## A Novel Deep Learning Algorithm to Calculate and Model the Age-Standardized COVID-19 Mortality Rate of a Subpopulation When Compared to a Standard Population by Mayur T. Talele

March 16, 2022
Mayur T. Talele, Herricks High School

Abstract:

Coronavirus disease -19 (COVID-19) has gained widespread interest in the field of mathematical epidemiology in order to inform the public on basic statistics surrounding COVID-19. However, the age-standardized mortality rates (ASMRs), which adjust age and population discrepancies between different regions by comparing a subpopulation to a standard population, have not been shown publicly. Usually, COVID-19 ASMRs have not been calculated due to the lengthy process required to calculate them; however, ASMRs for COVID-19 have occasionally been calculated, but their effectiveness have been hindered due to the use of a hand-written formula and graphical manual methods. My study involved the development of a deep learning algorithm to calculate ASMR and to instantly graph the ASMR of a subpopulation versus the crude mortality rate of the standard population. This algorithm was used to compare the ASMRs for COVID-19 in American states to the crude mortality rate of the standard population, America. In this study, the algorithm shows efficiency with a consistent runtime of time≤5seconds, within 95% confidence interval error bars among trials. ASMRs show statistically significant differences in expected COVID-19 deaths among most populations. There is at least 95% confidence (p≤0.05) that differences in ASMR are independent of age and population distributions. These findings suggest that there are more factors than just age discrepancy that affect COVID-19 mortality rates.

Keywords: COVID-19, Age-Standardization, Mortality Rate, Algorithm, Deep Learning

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