COVID-19 Epidemic Models: A Study from Georgia State in the USA |Biomedgrid

The purpose of this study was to identify gender-specific differences and best-fit coronavirus (covid-19) model for the infected people of Georgia. Statistical methods chi-squared, ANOVA, logistic regression, Poisson and negative binomial regression models were utilized to analyze Covid-19 data, which were obtained from the Georgia Department of Public Health. The difference among the mean ages of deaths for overall underlying conditions (P = 0.0248) and with ‘no' and ‘unknown' medical conditions (P = 0.0196) were found to be significant. The covariates regions, minimum age, maximum age, and average age were found to have a significant effect (P < 0.0001). The negative binomial regression model exhibited a best-fit model in building a death curve compared to Poisson regression model obtained by the GLM method. The findings will help to determine genderspecific future virus models for effective interventions, and they can be generalized to the population with geographic and racial/ethnic similarities.