Risk Identification and Prediction for COVID-19 Mortality
Risk Identification and Prediction for COVID-19 Mortality
DOI:
https://doi.org/10.46570/utjms.vol9-2021-462Keywords:
COVID-19, Case Fatality Rate, Mortality Rate, Logistic Regression, Receiver Operating Characteristics Curve, Area Under the CurveAbstract
This paper studies several key metrics for COVID-19 using a public surveillance system data set. It compares the difference between two case fatality rates: the naive case fatality rate, which has been frequently mentioned in media outlets, and one which is the sample estimate for the mortality rate. A logistic regression model is applied to modeling the daily mortality rate. The conclusion is that time, gender, age and some of their interactions, appear to have a significant impact on the mortality rate; the daily mortality rate has been decreasing since the outbreak; males older than 60 has been the most vulnerable group. The receiver operating characteristics curve and the curve under the area show that the proposed logistic model is capable of predicting the outcome of a reported case with accuracy as high as 89%. These findings are helpful in assessing the magnitude of the risk posed by the COVID-19 virus to certain groups, predicting outcome severity, and optimally allocating medical resources such as intensive care units and ventilators.
References
Agresti, A. (2002) Categorical Data Analysis (2nd), Wiley-Interscience, New Jersey.
Angelopoulos, A. N., Pathak, R., Varma, R., & Jordan, M. (2020). On Identifying and Mitigating Bias in the Estimation of the COVID-19 Case Fatality Rate. Harvard Data Science Review, DOI: 10.1162/99608f92.f01ee285
Bendavid, E., Mulaney, B., Sood, N., Shah, S., Ling, E., Bromley-Dulfano, R., Lai, C., Weissberg, Z., Saavedra-Walker, R., Tedrow, J., Tversky, D., Bogan, A., Kupiec, T., Eichner, D., Gupta, R., Ioannidis, P. A. J., & Bhattacharya, J. (2020). COVID-19 Antibody Seroprevalence in Santa Clara County, California. medRxiv, 2020.04.14.20062463. doi: https://doi.org/10.1101/2020.04.14.20062463
Bertsimas, D., Boussioux, L., Cory-Wright, R., Delarue, A., Digalakis, V., Jacquillat, A., Kitane, L. D., Lukin, G., Li, M., Mingardi, L., Nohadani, O., Orfanoudaki, A., Papalexopoulos, T., Paskov, I., Pauphilet, J., Lami, O. S., Stellato, B., Bouardi, H. T., Carballo, K. V., Wiberg H., Zeng, C. (2021) From predictions to prescriptions: A data-driven response to COVID-19, Health Care Management Science,
https://doi.org/10.1007/s10729-020-09542-0
Bundgaard} Bundgaard H, Bundgaard JS, Raaschou-Pedersen DET, von Buchwald C, Todsen T, Norsk JB, Pries-Heje MM, Vissing CR, Nielsen PB, Winsl0‹3w UC, Fogh K, Hasselbalch R, Kristensen JH, Ringgaard A, Porsborg Andersen M, Goecke NB, Trebbien R, Skovgaard K, Benfield T, Ullum H, Torp-Pedersen C, Iversen K (2021) Effectiveness of Adding a Mask Recommendation to Other Public Health Measures to Prevent SARS-CoV-2 Infection in Danish Mask Wearers: A Randomized Controlled Trial. Annals of Internal Medicine, 174, 335-343. doi: 10.7326/M20-6817
Chen, N., Zhou, M., Dong, X., Qu, J., Gong, F., Han, Y. Qiu, Y., Wang, J., Liu, Y., Wei, Y., Xia, J., Yu, T., Zhang, X., &
Zhang, L. (2020). Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. The Lancet, 395, 507-513. https://doi.org/10.1016/S0140-6736(20)30211-7
Chen, Y., Liu, Q., & Guo, D. (2020). Emerging coronaviruses: Genome structure, replication, andpathogenesis. Journal of Virology, 92, 418-423.
doi: 10.1002/jmv.25681
Chiu, W.A., Fischer, R., & Ndeffo-Mbah, M.L. (2020). State-level needs for social distancing and contact tracing to contain COVID-19 in the United States. Nature Human Behavior, 4, 1080-1090. https://doi: 10.1038/s41562-020-00969-7
Cortegiani, A., Ingoglia, G., Ippolito, M., Giarratano, A., & Einav, S. (2020). A systematic review on the efficacy and safety of chloroquine for the treatment of COVID-19. Journal of Critical Care, 57, 279-283. doi:10.1016/j.jcrc.2020.03.005
Green, D. M. & Swets, J. A. (1966). Signal Detection Theory and Psychophysics. John Wiley and Sons, New York.
Guan, W., Ni, Z., Hu, Y., Liang, W., Ou, C., He, J., Liu, L., Shan, H., Lei, C.,
Hui, D.S.C., Du, B., Li, L., Zeng, G., Yuen, K. Y., Chen, R., Tang, C., Wang, T., Chen, P., Xiang, J., ..., Zhong, N. (2019). Clinical characteristics of coronavirus disease 2019 in China. The New England Journal of Medicine, 382, 1708-1720. doi: 10.1056/NEJMoa2002032
Havers FP, Reed C, Lim T, Montgomery JM, Klena JD, Hall AJ, Fry AM, Cannon DL, Chiang CF, Gibbons A, Krapiunaya I, Morales-Betoulle M, Roguski K, Rasheed MAU, Freeman B, Lester S, Mills L, Carroll DS, Owen SM, Johnson JA, Semenova V, Blackmore C, Blog D, Chai SJ, Dunn A, Hand J, Jain S, Lindquist S, Lynfield R, Pritchard S, Sokol T, Sosa L, Turabelidze G, Watkins SM, Wiesman J, Williams RW, Yendell S, Schiffer J, Thornburg NJ (2020). Seroprevalence of Antibodies to SARS-CoV-2 in 10 Sites in the United States, March 23-May 12, 2020. JAMA Intern Med, 180, 1576-86. doi:10.1001/jamainternmed.2020.4130
Kobayashi, T., Jung, S., Linton, M. N., Kinoshita, R., Hayashi, K., Miyama, T., Anzai, A., Yang, Y., Yuan, B., Akhmetzhanov, A. R., Suzuki, A., & Nishiura, H. (2020). Communicating the Risk of Death from Novel Coronavirus Disease (COVID-19). Journal of Clinical Medicine, 9, 580. doi.org/10.3390/jcm9020580
Li, X., Xu, S., Yu, M., Wang, K., Tao, Y., Zhou, Y., Shi, J., Zhou, M., Wu, B., Yang, Z., Zhang, C., Yue, J., Zhang, Z., Renz, H., Liu, X., Xie, J., Xie, M., & Zhao, J. (2020). Risk factors for severity and mortality in adult COVID-19 inpatients in Wuhan. The Journal of Allergy and Clinical Immunology, 146, 110-118. doi: 10.1016/j.jaci.2020.04.006
Liu, Y., Gayle, A. A., Wilder-Smith, A., & Rocklov, J. (2020). The reproductive number of COVID-19 is higher compared to SARS coronavirus. Journal of Travel Medicine, 27 1-4. doi: 10.1093/jtm/taaa021
McCullagh, P. & Nelder, J. A. (1989). Generalized Linear Models (2nd ed.), Chapman & Hall/CRC Monographs on Statistics & Applied Probability.
Mizumoto, K. & Chowell, G. (2020). Estimating Risk for Death from Coronavirus Disease, China, January-February 2020. Emerging Infectious Diseases, 26, 1251-1256. dx.doi.org/10.3201/eid2606.200233
R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
Reich, N. G., Lessler, J., Cummings, D. A. T. & Brookmeyer, R. (2012). Estimating Absolute and Relative Case Fatality Ratios from Infectious Disease Surveillance Data. Biometrics, 68, 598-606. doi: 10.1111/j.1541-0420.2011.01709.x
Ritchie, H. & Roser, M. (2020). What do we know about the risk of dying from COVID-19? https://ourworldindata.org/covid-mortality-risk
Ritchie, H., Ortiz-Ospina, E., Beltekian, D., Mathieu, E., Hasell, J., Macdonald, B., Giattino, C., Appel, C., & Roser, M. (2020). Mortality Risk of COVID-19. https://ourworldindata.org/mortality-risk-covid
Shao, Q., Thompson, G. & Thompson, A. (2020). COVID-19 Risk Factor Identification based on Ohio Data. Translation: The University of Toledo Journal of Medical Sciences, 8, 6-14.
Shen, C. Y. (2020). Logistic growth modelling of COVID-19 proliferation in China and its international implications. International Journal of Infectious Diseases, 96, 582-589. doi: 10.1016/j.ijid.2020.04.085
Zhou, Y., Wang, L., Zhang, L., Shi, L., Yang, K., He, J., Zhao, B., Overton, W., Purkayastha, S., & Song, P. (2020). A Spatiotemporal Epidemiological Prediction Model to Inform County-Level COVID-19 Risk in the United States. Harvard Data Science Review, https://doi.org/10.1162/99608f92.79e1f45e
Xu, C., Dong, Y., Yu, X., Wang, H., Tsamlag, L., Zhang, S., Chang, R., Wang, Z., Yu, Y., Long, R., Wang, Y., Xu, G., Shen, T., Wang, S., Zhang, X., Wang, H., & Cai, Y. (2020). Estimation of reproduction numbers of COVID-19 in typical countries and epidemic trends under different prevention and control scenarios. Frontiers of medicine, 14, 613-622. doi:10.1007/s11684-020-0787-4
Xu, K., Zhou, M., Yang, D., Ling, Y., Liu, K., Bai, T., Cheng, Z., & Li, J. (2020). Application of ordinal logistic regression analysis to identify the determinants of illness severity of COVID-19 in China. Epidemiology and Infection, 148, e146, 1-11. doi.org/10.1017/S0950268820001533
Zhang, Q., Bastard, P., Liu, Z., Pen, J. L., Moncada-Velez, M., Chen, J., Ogishi, M., Sabli, K. D. I., Hodeib, S., Korol, C., Rosain, J., Bilguvar, K., Ye, J., Bolze, A., Bigio, B., Yang, R., Arias, A. A., Zhou, Q., Zhang, Y., ..., Casanova, J. (2020). Inborn errors of type I IFN immunity in patients with life-threatening COVID-19. Science, 370. doi: 10.1126/science.abd4570
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