Evidence map›Paper›PMID 35037595›Full record

ArticlePathogens and global health2022

The mathematical relationship between COVID-19 cases and socio-economic indicators of OECD countries.

Mehmet Cem Catalbas, Serkan Burken

Abstract read
In one paragraph

Article in Pathogens and global health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

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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

2 authors.

Mehmet Cem Catalbas1st Organized Industrial Zone Vocational School, Department of Electronics and Automation, Ankara University, Ankara, Turkey.
Serkan BurkenFaculty of Economics and Administrative Sciences, Economics, Ostim Technical University, Ankara, Turkey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In most aspects, the COVID-19 pandemic has been the most impactful disease in the last few decades. Although two years have passed since the start of the COVID-19 pandemic, it is clear that the pandemic is far from being brought under full control. This pandemic, which was kept under control in part by the discovery of vaccines, has had a negative impact on people's daily lives as well as global economies. The transmission and death rate of this disease, which was detected in Wuhan, China in December 2019, has differed between countries. The reason for these different rates according to countries has been investigated while developing a mathematical model in this comprehensive study. The effect of 16 different socioeconomic indicators of countries on the parameters of the COVID-19 pandemic was analyzed in this study, which was performed on 35 countries, the majority of which are OECD countries. The main objective of this research is to minimize potential negative effects by improving or taking these indicators into account in future similar outbreaks. In this study, canonical correlation analysis was used to obtain the appropriate mathematical model and the performance of the obtained model has been tested statistically. According to the mathematical model obtained, the age factor comes to the fore as the most important indicator in the COVID-19 pandemic and the next important indicator is the health expenditures of the countries. The influence of 16 different socioeconomic indicators on the COVID-19 pandemic parameters are presented as a percentage with details.

Indexed as

COVID-19HumansOrganisation for Economic Co-Operation and DevelopmentPandemicsSARS-CoV-2Socioeconomic Factorscanonical correlation analysisCOVID-19mathematical modelOECD countriesSARS-CoV-2socioeconomic indicators

Identifiers

PMID35037595
PMCPMC9387341

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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.