Statistical Similarity of Mortality and Recovery Ratios for Covid-19 Patients based on Gender and Age

Abbas Mahmoudabadi


Background: Studying the behavior of patients infected with Covid-19 is an essential issue for health authorities during the global pandemic, so the aim of this study is to investigate the statistical similarity between the recovery and mortality ratios based on the patients’ age and gender. For this purpose, the well-known statistical testing method of Kolmogorov-Smirnov has been utilized to investigate the similarity of distribution functions for mortality and recovery rates for patients infected with Covid-19. Results: Data for 1015 patients resulting in death, recovery, and transfer has been collected and analyzed. The age is cross-classified by gender where the rates’ cumulative distribution functions are independently calculated and depicted for females and males. The results revealed that there is no significant difference between the distribution functions of mortality and recovery rates by gender, but there is by age. Conclusion: The research results would support the health authorities in managing the admission and discharge procedures of the Covid-19 patients where the hospitality services are traditionally provided differently by gender.


Doi: 10.28991/HIJ-2021-02-04-05

Full Text: PDF


Kolmogorov-Smirnov Test; Covid-19; Distribution Function; Statistical Similarity; Mortality and Recovery Rate.


Di Mascio, D., Khalil, A., Saccone, G., Rizzo, G., Buca, D., Liberati, M., Vecchiet, J., Nappi, L., Scambia, G., Berghella, V., &D’Antonio, F. (2020). Outcome of coronavirus spectrum infections (SARS, MERS, COVID-19) during pregnancy: a systematic review and meta-analysis. American Journal of Obstetrics and Gynecology MFM, 2(2). doi:10.1016/j.ajogmf.2020.100107.

Zhao, S., Lin, Q., Ran, J., Musa, S. S., Yang, G., Wang, W., … Wang, M. H. (2020). Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak. International Journal of Infectious Diseases, 92, 214–217. doi:10.1016/j.ijid.2020.01.050.

Law, S., Leung, A. W., & Xu, C. (2020). Severe acute respiratory syndrome (SARS) and coronavirus disease-2019 (COVID-19): From causes to preventions in Hong Kong. International Journal of Infectious Diseases, 94, 156–163. doi:10.1016/j.ijid.2020.03.059.

Roberts, G. V., & Gee, P. O. (2020). The early days: The postkidney transplant recipients’ covid-19 journey. Clinical Journal of the American Society of Nephrology 15(9), 1221–1223. doi:10.2215/CJN.08780620.

Sun, L., Depuy, G. W., & Evans, G. W. (2014). Multi-objective optimization models for patient allocation during a pandemic influenza outbreak. Computers and Operations Research, 51, 350–359. doi:10.1016/j.cor.2013.12.001.

Pastore, M., &Calcagnì, A. (2019). Measuring distribution similarities between samples: A distribution-free overlapping index. Frontiers in Psychology, 10, 1089. doi:10.3389/fpsyg.2019.01089.

Lee, L. (1999). Measures of distributional similarity. Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics, Maryland, United States. 25–32. doi:10.3115/1034678.1034693.

Sahinturk, L., &Özcan, B. (2017). The Comparison of Hypothesis Tests Determining Normality and Similarity of Samples. Journal of Naval Science and Engineering, 13(2), 21–36.

Vrbik, J. (2018). Small-Sample Corrections to Kolmogorov–Smirnov Test Statistic. Pioneer Journal of Theoretical and Applied Statistics, 15(1–2), 15–23.

Arnold, T. B., & Emerson, J. W. (2011). Nonparametric goodness-of-fit tests for discrete null distributions. R Journal, 3(2), 34-39.

Lopes, R. H., Reid, I. D., & Hobson, P. R. (2007). The two-dimensional Kolmogorov-Smirnov test. Proceedings of Science. XI International Workshop on Advanced Computing and Analysis Techniques in Physics Research, Amsterdam, Netherlands.

Simard, R., &L’Ecuyer, P. Computing the two-sided Kolmogorov-Smirnov distribution. Journal of Statistical Software, 39(11), 1–18.

Drezner, Z., Turel, O., &Zerom, D. (2010). A modified kolmogorov-smirnov test for normality. Communications in Statistics: Simulation and Computation, 39(4), 693–704. doi:10.1080/03610911003615816.

Mahmoudabadi, A., &Abdous, H. (2020). Do the Coaches’ Crashes and Their Usage Exposure Come from the Same Distributions? Society & Sustainability, 2(3), 10–19. doi:10.38157/society_sustainability.v2i3.165.

Stolwijk, C., van Onna, M., Boonen, A., & van Tubergen, A. (2016). Global Prevalence of Spondyloarthritis: A Systematic Review and Meta-Regression Analysis. Arthritis Care and Research, 68(9), 1320–1331. doi:10.1002/acr.22831.

Dash, S., Shakyawar, S. K., Sharma, M., &Kaushik, S. (2019). Big data in healthcare: management, analysis and future prospects. Journal of Big Data, 6(1), 54. doi:10.1186/s40537-019-0217-0.

Panay, B., Baloian, N., Pino, J., Peñafiel, S., Sanson, H., &Bersano, N. (2019). Predicting Health Care Costs Using Evidence Regression. In Proceedings (Vol. 31, Issue 1). Multidisciplinary Digital Publishing Institute Proceedings. doi:10.3390/proceedings2019031074.

Myers, J., Tan, S. Y., Abella, J., Aleti, V., &Froelicher, V. F. (2007). Comparison of the chronotropic response to exercise and heart rate recovery in predicting cardiovascular mortality. European Journal of Preventive Cardiology, 14(2), 215–221. doi:10.1097/HJR.0b013e328088cb92.

Hogan, H., Zipfel, R., Neuburger, J., Hutchings, A., Darzi, A., & Black, N. (2015). Avoidability of hospital deaths and association with hospital-wide mortality ratios: Retrospective case record review and regression analysis. BMJ (Online), 351, 3239. doi:10.1136/bmj.h3239.

Lenzi, J., Caporlingua, F., Caporlingua, A., Anichini, G., Nardone, A., Passacantilli, E., & Santoro, A. (2017). Relevancy of positive trends in mortality and functional recovery after surgical treatment of acute subdural hematomas. Our 10-year experience. British Journal of Neurosurgery, 31(1), 78–83. doi:10.1080/02688697.2016.1226253.

Polaraju, K., Durga Prasad, D., & Tech Scholar, M. (2017). Prediction of Heart Disease using Multiple Linear Regression Model. International Journal of Engineering Development and Research, 5(4), 2321–9939.

Pandey, G., Chaudhary, P., Gupta, R., & Pal, S. (2020). SEIR and Regression Model based COVID-19 outbreak predictions in India. doi:10.1101/2020.04.01.20049825.

Riascos, A., & Serna, N. (2017). Predicting Annual Length-Of-Stay and its Impact on Health (Vol. 69). Medical Informatics and Healthcare. Available online: (accessed on May 2021).

Ujah, I. A., Aisien, O. A., Mutihir, J. T., Vanderjagt, D. J., Glew, R. H., &Uguru, V. E. (2005). Factors contributing to maternal mortality in north-central Nigeria: a seventeen-year review. African Journal of Reproductive Health, 9(3), 27–40. doi:10.2307/3583409.

Kowalski, L. P., Sanabria, A., Ridge, J. A., Ng, W. T., de Bree, R., Rinaldo, A., Takes, R. P., Mäkitie, A. A., Carvalho, A. L., Bradford, C. R., Paleri, V., Hartl, D. M., Vander Poorten, V., Nixon, I. J., Piazza, C., Lacy, P. D., Rodrigo, J. P., Guntinas-Lichius, O., Mendenhall, W. M., … Ferlito, A. (2020). COVID-19 pandemic: Effects and evidence-based recommendations for otolaryngology and head and neck surgery practice. Head and Neck, 42(6), 1259–1267. doi:10.1002/hed.26164.

Tuite, A. R., Bogoch, I. I., Sherbo, R., Watts, A., Fisman, D., & Khan, K. (2020). Estimation of Coronavirus Disease 2019 (COVID-19) Burden and Potential for International Dissemination of Infection from Iran. Annals of Internal Medicine, 172(10), 699–701. doi:10.7326/m20-0696.

Lauer, S. A., Grantz, K. H., Bi, Q., Jones, F. K., Zheng, Q., Meredith, H. R., Azman, A. S., Reich, N. G., &Lessler, J. (2020). The incubation period of coronavirus disease 2019 (CoVID-19) from publicly reported confirmed cases: Estimation and application. Annals of Internal Medicine, 172(9), 577–582. doi:10.7326/M20-0504.

Nishiura, H., Kobayashi, T., Miyama, T., Suzuki, A., Jung, S. mok, Hayashi, K., Kinoshita, R., Yang, Y., Yuan, B., Akhmetzhanov, A. R., & Linton, N. M. (2020). Estimation of the asymptomatic ratio of novel coronavirus infections (COVID-19). International Journal of Infectious Diseases, 94, 154–155. doi:10.1016/j.ijid.2020.03.020.

Read, J. M., Bridgen, J. R. E., Cummings, D. A. T., Ho, A., & Jewell, C. P. (2020). Novel coronavirus 2019-nCoV: early estimation of epidemiological parameters and epidemic predictions. doi:10.1101/2020.01.23.20018549.

Tang, B., Bragazzi, N. L., Li, Q., Tang, S., Xiao, Y., & Wu, J. (2020). An updated estimation of the risk of transmission of the novel coronavirus (2019-nCov). Infectious Disease Modelling, 5, 248–255. doi:10.1016/j.idm.2020.02.001.

Full Text: PDF

DOI: 10.28991/HIJ-2021-02-04-05


  • There are currently no refbacks.

Copyright (c) 2021 Abbas Mahmoudabadi