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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
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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

References

  1. Wang, D., Li, Z., & Liu, Y. (2020). An overview of the safety, clinical application and antiviral research of the COVID-19 therapeutics. Journal of Infection and Public Health. doi:10.1016/j.jiph.2020.07.004
  2. Brown, S. M., Doom, J. R., Lechuga-Peña, S., Watamura, S. E., & Koppels, T. (2020). Stress and parenting during the global COVID-19 pandemic. Child Abuse & Neglect. doi:10.1016/j.chiabu.2020.104699
  3. Overton, C. E., Stage, H. B., Ahmad, S., Curran-Sebastian, J., Dark, P., Das, R., . . . Webb, L. (2020). Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example. Infectious Disease Modelling, 5, 409-441. doi:10.1016/j.idm.2020.06.008
  4. Tiirinki, H., Tynkkynen, L., Sovala, M., Atkins, S., Koivusalo, M., Rautiainen, P., . . . Keskimäki, I. (2020). COVID-19 pandemic in Finland – preliminary analysis on health system response and economic consequences. Health Policy and Technology. doi:10.1016/j.hlpt.2020.08.005
  5. Russell, T. W., Hellewell, J., Jarvis, C. I., Zandvoort, K. V., Abbott, S., Ratnayake, R., . . . Kucharski, A. J. (2020). Estimating the infection and case fatality ratio for coronavirus disease (COVID-19) using age-adjusted data from the outbreak on the Diamond Princess cruise ship, February 2020. Eurosurveillance, 25(12). doi:10.2807/1560-7917.es.2020.25.12.2000256
  6. Bernardino, G., Benkarim, O., Garza, M. S., Prat-Gonzàlez, S., Sepulveda-Martinez, A., Crispi, F., . . . Ballester, M. A. (2020). Handling confounding variables in statistical shape analysis - application to cardiac remodelling. Medical Image Analysis, 65. doi:10.1016/j.media.2020.101792
  7. Xu, L., Polya, D. A., Li, Q., & Mondal, D. (2020). Association of low-level inorganic arsenic exposure from rice with age-standardized mortality risk of cardiovascular disease (CVD) in England and Wales. Science of The Total Environment, 743. doi:10.1016/j.scitotenv.2020.140534
  8. Shende, R., Gupta, G., & Macherla, S. (2019). Determination of an inflection point for a dosimetric analysis of unflattened beam using the first principle of derivatives by python code programming. Reports of Practical Oncology & Radiotherapy, 24(5), 432-442. doi:10.1016/j.rpor.2019.07.009
  9. Mohamed, M. O., Gale, C. P., Kontopantelis, E., Doran, T., Belder, M. D., Asaria, M., . . . Mamas, M. A. (2020). Sex-differences in mortality rates and underlying conditions for COVID-19 deaths in England and Wales. Mayo Clinic Proceedings. doi:10.1016/j.mayocp.2020.07.009
  10. Kavadi, D. P., Patan, R., Ramachandran, M., & Gandomi, A. H. (2020). Partial derivative Nonlinear Global Pandemic Machine Learning prediction of COVID 19. Chaos, Solitons & Fractals, 139. doi:10.1016/j.chaos.2020.110056
  11. Minicozzi, P., Cassetti, T., Vener, C., & Sant, M. (2018). Analysis of incidence, mortality and survival for pancreatic and biliary tract cancers across Europe, with assessment of influence of revised European age standardisation on estimates. Cancer Epidemiology, 55, 52-60. doi:10.1016/j.canep.2018.04.011
  12. Bosch, Jaume, et al. “Asynchronous Runtime with Distributed Manager for Task-Based Programming Models.” Parallel Computing, vol. 97, 2020, p. 102664., doi:10.1016/j.parco.2020.102664.
  13. Rodriguez-Diaz, Carlos E., et al. “Risk for COVID-19 Infection and Death among Latinos in the United States: Examining Heterogeneity in Transmission Dynamics.” Annals of Epidemiology, 23 July 2020, doi:10.1016/j.annepidem.2020.07.007.
  14. Wiemers, Emily, et al. “Disparities in Vulnerability to Severe Complications from COVID-19 in the United States.” Research in Social Stratification and Mobility, vol. 69, 2020, doi:10.3386/w27294.
  15. Etkin, Yana, et al. “Acute Arterial Thromboembolism in Patients with COVID-19 in the New York City Area.” Annals of Vascular Surgery, 28 Aug. 2020, doi:10.1016/j.avsg.2020.08.085.
  16. Centers for Disease Control and Prevention. www.cdc.gov/.
  17. 2020 World Population by Country, worldpopulationreview.com/