Volume 2, Issue 1 (January 2023)
Health Science Monitor 2023, 2(1): 13-20 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Kazempour Dizaji M, Kiani A, Varahram M, Abedini A, Zare A, Roozbahani R, et al . Estimation and prediction of the prevalence rate of COVID-19 disease based on multilayer perceptron artificial neural networks model. Health Science Monitor 2023; 2 (1) :13-20
URL: http://hsm.umsu.ac.ir/article-1-100-en.html
URL: http://hsm.umsu.ac.ir/article-1-100-en.html
Mehdi Kazempour Dizaji * 
, Arda Kiani , Mohammad Varahram , Atefe Abedini , Ali Zare , Rahim Roozbahani , Niloufar Alizedeh Kolahdozi , Syeyd Alireza Nadji , Mohammad Ali Emamhadi , Majid Marjani
, Arda Kiani , Mohammad Varahram , Atefe Abedini , Ali Zare , Rahim Roozbahani , Niloufar Alizedeh Kolahdozi , Syeyd Alireza Nadji , Mohammad Ali Emamhadi , Majid Marjani
Mycobacteriology Research Center (MRC), National Research Institute of Tuberculosis and Lung Disease (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran
Abstract: (626 Views)
Background & Aims: Nowadays, with the coronavirus disease-2019 (COVID-19) pandemic, millions of people have been infected with the coronavirus, and most countries in the world have been unable to treat and control this condition. The aim of this study was to estimate and predict the COVID-19 prevalence rate based on multilayer perceptron artificial neural network (MLP-ANN) model.
Materials & Methods: In this cross-sectional study, based on the information of 4,372 patients with COVID-19 referred to Dr. Masih Daneshvari Hospital in Tehran, the prevalence rate of this disease was estimated. In addition, considering the role of the health measures and social restrictions, the trend of this index based on the MLP-ANN model was predicted.
Results: According to the results of this study, the prevalence of COVID-19 increased by an average of 7.05 per thousand people daily during the 48 days from the onset of the epidemic, and it reached about 341.96 per thousand people. Based on the MLP-ANN model with a lack of attention to the health measures by individuals in the community and failure to reduce social restrictions by the government, the COVID-19 prevalence increased by an average of 1.03 per thousand people per day. While in the case of attention to the health measures by the people and continued social restrictions by the state, the prevalence of this disease decreased by an average of 2.13 per thousand people, daily.
Conclusion: The study on the prevalence of COVID-19 disease and prediction of the trend of this index provides researchers with useful information about the role of the health measures and social restrictions in controlling this disease.
Materials & Methods: In this cross-sectional study, based on the information of 4,372 patients with COVID-19 referred to Dr. Masih Daneshvari Hospital in Tehran, the prevalence rate of this disease was estimated. In addition, considering the role of the health measures and social restrictions, the trend of this index based on the MLP-ANN model was predicted.
Results: According to the results of this study, the prevalence of COVID-19 increased by an average of 7.05 per thousand people daily during the 48 days from the onset of the epidemic, and it reached about 341.96 per thousand people. Based on the MLP-ANN model with a lack of attention to the health measures by individuals in the community and failure to reduce social restrictions by the government, the COVID-19 prevalence increased by an average of 1.03 per thousand people per day. While in the case of attention to the health measures by the people and continued social restrictions by the state, the prevalence of this disease decreased by an average of 2.13 per thousand people, daily.
Conclusion: The study on the prevalence of COVID-19 disease and prediction of the trend of this index provides researchers with useful information about the role of the health measures and social restrictions in controlling this disease.
Type of Study: Research |
Subject:
General
Received: 2022/12/15 | Accepted: 2023/01/2 | Published: 2023/01/20
Received: 2022/12/15 | Accepted: 2023/01/2 | Published: 2023/01/20
References
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. WHO Director-General's remarks at the media briefing on 2019-nCoV on 11 February 2020 [Internet]. Geneva: World Health Organization; 2020 [cited 2020 Mar 12]. [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. Hui DS, Azhar EI, Madani TA, Ntoumi F, Kock R, Dar O, Ippolito G, Mchugh TD, Memish ZA, Drosten C, Zumla A. The continuing 2019-nCoV epidemic threat of novel coronaviruses to global health-The latest 2019 novel coronavirus outbreak in Wuhan, China. International journal of infectious diseases. 2020 Feb 1;91:264-6. [DOI] [PMID] [PMCID]
5. COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU)". ArcGIS. Johns Hopkins University. Retrieved 26 May 2020. 26 May 2020. [URL]
6. Csse J. Coronavirus COVID-19 global cases by the center for systems science and engineering (CSSE) at Johns Hopkins University (JHU). CSSE, JHU: Coronavirus Resources Center. Diakses dari https://Covid-19virus. jhu. edu/map. html. Diakses pada. 2020 Jul;31. [URL]
7. CSSE J. Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). 2020. [URL]
8. Maitra S, Biswas M, Bhattacharjee S. Case-fatality rate in COVID-19 patients: a meta-analysis of publicly accessible database. medRxiv. 2020 Apr 14:2020-04. [DOI]
9. 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]
10. Mahase E. Coronavirus: covid-19 has killed more people than SARS and MERS combined, despite lower case fatality rate. 2020. [DOI] [PMID]
11. World Health Organization 2. WHO Director-General's remarks at the media briefing on 2019-nCoV on 11 February 2020. [URL]
12. Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, et al. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. New England journal of medicine. 2020 Jan 29. [DOI]
13. Chan JF, Yuan S, Kok KH, To KK, Chu H, Yang J, et al. 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 Feb 15;395(10223):514-23. [DOI] [PMID]
14. 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]
15. Ahn JY, Sohn Y, Lee SH, Cho Y, Hyun JH, Baek YJ, et al. 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]
16. Lu H. Drug treatment options for the 2019-new coronavirus (2019-nCoV). Bioscience trends. 2020 Feb 29;14(1):69-71. [DOI] [PMID]
17. Tanne JH. Covid-19: FDA approves use of convalescent plasma to treat critically ill patients. Bmj. 2020 Mar 26;368(m1256):m1256. [DOI] [PMID]
18. 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]
19. 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]
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. Kay JW, Titterington DM, Titterington SD, editors. Statistics and neural networks: advances at the interface. Oxford University Press on Demand; 1999. [Google Books]
22. Warner B, Misra M. Understanding neural networks as statistical tools. The american statistician. 1996 Nov 1;50(4):284-93. [DOI]
23. Yegnanarayana B. Artificial neural networks. PHI Learning Pvt. Ltd.; 2009 Jan 14. [Google Books]
24. 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]
25. 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]
26. Kwon YS, Kim YH, Song JU, Jeon K, Song J, Ryu YJ, et al. 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]
27. Ahmed FE. Artificial neural networks for diagnosis and survival prediction in colon cancer. Molecular cancer. 2005 Dec;4(1):1-2. [DOI] [PMID] [PMCID]
28. 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]
29. 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;150:w20203. [DOI]
30. Singh R, Adhikari R. Age-structured impact of social distancing on the COVID-19 epidemic in India. arXiv preprint arXiv:2003.12055. 2020 Mar 26. [URL]
31. Peng L, Yang W, Zhang D, Zhuge C, Hong L. Epidemic analysis of COVID-19 in China by dynamical modeling. arXiv preprint arXiv:2002.06563. 2020 Feb 16. [DOI]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
