Abstract

Background: The magnitude of the impact of COVID-19 is dependent on social, demographic, health, nutrition and even environmental factors. These factors act individually and synergistically to impact the incidence, mortality and morbidity of COVID-19. We aimed to evaluate the variables contributing individually to COVID-19 incidence and mortality utilizing techniques to minimize the effects of interaction between these factors. Method: 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 a completeness of more than 70% were selected. The analysis was done separately for the incidence and mortality of COVID19. Principal component Analysis (PCA) and Elastic net regression were used to identify the most important single variables. The significant variables of the PCA which explained the most variance were identified. Subsequently, variables with the highest importance (using normalized ranked regression coefficients) in the Elastic Net model were selected and the intersecting set of variables common to both models was considered as predictors affecting incidence and mortality of COVID-19. Result: The study revealed communities with a high prevalence of anaemia has a negative correlation with COVID-19 incidence which was furthermore, interestingly seen in multiple age groups. Diphtheria, Tetanus and Pertussis (DTP) Immunization in children was also found to have a negative linear correlation. Conclusion: A negative individual association was seen between anaemia (in multiple age groups) and DTP immunization in children with the incidence and mortality of COVID 19.

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