Coronavirus (COVID-19) Spike in Georgia: An Epidemiologic Study of Data, Modelling, and Policy Implications to Understand the Gender-and Race-Specific Variations

Abstract

ObjectiveThe purpose of this study was to investigate the gender-and race-specific predictive variations in COVID-19 cases and deaths in Georgia, USA.MethodsThe data were extracted from the Georgia Department of Public Health (GDPH). Statistical methods, such as descriptive statistics, Artificial neural networks (ANN), and Bayesian approach, were utilized to analyze the data.ResultsMore Whites died from COVID-19 than African-Americans/Blacks in Cobb, Hall, Gwinnett, and non-Georgia residents; however, more Blacks died in Dekalb and Fulton counties. The highest posterior mean for female deaths was obtained in Gwinnett County (77.17; 95% CI, 74.23–80.07) and for male deaths in Fulton County (73.48; 95% CI, 72.18–74.49). For overall race/ethnicity, Whites had the highest posterior mean for deaths (183.18; 95% CI, 128.29–238.27) compared with Blacks (162.48; 95% CI, 127.15– 197.42). Assessing the classification of the chronic medical conditions using ANN, Cobb and Hall Counties showed the highest mean AUC-ROC of the models (78% and 79%, respectively).ConclusionsThe predictive models of COVID-19 transmission will help public health practitioners and researchers to better understand the course of the COVID-19 pandemic. The study findings are generalizable to populations with geographic and racial/ethnic similarities and may be used to determine gender/race-specific future virus models for effective interventions or policy modifications.Human SubjectsNo personal identifiable information was obtained.

The statistical analysis of daily data associated with different parameters of the New Coronavirus COVID-19 pandemic in Georgia and their monthly interval prediction from September 1, 2021 to December 31, 2021

“…In particular, in our works [6][7][8][9][10][11], it was noted that specialists in the field of physical and mathematical sciences make an important contribution to research on the spread of the new coronavirus COVID-19. Works on statistical analysis [2][3][4][5][12][13][14][15], forecasting [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32], forecasting systematization [33,34], spatial-temporary modeling of the spread of the new coronavirus [35][36][37][38] etc. was actively continuing in 2021.…”