing; (ii) behavioral responses and/or imposing or lifting policies at a specific time or conditional on observed states. As the NSIR model keeps track of individual states, an economic cost-benefit module and agent heterogeneity (e.g., in savings, employment status; ability to pay bills) is easily incorporated. I find that viral transmission over a network-connected population can proceed slower and reach lower peak than transmission via uniform contacts. The resulting longer epidemic duration may imply larger overall economic costs, e.g., if accompanied by prolonged lockdown policies. If lifted early, distancing policies mostly shift the infection peak into the future with associated economic costs. Delayed or intermittent (on-off-on) interventions or endogenous behavioral responses can lead to a twin-peaked infection rate, a form of 'curve flattening', but may have costlier economic consequences by prolonging the epidemic duration.

Keywords: COVID-19, social network, SIR model, lockdown, distancing, testing, contact tracing