ISSN :2582-9793

Association of social, demographic, health, nutritional and environmental factors with the incidence and death rates of COVID-19; A global cross-sectional analytical study

Original Research (Published On: 19-May-2022 )
Association of social, demographic, health, nutritional and environmental factors with the incidence and death rates of COVID-19; A global cross-sectional analytical study
DOI : 10.54364/AAIML.2022.1123

supun Sudaraka manathunga, Ishanya Ayeshini Abeyagunawardena, Raahya Sudaraka Lafir and Samath Sudaraka Dharmaratne

Adv. Artif. Intell. Mach. Learn., 2 (2):347-365

supun Sudaraka manathunga : National Hospital of Sri Lanka

Ishanya Ayeshini Abeyagunawardena : National Hospital of Sri Lanka

Raahya Sudaraka Lafir : National Hospital of Sri Lanka

Samath Sudaraka Dharmaratne : Institute for Health Metrics and Evaluation, Department of Health Metrics Sciences, School of Medicine, University of Washington, United States of America

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DOI: 10.54364/AAIML.2022.1123

Article History: Received on: 06-Apr-22, Accepted on: 30-Apr-22, Published on: 19-May-22

Corresponding Author: supun Sudaraka manathunga

Email: ssm123ssm@gmail.com

Citation: Supun Sudaraka manathunga, Ishanya I. Abeyagunawardena, Raahya Lafir and Samath Dharmaratne (2022). Association of social, demographic, health, nutritional and environmental factors with the incidence and death rates of COVID-19; A global cross-sectional analytical study. Adv. Artif. Intell. Mach. Learn., 2 (2 ):347-365

          

Abstract

    

Data regarding 88 variables for 195 countries over three years were extracted from The Health Nutrition and Population Statistics database and aggregated into a consolidated median. Outliers were eliminated and variables having completeness of more than 70% were selected. The analysis was done separately for the incidence and mortality of COVID-19. Principal component analysis (PCA), Elastic net regression, Random Forest and XGBoost models were used to identify the most important single variables. Subsequently, variables with the highest importance (using normalized ranked regression coefficients) in the Elastic Net model were selected and the intersecting sets of variables common to multiple models were considered as predictors affecting the incidence and mortality of COVID-19.

Random Forest and XGBoost algorithms performed well in predicting the incidence and mortality of COVID-19.

The study revealed communities with a high prevalence of anaemia have a negative correlation with COVID-19 incidence which was furthermore, interestingly seen in multiple age groups. Diphtheria, Tetanus and Pertussis (DTP) Immunisation in children was also found to have a negative linear correlation.



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