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Factor analysis approach to classify COVID-19 datasets in several regions.
Mohammad Reza Mahmoudi1, Dumitru Baleanu2,3, Shahab S Band4
1Department of Statistics, Faculty of Science, Fasa University, Fasa, Fars, Iran.
Results in Physics
|March 29, 2021
Summary
This study examined the link between COVID-19 cases and deaths in seven nations. Factor analysis grouped countries by their correlation patterns, revealing distinct pandemic impact relationships.
Area of Science:
- Epidemiology
- Biostatistics
Background:
- The COVID-19 pandemic caused widespread mortality and morbidity globally.
- Understanding the relationship between infection rates and fatality is crucial for public health.
- Seven severely impacted nations were selected for this correlational study.
Purpose of the Study:
- To investigate the correlation between COVID-19 case counts and death counts in seven severely affected countries.
- To categorize these countries based on the identified relationships between cases and deaths.
Main Methods:
- Pearson's correlation coefficient was employed to quantify the linear relationship between COVID-19 cases and deaths.
- Factor analysis was utilized to group countries based on the strength and direction of these correlations.
Main Results:
- The study identified varying degrees of correlation between case numbers and mortality across the selected nations.
- Factor analysis revealed distinct clusters of countries, indicating different patterns in the case-fatality relationship.
Conclusions:
- The relationship between COVID-19 cases and deaths is not uniform across all severely affected countries.
- Country-specific factors likely influence the observed correlations, necessitating tailored public health strategies.
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