Evidence map›Paper›PMID 38071385›Full record

ArticleScientific reports2023

Beyond scale-free networks: integrating multilayer social networks with molecular clusters in the local spread of COVID-19.

Kayo Fujimoto, Jacky Kuo, Guppy Stott, Ryan Lewis, Hei Kit Chan, Leke Lyu, Gabriella Veytsel, Michelle Carr, Tristan Broussard, Kirstin Short and 4 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.9field-weighted citation impact, top 28% 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

0 citing papers in PubMed, 4 citations in OpenAlex.

No citing paper in PubMed yet.

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

14 authors at 4 institutions in 1 country.

Kayo FujimotoSchool of Public Health, University of Texas Health Science Center at Houston, 7000 Fannin Street, UCT 2514, Houston, TX, 77030, USA. Kayo.Fujimoto@uth.tmc.edu.
Jacky KuoSchool of Public Health, University of Texas Health Science Center at Houston, 7000 Fannin Street, UCT 2514, Houston, TX, 77030, USA.
Guppy StottInstitute of Bioinformatics, University of Georgia, 501 D.W. Brooks Drive, Athens, GA, 30602, USA.
Ryan LewisSchool of Public Health, University of Texas Health Science Center at Houston, 7000 Fannin Street, UCT 2514, Houston, TX, 77030, USA.
Hei Kit ChanSchool of Public Health, University of Texas Health Science Center at Houston, 7000 Fannin Street, UCT 2514, Houston, TX, 77030, USA.
Leke LyuInstitute of Bioinformatics, University of Georgia, 501 D.W. Brooks Drive, Athens, GA, 30602, USA.
Gabriella VeytselInstitute of Bioinformatics, University of Georgia, 501 D.W. Brooks Drive, Athens, GA, 30602, USA.
Michelle CarrCity of Houston Health Department, Houston, TX, USA.
Tristan BroussardCity of Houston Health Department, Houston, TX, USA.
Kirstin ShortCity of Houston Health Department, Houston, TX, USA.
Pamela BrownCity of Houston Health Department, Houston, TX, USA.
Roger SealyCity of Houston Health Department, Houston, TX, USA.
Armand BrownCity of Houston Health Department, Houston, TX, USA.
Justin BahlInstitute of Bioinformatics, University of Georgia, 501 D.W. Brooks Drive, Athens, GA, 30602, USA. Justin.Bahl@uga.edu.
Houston Health and Human Services Department · USUniversity of Georgia · USThe University of Texas Health Science Center at Houston · USThe University of Texas Health Science Center · US

Funding

Centers for Disease Control and Prevention Foundation 75D30121C10133
6 · The paper itself

Abstract

This study evaluates the scale-free network assumption commonly used in COVID-19 epidemiology, using empirical social network data from SARS-CoV-2 Delta variant molecular local clusters in Houston, Texas. We constructed genome-informed social networks from contact and co-residence data, tested them for scale-free power-law distributions that imply highly connected hubs, and compared them to alternative models (exponential, log-normal, power-law with exponential cutoff, and Weibull) that suggest more evenly distributed network connections. Although the power-law model failed the goodness of fit test, after incorporating social network ties, the power-law model was at least as good as, if not better than, the alternatives, implying the presence of both hub and non-hub mechanisms in local SARS-CoV-2 transmission. These findings enhance our understanding of the complex social interactions that drive SARS-CoV-2 transmission, thereby informing more effective public health interventions.

Indexed as

COVID-19HumansSARS-CoV-2Social NetworkingTexas

Identifiers

PMID38071385
PMCPMC10710469
OpenAlexW4389503834

What OpenQuestion holds

Textmetadata
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.