Evidence map›Paper›PMID 33404371›Full record

ArticleJournal of the Royal Society, Interface2021

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

Cameron Zachreson, Lewis Mitchell, Michael J Lydeamore, Nicolas Rebuli, Martin Tomko, Nicholas Geard

Abstract read
In one paragraph

Article in Journal of the Royal Society, Interface, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

29 citing papers in PubMed.

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  12. Social media mining under the COVID-19 context: Progress, challenges, and opportunities.International journal of applied earth observation and geoinformation : ITC journal · 2022
    Article
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  16. Is the SARS CoV-2 Omicron Variant Deadlier and More Transmissible Than Delta Variant?International journal of environmental research and public health · 2022
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Cameron ZachresonSchool of Computing and Information Systems, The University of Melbourne, Melbourne, Australia.
Lewis MitchellSchool of Mathematical Sciences, The University of Adelaide, Adelaide, Australia.
Michael J LydeamoreVictorian Department of Health and Human Services, Government of Victoria, Melbourne, Australia.
Nicolas RebuliSchool of Public Health and Community Medicine, University of New South Wales, Sydney, Australia.
Martin TomkoMelbourne School of Engineering, The University of Melbourne, Melbourne, Australia.
Nicholas GeardThe Peter Doherty Institute for Infection and Immunity, The Royal Melbourne Hospital and The University of Melbourne, Melbourne, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 geographical 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 outbreaks 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 geographical patterns of exposure risk from transmission centres, particularly in outbreaks involving workplaces or other environments associated with habitual travel patterns. For community transmission scenarios, our results demonstrate that mobility data add 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.

Indexed as

Disease OutbreaksPublic HealthSARS-CoV-2TravelAustraliaContact TracingCOVID-19DemographyHumansModels, BiologicalRisk AssessmentCOVID-19infectious diseasesmobilitytransmission risk

Identifiers

PMID33404371
PMCPMC7879754

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.