Evidence map›Paper›PMID 37039387›Full record

ArticleInquiry : a journal of medical care organization, provision and financing

Analysis of COVID-19 Vaccinations and Symptom Mapping Diagnostic Technique for Viral Diseases: Using Data Analytics, Machine Learning, and Artificial Intelligence.

Chikezie Kennedy Kalu

Abstract read
In one paragraph

Article in Inquiry : a journal of medical care organization, provision and financing. 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

1 author.

Chikezie Kennedy KaluDepartment of Management Science and Engineering, School of Management, Jiangsu University, Zhenjiang, Jiangsu, China.ORCID 0000-0002-1179-5449

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To analyze, understand, and measure the COVID-19 vaccination outlook in a developing country as Nigeria; and the non-clinical analysis, diagnosis, treatment and management of COVID-19, and other Viral Diseases, using Data/Machine Learning (ML)/Artificial Intelligence (AI), Analytical Tools, and Methodologies. Using current and historical data from validated open source data stores, analysis was carried out on COVID-19 vaccination and related economic, demographic, and geo-climatic data for a developing country, Nigeria and selected countries from all continents of the world. The methodical and data-driven analyses were carried out using the following Data/Artificial Intelligence (AI) methodologies and algorithms: Excel Data Analytics, Multivariate Linear Regression Analysis method in Machine Learning (ML) Engineering, Symptom Mapping Analysis, Gray System Analysis. The COVID-19 vaccinations expectedly does reduce the number of active COVID cases and the amount or number of vaccinations for a developing country as Nigeria is affected by a good number of economic, demographic, and geo-climatic factors; and so COVID-19 vaccinations strategies must be unique to a country and categories of countries and take into account influencing factors not only limited to number of active COVID cases. The strategies (including vaccinations roll-out) to eliminate COVID-19 can be better understood and managed for increased productivity and faster success rate in the fight against COVID-19. Medical practitioners can provide even more efficient diagnosis and treatment of viral diseases; and also patients can carry out personalized cost effective diagnosis and treatment/management of viral diseases, with also the advises of medical practitioners.

Indexed as

Artificial IntelligenceCOVID-19COVID-19 TestingCOVID-19 VaccinesData ScienceHumansMachine LearningCOVID-19 Vaccinesartificial intelligenceCOVID-19 vaccinationdata analysismachine learningsymptom mappingviral diseases

Identifiers

PMID37039387
PMCPMC10090961

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.