Evidence map›Paper›PMID 35885762›Full record

ArticleHealthcare (Basel, Switzerland)2022

Cluster Analysis of US COVID-19 Infected States for Vaccine Distribution.

Dong-Her Shih, Pai-Ling Shih, Ting-Wei Wu, Cheng-Jung Li, Ming-Hung Shih

Abstract read
In one paragraph

Article in Healthcare (Basel, Switzerland), 2022. 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
–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

0 citing papers in PubMed.

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

5 authors.

Dong-Her ShihDepartment of Information Management, National Yunlin University of Science and Technology, Douliu 64002, Taiwan.ORCID 0000-0003-1605-8488
Pai-Ling ShihDepartment of Information Management, National Chung Cheng University, Chiayi 621301, Taiwan.
Ting-Wei WuDepartment of Information Management, National Yunlin University of Science and Technology, Douliu 64002, Taiwan.
Cheng-Jung LiDepartment of Information Management, National Yunlin University of Science and Technology, Douliu 64002, Taiwan.
Ming-Hung ShihDepartment of Electrical and Computer Engineering, Iowa State University, 2520 Osborn Drive, Ames, IA 50011, USA.

Funding

Taiwan Ministry of Science and Technology (grants MOST 110-2410-H-224-010).
6 · The paper itself

Abstract

Since December 2019, COVID-19 has been raging worldwide. To prevent the spread of COVID-19 infection, many countries have proposed epidemic prevention policies and quickly administered vaccines, However, under facing a shortage of vaccines, the United States did not put forward effective epidemic prevention policies in time to prevent the infection from expanding, resulting in the epidemic in the United States becoming more and more serious. Through "The COVID Tracking Project", this study collects medical indicators for each state in the United States from 2020 to 2021, and through feature selection, each state is clustered according to the epidemic's severity. Furthermore, through the confusion matrix of the classifier to verify the accuracy of the cluster analysis, the study results show that the Cascade K-means cluster analysis has the highest accuracy. This study also labeled the three clusters of the cluster analysis results as high, medium, and low infection levels. Policymakers could more objectively decide which states should prioritize vaccine allocation in a vaccine shortage to prevent the epidemic from continuing to expand. It is hoped that if there is a similar epidemic in the future, relevant policymakers can use the analysis procedure of this study to determine the allocation of relevant medical resources for epidemic prevention according to the severity of infection in each state to prevent the spread of infection.

Indexed as

classification validationclustering analysisCOVID-19machine learningvaccine distribution

Identifiers

PMID35885762
PMCPMC9323689

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.