Critical Thresholds in Non-Pharmaceutical Interventions for Epidemic Control

TL;DR

This paper develops a probabilistic framework to identify critical thresholds for non-pharmaceutical interventions like contact tracing and social distancing, enabling better epidemic control strategies based on empirical data.

Contribution

It introduces a novel probabilistic model analyzing the interaction between contact tracing speed and social distancing, validated with real outbreak data, to determine effective epidemic control thresholds.

Findings

Contact tracing alone can contain diseases with R0 < 2.12

Combining contact tracing with social distancing extends control to R0 < 7.82

Empirical data supports targeted NPIs as effective alternatives to mass testing

Abstract

Non-pharmaceutical interventions, such as contact tracing and social distancing, are critical for controlling epidemic outbreaks, yet their dynamic interactions remain underexplored. We introduce a probabilistic framework to analyze the synergy between contact tracing speed, quantified by the contact tracing period $\tau$, and the average number of close contacts, $\bar{k}_+$, reflecting social distancing measures. We identify critical thresholds ($R=1$) that separate pandemic and contained phases in the $\bar{k}_{+}-\tau$ plane, validated using high-resolution data from Shenzhen's 2022 Omicron outbreak (1,187 cases, 86,451 contacts). Our findings show that contact tracing alone can contain diseases with $R_0 < 2.12$ (95% CI 2.07-2.16), covering 43.33% of major infectious diseases, while combining with social distancing extends control to $R_0 < 7.82$ (95% CI 7.70-7.93), encompassing 86.67% of pathogens. These results, supported by empirical data, highlight the efficacy of rapid tracing and targeted social distancing as alternatives to mass PCR testing. Our framework offers actionable insights for optimizing NPI strategies, though challenges in scaling to regions with higher tracing miss rates or weaker infrastructure underscore the need for adaptive, data-driven policies.