Spatial Cross-Recurrence Quantification Analysis for Multi-Platform Contact Tracing and Epidemiology Research

TL;DR

This paper introduces SpaRQ, a novel method for integrating diverse contact tracing data sources to assess infection risk and pathogen characteristics without compromising user privacy.

Contribution

SpaRQ provides a unified analysis framework for contact tracing data from multiple sources, enhancing epidemiological insights while preserving privacy.

Findings

Effective integration of contact tracing data sources.

Ability to analyze pathogen viability and transmission risk.

Enhanced privacy preservation in contact tracing analysis.

Abstract

Contact tracing is an essential tool in slowing and containing outbreaks of contagious diseases. Current contact tracing methods range from interviews with public health personnel to Bluetooth pings from smartphones. While all methods offer various benefits, it is difficult for different methods to integrate with one another. Additionally, for contact tracing mobile applications, data privacy is a concern to many as GPS data from users is saved to either a central server or the user's device. The current paper describes a method called spatial cross-recurrence quantification analysis (SpaRQ) that can combine and analyze contact tracing data, regardless of how it has been obtained, and generate a risk profile for the user without storing GPS data. Furthermore, the plots from SpaRQ can be used to investigate the nature of the infectious agent, such as how long it can remain viable in air or on surfaces after an infected person has passed, the chance of infection based on exposure time, and what type of exposure is maximally infective.