Infection Risk Score: Identifying the risk of infection propagation based on human contact

TL;DR

This paper introduces an infection risk score derived from human contact data to estimate infection propagation risk, aiding early intervention and safer social resumption during pandemics.

Contribution

It presents a novel infection risk score based on contact data, with implementation strategies for smartphones and practical use cases for pandemic management.

Findings

The risk score accurately estimates infection risk in real-world data.

Implementation on smartphones is feasible and practical.

Use cases demonstrate potential to reduce infection spread.

Abstract

A wide range of approaches have been applied to manage the spread of global pandemic events such as COVID-19, which have met with varying degrees of success. Given the large-scale social and economic impact coupled with the increasing time span of the pandemic, it is important to not only manage the spread of the disease but also put extra efforts on measures that expedite resumption of social and economic life. It is therefore important to identify situations that carry high risk, and act early whenever such situations are identified. While a large number of mobile applications have been developed, they are aimed at obtaining information that can be used for contact tracing, but not at estimating the risk of social situations. In this paper, we introduce an infection risk score that provides an estimate of the infection risk arising from human contacts. Using a real-world human contact dataset, we show that the proposed risk score can provide a realistic estimate of the level of risk in the population. We also describe how the proposed infection risk score can be implemented on smartphones. Finally, we identify representative use cases that can leverage the risk score to minimize infection propagation.