Published Paper Details:

Objective: Number of infection per unit population of country and related deaths due to COVID-19 is being reported different in all countries, seems not related with population density of country or hygiene level of country as anticipated. Factors contributing for different infection rates is not completely understood. This study is aimed at analyzing the correlation of COVID-19 infection by statistical method using open source data available with demographic and environmental parameters including EMF radiation of mobile and broadband with wi-fi router. Statistical analysis design: Open source data on target parameter COVID-19 infections per unit population and related mortality were analysed for correlation with open source data on 14 various predictor parameters like Population Density, Net Migrants, Median Age, Vegetarian diet (%), % Urban Population, % Mobile Penetration in country, Mobile Connection Speed, Number of Fixed Broad Band Subscriptions in the country with wi-fi router, Fixed Broad Band Speed, Average February � March Temperature, % BCG Immunization Coverage, % Total & Urban Population using at least basic sanitation services and Per Capita CO2 Emissions. For statistical analysis, both actual data and average of data within sets of ranging infections per unit population have been considered. Also a mathematical model is generated for prediction of infections, using regression technique on best correlated parameter and also using combination of highly correlated parameters. This regression model could be used to calculate expected infections in the country under influence of set of parameters. Additionally, difference between actual infection and predicted infection calculated by this model, could be useful for finding out effectiveness of implementing other measures by the country to control infection below expected value and vice versa. Results: with respect to target parameter total infection cases per unit population, top 7 predictor parameters in descending order of correlation coefficient values (?) are: Net Migrant / 1M Population (0.784), Fixed Broad Band Speed (0.749), Number of Fixed Broad Band with wi-fi router Subscriptions per unit population (0.694), Per Capita CO2 Emission (0.689), Median Age (0.637), % Urban Population (0.598) and % Total Population using at least basic sanitation services (0.597). Also with respect to another target parameter i.e. death per unit cases in the country, relatively lower ? values are obtained. Top 5 parameters in descending order of correlation coefficient values (?) are: Number of Fixed Broad Band with wi-fi router Subscriptions per unit population (0.678), Tot cases/ 1M population (0.623), % BCG Immunization Coverage (-0.576; negative directional), Median Age (0.552) and Fixed Broad Band Speed (0.503). Additionally regression analysis helped in generating mathematical model of finding a combined parameters with higher correlation ? value 0.846. The regression equation helped to calculate expected infection numbers and difference with actual could be utilized to access effectiveness of country in controlling infection by other means. Conclusion: Statistical analysis showed highest correlation on COVID-19 infection and related deaths with use of high speed broadband services with wi-fi router, may be due to ill effects on health and immune power of electromagnetic radiation. There is need for further research on mechanism by which electromagnetic radiation influences COVID-19 infection. Also need to find out whether the effect of EMF radiation is temporary or permanent i.e. if radiation is reduced; infections and deaths are reduces or not.