Transitioning Out of the Coronavirus Lockdown: A Framework for Evaluating Zone-Based Social Distancing.

Eric Friedman,Adam Landsberg,Simon Johnson,John Friedman

doi:10.3389/fpubh.2020.00266

Abstract

In the face of elevated pandemic risk, canonical epidemiological models imply the need for extreme social distancing over a prolonged period. Alternatively, people could be organized into zones, with more interactions inside their zone than across zones. Zones can deliver significantly lower infection rates, with less social distancing, particularly if combined with simple quarantine rules and contact tracing. This paper provides a framework for understanding and evaluating the implications of zones, quarantines, and other complementary policies.

Highlights

As a result of its implications for health and mortality, the Coronavirus disease (COVID-19) pandemic has triggered massive disruptions to both economies and social structures [1]
We present a simple epidemiological model of “zonal social distancing” that offers a framework for assessing the efficacy of zone-based policies
If R0 < 1 the epidemic will typically die out after infecting only a small number of nodes, while if R0 > 1 the epidemic is likely to spread widely, infecting a significant fraction of the population. We extend this idea and compute RZ, the inter-zonal reproduction number, which captures the interaction between how zones are structured and—crucially—the speed with which people within a zone can be separated from other zones should an infection enter that zone

Summary

INTRODUCTION

As a result of its implications for health and mortality, the Coronavirus disease (COVID-19) pandemic has triggered massive disruptions to both economies and social structures [1]. If R0 < 1 the epidemic will typically die out after infecting only a small number of nodes, while if R0 > 1 the epidemic is likely to spread widely, infecting a significant fraction of the population We extend this idea and compute RZ, the inter-zonal reproduction number, which captures the interaction between how zones are structured and—crucially—the speed with which people within a zone can be separated from other zones should an infection enter that zone. The zonal policy is still likely to be significantly more effective We simulated this simple example, assuming people violate the zone rules 2% of the time, by interacting with someone in another zone (Details are in the Technical Appendix). As the number of cases ebbs, one could merge zones or gradually increase the number of allowed inter-zonal interactions

CONCLUSIONS

Findings

24. Coronavirus