Facebook data could map COVID transmission risk in Aussie outbreaks

Embargoed until: Publicly released:
Peer-reviewed: This work was reviewed and scrutinised by relevant independent experts.

Simulation/modelling: This type of study uses a computer simulation or mathematical model to predict an outcome. The original values put into the model may have come from real-world measurements (eg: past spread of a disease used to model its future spread).

Facebook's mobility data could be used to estimate the spread of COVID-19 transmission risk from locations where
active cases have been identified, say Australian researchers. The team used the data, which tells them the number of people moving between locations occupied in subsequent 8-hour intervals, to look back at three recent outbreaks: Cedar Meats in Brimbank, Victoria; The crossroads hotel in Western Sydney; and the Victorian second wave. Their results indicate that aggregate mobility data can be a useful tool in estimating COVID-19 transmission risk.

Journal/conference: Journal of the Royal Society Interface

Link to research (DOI): 10.1098/rsif.2020.0657

Organisation/s: The University of Melbourne, The University of Adelaide, Victorian Government, Monash University, The University of New South Wales

Funder: This work was supported in part by and NHMRC project grant no. (APP1165876), an NHMRC Centre of Research Excellence (APP1170960), and the Victoria State Government Department of Health and Human Services

Media release

From: The Royal Society

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

Journal of the Royal Society Interface

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 present and evaluate 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 outbreaks in Australia.

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