Evidence map›Paper›PMID 32837240›Full record

ArticleTransactions in GIS : TG2020

Spatially explicit models for exploring COVID-19 lockdown strategies.

David O'Sullivan, Mark Gahegan, Daniel J Exeter, Benjamin Adams

Open access · hybridAbstract read
In one paragraph

Article in Transactions in GIS : TG, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
1.9field-weighted citation impact, top 10% of its field
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

20 citing papers in PubMed, 58 citations in OpenAlex.

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  19. Disease control across urban-rural gradients.Journal of the Royal Society, Interface · 2020
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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

4 authors at 3 institutions in 1 country.

David O'SullivanDepartment of Geography, Environment and Earth Science Victoria University of Wellington Wellington New Zealand.
Mark GaheganDepartment of Computer Science Centre for eResearch University of Auckland - City Campus Auckland New Zealand.ORCID https://orcid.org/0000-0001-7209-8156
Daniel J ExeterSchool of Population Health University of Auckland Auckland New Zealand.
Benjamin AdamsDepartment of Computer Science and Software Engineering University of Canterbury Christchurch New Zealand.ORCID https://orcid.org/0000-0002-1657-9809
University of Auckland · NZUniversity of Canterbury · NZVictoria University of Wellington · NZ

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article describes two spatially explicit models created to allow experimentation with different societal responses to the COVID-19 pandemic. We outline the work to date on modeling spatially explicit infective diseases and show that there are gaps that remain important to fill. We demonstrate how geographical regions, rather than a single, national approach, are likely to lead to better outcomes for the population. We provide a full account of how our models function, and how they can be used to explore many different aspects of contagion, including: experimenting with different lockdown measures, with connectivity between places, with the tracing of disease clusters, and the use of improved contact tracing and isolation. We provide comprehensive results showing the use of these models in given scenarios, and conclude that explicitly regionalized models for mitigation provide significant advantages over a "one-size-fits-all" approach. We have made our models, and their data, publicly available for others to use in their own locales, with the hope of providing the tools needed for geographers to have a voice during this difficult time.

Identifiers

PMID32837240
PMCPMC7283721
OpenAlexW3032167546

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.