Volume 1, Issue 2 (November 2022)                   Health Science Monitor 2022, 1(2): 131-137 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Kazempour Dizaji M, Zare A, Tabarsi P. Study and prediction of the case-fatality rate (CRF) of COVID-19 based on patient’s medical information referred to Dr. Masih Daneshvari Hospital in Tehran. Health Science Monitor 2022; 1 (2) :131-137
URL: http://hsm.umsu.ac.ir/article-1-60-en.html
Mycobacteriology Research Center (MRC),National Research Institute of Tuberculosis and Lung Disease (NRITLD) , Shahid Beheshti University of Medical Sciences, Tehran, Iran
Abstract:   (585 Views)
Background & Aims:  Coronavirus disease 2019 (COVID-19) is an acute respiratory syndrome that despite global health efforts to prevent its spread, it still has high fatality rates in many countries.
Materials & Methods: Based on the medical information of 4,372 COVID-19 patients referring to Dr. Masih Daneshvari Hospital in Tehran, Iran, the case-fatality rate (CFR) for COVID-19 was calculated, and the trend of this index was assessed using the artificial neural network (ANN) model.
Results: In this study, the CFR for COVID-19 reduced by an average of 0.4% per day and reached 4.43% during 50 days of the epidemic onset. Predicting the daily trend of this index using ANN model also showed a very gentle downward trend. According to the prediction of this model, during the first 100 days and also the second 100 days from the COVID-19 epidemic onset, the CFR for this disease decreased by an average of 0.03% and 0.01% per day, and reached 3.87% and 3.05%, respectively,
Conclusion: The use of CFR for COVID-19 and prediction of the trend of this index for the future can provide valuable information on the diagnosis of the disease severity and evaluation of the effectiveness of control and treatment strategies, as well as assessment of the health care.
Full-Text [PDF 517 kb]   (355 Downloads)    
Type of Study: Research | Subject: General
Received: 2022/08/20 | Accepted: 2022/09/4 | Published: 2022/11/19

1. Roush S, Fast H, Miner CE, Vins H, Baldy L, McNall R, Kang S, Vundi V. National Center for Immunization and Respiratory Diseases (NCIRD) Support for Modernization of the Nationally Notifiable Diseases Surveillance System (NNDSS) to Strengthen Public Health Surveillance Infrastructure in the US. In2019 CSTE Annual Conference 2019 Jun 3. CSTE. [Google Scholar]
2. World Health Organization 2. WHO Director-General's remarks at the media briefing on 2019-nCoV on 11 February 2020. [URL]
3. Guan WJ, Ni ZY, Hu Y, Liang WH, Ou CQ, He JX, Liu L, Shan H, Lei CL, Hui DS, Du B. Clinical characteristics of coronavirus disease 2019 in China. New England journal of medicine. 2020 Apr 30;382(18):1708-20. [DOI] [PMID] [PMCID]
4. Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, Ren R, Leung KS, Lau EH, Wong JY, Xing X. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. New England journal of medicine. 2020 Jan 29. [DOI] [PMID] [PMCID]
5. Chan JF, Yuan S, Kok KH, To KK, Chu H, Yang J, Xing F, Liu J, Yip CC, Poon RW, Tsoi HW. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. The lancet. 2020;395(10223):514-23. [DOI] [PMID]
6. Maitra S, Biswas M, Bhattacharjee S. Case-fatality rate in COVID-19 patients: a meta-analysis of publicly accessible database. medRxiv. 2020 Jan 1. [DOI]
7. Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention. jama. 2020 Apr 7;323(13):1239-42. [DOI] [PMID]
8. Mahase E. Coronavirus: covid-19 has killed more people than SARS and MERS combined, despite lower case fatality rate. 2020. [DOI] [PMID]
9. Battegay M, Kuehl R, Tschudin-Sutter S, Hirsch HH, Widmer AF, Neher RA. 2019-novel Coronavirus (2019-nCoV): estimating the case fatality rate-a word of caution. Swiss medical weekly. 2020 Feb 7(5). [DOI]
10. Onder G, Rezza G, Brusaferro S. Case-fatality rate and characteristics of patients dying in relation to COVID-19 in Italy. Jama. 2020 May 12;323(18):1775-6. [DOI] [PMID]
11. Team E. The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19)-China, 2020. China CDC weekly. 2020 Feb 2;2(8):113. [DOI]
12. Warner B, Misra M. Understanding neural networks as statistical tools. The american statistician. 1996 Nov 1;50(4):284-93. [DOI]
13. Kay JW, Titterington DM. Statistics and neural networks: advances at the interface. Oxford University Press on Demand; 1999. [Google Books]
14. Sargent DJ. Comparison of artificial neural networks with other statistical approaches: results from medical data sets. Cancer: Interdisciplinary International Journal of the American Cancer Society. 2001 Apr 15;91(S8):1636-42. https://doi.org/10.1002/1097-0142(20010415)91:8+<1636::AID-CNCR1176>3.0.CO;2-D [DOI] [PMID]
15. Chi CL, Street WN, Wolberg WH. Application of artificial neural network-based survival analysis on two breast cancer datasets. InAMIA annual symposium proceedings 2007 (Vol. 2007, p. 130). American Medical Informatics Association. [PMID] [PMCID]
16. Kwon YS, Kim YH, Song JU, Jeon K, Song J, Ryu YJ, Choi JC, Kim HC, Koh WJ. Risk factors for death during pulmonary tuberculosis treatment in Korea: a multicenter retrospective cohort study. Journal of Korean medical science. 2014 Sep 1;29(9):1226-31. [DOI] [PMID] [PMCID]
17. Ahmed FE. Artificial neural networks for diagnosis and survival prediction in colon cancer. Molecular cancer. 2005 Dec;4(1):1-2. [DOI] [PMID] [PMCID]
18. Akl A, Ismail AM, Ghoneim M. Prediction of graft survival of living-donor kidney transplantation: nomograms or artificial neural networks? Transplantation. 2008 Nov 27;86(10):1401-6. [DOI] [PMID]
19. CSSE J. Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). 2020. [URL]
20. Aslan IH, Demir M, Wise MM, Lenhart S. Modeling COVID‐19: Forecasting and analyzing the dynamics of the outbreaks in Hubei and Turkey. Mathematical Methods in the Applied Sciences. 2022 Jul 15;45(10):6481-94. [DOI]
21. Li G, De Clercq E. Therapeutic options for the 2019 novel coronavirus (2019-nCoV). Nature reviews Drug discovery. 2020 Mar;19(3):149-50. [DOI] [PMID]
22. Lu H. Drug treatment options for the 2019-new coronavirus (2019-nCoV). Bioscience trends. 2020 Feb 29;14(1):69-71. [DOI] [PMID]
23. Tanne JH. Covid-19: FDA approves use of convalescent plasma to treat critically ill patients. Bmj. 2020 Mar 26;368(m1256). [DOI] [PMID]
24. Roback JD, Guarner J. Convalescent plasma to treat COVID-19: possibilities and challenges. Jama. 2020 Apr 28;323(16):1561-2. [DOI] [PMID]
25. Ahn JY, Sohn Y, Lee SH, Cho Y, Hyun JH, Baek YJ, Jeong SJ, Kim JH, Ku NS, Yeom JS, Roh J. Use of convalescent plasma therapy in two COVID-19 patients with acute respiratory distress syndrome in Korea. Journal of Korean medical science. 2020 Apr 13;35(14). [DOI] [PMID] [PMCID]

Add your comments about this article : Your username or Email:

Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2024 All Rights Reserved | Health Science Monitor

Designed & Developed by : Yektaweb