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.
Highlights
I construct and compute a dynamic social network-based model of the COVID-19 epidemic and use it to evaluate a range of simulated health and economic policies—herd immunity, distancing, lockdown, testing, quarantine, and contact tracing
Relative to the SIR model, the network structure and degree heterogeneity introduces uncertainty and unpredictability in the epidemic dynamics and duration as well as in policy outcomes, since the infection can spread in a non-uniform, state-dependent way
The immediate change in Rt because of a superspreader node turning infectious is compared to the average preceding Rt change in row 4. These results show that superspreaders can lead to significant ‘jumps’ in Rt in the NSIR model
Summary
I construct and compute a dynamic social network-based model of the COVID-19 epidemic and use it to evaluate a range of simulated health and economic policies—herd immunity, distancing, lockdown, testing, quarantine, and contact tracing. Relative to the SIR model, the network structure and degree heterogeneity introduces uncertainty and unpredictability in the epidemic dynamics and duration as well as in policy outcomes, since the infection can spread in a non-uniform, state-dependent way. Viral transmission over a network-connected population can proceed slower and reach a lower peak than transmission via uniform/random contacts as assumed by standard SIR models This is consistent with the findings of [17] using New York social interactions data. Behavioral responses, through reducing the number or rate of social contacts based on observed infections, on aggregate or in one’s own network, can be a powerful and economically less costly alternative to mandated lockdowns but could induce a cyclical pattern of tightening and relaxation over a prolonged period
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