Evidence map›Paper›PMID 33112922›Full record

ArticlePloS one2020

Sub-national longitudinal and geospatial analysis of COVID-19 tweets.

Raphael E Cuomo, Vidya Purushothaman, Jiawei Li, Mingxiang Cai, Timothy K Mackey

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.6field-weighted citation impact, top 9% 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

8 citing papers in PubMed, 19 citations in OpenAlex.

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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

5 authors at 1 institution in 1 country.

Raphael E CuomoDepartment of Anesthesiology, San Diego School of Medicine, University of California, San Diego, California, United States of America.ORCID 0000-0002-8179-0619
Vidya PurushothamanDepartment of Anesthesiology, San Diego School of Medicine, University of California, San Diego, California, United States of America.
Jiawei LiGlobal Health Policy Institute, San Diego, California, United States of America.
Mingxiang CaiDepartment of Healthcare Research and Policy, University of California, San Diego, California, United States of America.
Timothy K MackeyDepartment of Anesthesiology, San Diego School of Medicine, University of California, San Diego, California, United States of America.ORCID 0000-0002-2191-7833
University of California San Diego · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesAccording to current reporting, the number of active coronavirus disease 2019 (COVID-19) infections is not evenly distributed, both spatially and temporally. Reported COVID-19 infections may not have properly conveyed the full extent of attention to the pandemic. Furthermore, infection metrics are unlikely to illustrate the full scope of negative consequences of the pandemic and its associated risk to communities.

methodsIn an effort to better understand the impacts of COVID-19, we concurrently assessed the geospatial and longitudinal distributions of Twitter messages about COVID-19 which were posted between March 3rd and April 13th and compared these results with the number of confirmed cases reported for sub-national levels of the United States. Geospatial hot spot analysis was also conducted to detect geographic areas that might be at elevated risk of spread based on both volume of tweets and number of reported cases.

resultsStatistically significant aberrations of high numbers of tweets were detected in approximately one-third of US states, most of which had relatively high proportions of rural inhabitants. Geospatial trends toward becoming hotspots for tweets related to COVID-19 were observed for specific rural states in the United States. DISCUSSION: Population-adjusted results indicate that rural areas in the U.S. may not have engaged with the COVID-19 topic until later stages of an outbreak. Future studies should explore how this dynamic can inform future outbreak communication and health promotion.

Indexed as

BetacoronavirusCoronavirus InfectionsGeography, MedicalPandemicsPneumonia, ViralSocial MediaAttitude to HealthCommunity ParticipationCOVID-19HumansProspective StudiesPublic HealthRural PopulationSARS-CoV-2Time FactorsUnited States

Identifiers

PMID33112922
PMCPMC7592735
OpenAlexW3096948331

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.