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Environ Sci Pollut Res Int. 2021 Aug;28(30):40496-40506. doi: 10.1007/s11356-021-13824-7. Epub 2021 Apr 10.

The COVID-19 pandemic: prediction study based on machine learning models.

Environmental science and pollution research international

Zohair Malki, El-Sayed Atlam, Ashraf Ewis, Guesh Dagnew, Osama A Ghoneim, Abdallah A Mohamed, Mohamed M Abdel-Daim, Ibrahim Gad

Affiliations

  1. College of Computer Science and Engineering, Taibah University, Yanbu, Saudi Arabia.
  2. College of Computer Science and Engineering, Taibah University, Yanbu, Saudi Arabia. [email protected].
  3. Faculty of Science, Tanta University, Tanta, Egypt. [email protected].
  4. Department of Public Health and Occupational Medicine, Faculty of Medicine, Minia University, Minia, Egypt.
  5. Department of Public Health, Faculty of Health Sciences - AlQunfudah, Umm AlQura University, Meccah, Saudi Arabia.
  6. Department of Computer Science, Institute of Technology, Dire Dawa University, Dire Dawa, Ethiopia.
  7. Faculty of Computers and Informatics, Tanta University, Tanta, Egypt.
  8. Mathematics and Computer Science Department, Faculty of Science, Menoufia University, Shibin El Kom, Egypt.
  9. Department of Zoology, College of Science, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia.
  10. Pharmacology Department, Faculty of Veterinary Medicine, Suez Canal University, Ismailia, 41522, Egypt.
  11. Faculty of Science, Tanta University, Tanta, Egypt.

PMID: 33840016 PMCID: PMC8035887 DOI: 10.1007/s11356-021-13824-7

Abstract

COVID-19 was first discovered in Wuhan, China in December 2019. It is one of the worst pandemics in human history. Recent studies reported that COVID-19 is transmitted among humans by droplet infection or direct contact. COVID-19 pandemic has invaded more than 210 countries around the world and as of February 18

© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Keywords: Artificial intelligence; COVID-19 pandemic; Machine learning model; Prediction

References

  1. Bayyurt L. and Bayyurt B. (2020) Forecasting of COVID-19 cases and deaths using ARIMA models. medrxiv https://doi.org/10.1101/2020.04.17.20069237,10.1101%2F2020.04.17.20069237 - PubMed
  2. Chu DKW, Hui KPY, Perera RAPM, Miguel E, Oladipo JO, Traore A, Fassi-Fihri O, Chan MCW, Zhou Z, So RTY, Chevalier V, Peiris JSM (2019) A52 MERS corona-viruses from camels in Africa exhibit region-dependent genetic diversity. Virus Evolution 5 10.1093%2Fve%2Fvez002.051 , https://doi.org/10.1093/ve/vez002.051 - PubMed
  3. CIDRAP, (2020) CIDRAP - Center for Infectious Disease Research and Policy https://www.cidrap.umn.edu/news-perspective/2013/01/study-puts-global-2009-pandemic-h1n1-infection-rate-24 (Accessed April 2020) - PubMed
  4. Dandekar R, Barbastathis G, (2020) Quantifying the effect of quarantine control in covid-19 infectious spread using machine learning. medRxiv - PubMed
  5. Direkoglu C. and Sah M (2020) Worldwide and regional forecasting of coronavirus (covid-19) spread using a deep learning model https://doi.org/10.1101/2020.05.23.20111039,10.1101%2F2020.05.23.20111039 - PubMed
  6. Ebola (2020) First Ebola vaccine approved. Nat Biotechnol 38:6–6. https://doi.org/10.1038/2Fs41587-019-0385-710.1038/s41587-019-0385-7 - PubMed
  7. Healthline (2020) How deadly is the coronavirus compared to past outbreaks flu pandemic https://www.healthline.com/health-news/how-deadly-is-the-coronavirus-compared-to-past-outbreaks#2009-(H1N1)-flu-pandemic - PubMed
  8. Helmy, Y. A., Fawzy M., Shehata, A.A. (2020) The covid-19 pandemic: a comprehensive review of taxonomy, genetics, epidemiology, diagnosis, treatment, and control. Journal of Clinical Medicine 9 - PubMed
  9. Hu B, Zeng LP, Yang XL, Ge XY, Zhang W, Li B, Xie JZ, Shen XR, Zhang YZ, Wang N, Luo DS, Zheng XS, Wang MN, Daszak P, Wang LF, Cui J, Shi ZL (2017) Discovery of a rich gene pool of bat SARS-related coronaviruses provides new insights into the origin of SARS coronavirus. PLoS Pathog 13:e1006698. https://doi.org/10.1371/2Fjournal.ppat.1006698,10.1371/journal.ppat.1006698 - PubMed
  10. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X et al (2020) Clinical features of patients infected with 2019 novel corona virus in Wuhan, China. Lancet 395:497–506 - PubMed
  11. Kelly-Cirino C, Mazzola LT, Chua A, Oxenford CJ, Van Kerkhove MD (2019) An updated roadmap for MERS-CoV research and product development: focus on diagnostics. BMJ Glob Health 4:e001105 - PubMed
  12. Lai CC, Shih TP, Ko WC, Tang HJ, Hsueh PR (2020a) Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and corona virus disease-2019 (covid-19): the epidemic and the challenges. International journal of antimicrobial agents, 105924. https://doi.org/10.1016/j.ijantimicag.2020.105924 - PubMed
  13. Lai CC, Shih TP, Ko WC, Tang HJ, Hsueh PR (2020b) Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-483 2) and coronavirus disease-2019 (COVID-19): the epidemic and the challenges. International Journal of Antimicrobial485 Agents 55:105924. https://doi.org/10.1016/2Fj.ijantimicag.2020.105924,10.1016/j.ijantimicag.2020.105924 - PubMed
  14. Lai, C.C., Wang, C.Y., Wang, Y.H., Hsueh, S.C., Ko, W.C., Hsueh, P.R., 2020c. Global epidemiology of coronavirus disease 2019: disease incidence, daily cumulative index, mortality, and their association with country healthcare resources and economic status. International Journal of Antimicrobial Agents, 105946 - PubMed
  15. Lathouwers E, Wong EY, Luo D, Seyed kazemi S, Meyer SD, Brown K (2017) HIV-1 resistance rarely observed in subjects using darunavir once-daily regimens across clinical studies. HIV496 ClinicalTrials 18:196–104. https://doi.org/10.1080/2F15284336.2017.1387690,10.1080/15284336.2017.1387690 - PubMed
  16. Malki Z, Atlam ES, Hassanien AE, Dagnew G, Elhosseini MA, Gad I (2020) Association between weather data and COVID-19 pandemic predicting mortality rate: machine learning approaches. Chaos, Solitons & Fractals 138:110137. https://doi.org/10.1016/j.chaos.2020.110137,10.1016%2Fj.chaos.2020.110137 - PubMed
  17. Organization WH, et al. (2020) Rational use of personal protective equipment for coronavirus disease (COVID-19): interim guidance, 27 February 2020. Technical Report. World Health Organization - PubMed
  18. Punn NS, Sonbhadra SK, Agarwal S (2020) Covid-19 epidemic analysis using machine learning and deep learning algorithms. medRxiv - PubMed
  19. Qiu H, Wu J, Hong L, Luo Y, Song Q, Chen D (2020) Clinical and epidemiological features of 36 children with coronavirus disease 2019 (covid-19) in Zhejiang, China: an observational cohort study. Lancet Infect Dis 20:689–696 - PubMed
  20. Sohrabi C, Alsafi Z, O’Neill N, Khan M, Kerwan A, Al-Jabir A, Iosifidis C, Agha R (2020) World Health Organization declares global emergency: a review of the 2019 novel coronavirus (covid-19). International Journal of Surgery - PubMed
  21. World Health Organization (WHO) (2020) Coronavirus https://www.who.int/health-topics/coronavirus Accessed April 13, 2020 - PubMed
  22. Worldometer 2020 COVID-19 CORO-NAVIRUS PANDEMIC https://www.worldometers.info/coronavirus/ Accessed April 13, 2020 - PubMed
  23. Wu J, Liu J, Zhao X, Liu C, Wang W, Wang D, Xu W, Zhang C, Yu J, Jiang B et al. (2020) Clinical characteristics of imported cases of covid-19 in jiangsu province: a multi-center descriptive study. Clinical infectious diseases: an official publication of the Infectious Diseases Society of America - PubMed
  24. Yang P, Liu P, Li D, Zhao D (2020) Corona virus disease 2019, a growing threat to children? The Journal of Infection - PubMed
  25. Yosra AH, Mohamed F, Ahmed E, Ahmed S, Scott PK, Awad AS (2020) The COVID-19 pandemic: a comprehensive review of taxonomy, genetics, epidemiology, diagnosis, treatment, and control. J Clin Med 9:1255 - PubMed

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