Evidence map›Paper›PMID 35035282›Full record

ArticleStochastic environmental research and risk assessment : research journal2022

Factors affecting the COVID-19 risk in the US counties: an innovative approach by combining unsupervised and supervised learning.

Samira Ziyadidegan, Moein Razavi, Homa Pesarakli, Amir Hossein Javid, Madhav Erraguntla

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Stochastic environmental research and risk assessment : research journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. An analytical approach to evaluate the impact of age demographics in a pandemic.Stochastic environmental research and risk assessment : research journal · 2023
    Article
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.

Samira Ziyadidegan *Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX 77843 USA.
Moein Razavi *Department of Computer Science and Engineering, Texas A&M University, College Station, TX 77843 USA.ORCID 0000-0002-8036-1353
Homa Pesarakli *Department of Architecture, Texas A&M University, College Station, TX 77843 USA.
Amir Hossein JavidDepartment of Statistics, Oklahoma State University, Stillwater, OK 74074 USA.
Madhav ErraguntlaDepartment of Industrial and Systems Engineering, Texas A&M University, College Station, TX 77843 USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 disease spreads swiftly, and nearly three months after the first positive case was confirmed in China, Coronavirus started to spread all over the United States. Some states and counties reported high number of positive cases and deaths, while some reported lower COVID-19 related cases and death. In this paper, the factors that could affect the risk of COVID-19 infection and death were analyzed in county level. An innovative method by using K-means clustering and several classification models is utilized to determine the most critical factors. Results showed that

Indexed as

COVID-19K-means clusteringMeteorological variablesMultinomial logistic regressionSARS-CoV-2

Identifiers

PMID35035282
PMCPMC8747889

What OpenQuestion holds

Textmetadata
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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.