Risk mapping for COVID-19 outbreaks in Australia using mobility data

TL;DR

This study demonstrates that aggregate mobility data can effectively predict geographic COVID-19 transmission risks in Australia, aiding targeted responses especially in early or clustered community transmission scenarios.

Contribution

The paper introduces a simple, real-time method for estimating spatial transmission risk using mobility data validated against multiple outbreak scenarios in Australia.

Findings

Mobility data predicts exposure risk in workplaces and habitual travel environments.

Risk predictions are most accurate in early or clustered community transmission cases.

Method can inform targeted testing and movement restrictions.

Abstract

COVID-19 is highly transmissible and containing outbreaks requires a rapid and effective response. Because infection may be spread by people who are pre-symptomatic or asymptomatic, substantial undetected transmission is likely to occur before clinical cases are diagnosed. Thus, when outbreaks occur there is a need to anticipate which populations and locations are at heightened risk of exposure. In this work, we evaluate the utility of aggregate human mobility data for estimating the geographic distribution of transmission risk. We present a simple procedure for producing spatial transmission risk assessments from near-real-time population mobility data. We validate our estimates against three well-documented COVID-19 outbreak scenarios in Australia. Two of these were well-defined transmission clusters and one was a community transmission scenario. Our results indicate that mobility data can be a good predictor of geographic patterns of exposure risk from transmission centres, particularly in scenarios involving workplaces or other environments associated with habitual travel patterns. For community transmission scenarios, our results demonstrate that mobility data adds the most value to risk predictions when case counts are low and spatially clustered. Our method could assist health systems in the allocation of testing resources, and potentially guide the implementation of geographically-targeted restrictions on movement and social interaction.