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Forecasting for the number of the COVID-19 cases with Brown's linear exponential smoothing method: Comparison of the growth trends with 15 days, 30 and 60 days forecasts

Cilt: 13 Sayı: 2 30 Haziran 2022
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Forecasting for the number of the COVID-19 cases with Brown's linear exponential smoothing method: Comparison of the growth trends with 15 days, 30 and 60 days forecasts

Öz

Aim: The aim of this study was to estimate the number of the COVID-19 cases for the 15, 30 and 60-days with the ideal forecasting analysis methods by using the daily data of the Turkey, Germany, Brazil, United Arab Emirates and United Kingdom. Material and Methods: The data were reached from the Our World in Data COVID-19 dataset. The forecasts for the cumulative cases for 15, 30, and 60 days periods to 19 February 2022 were made. The most commonly used methods for forecasting are explanatory techniques and time series algorithms. The exponential smoothing method (Brown’s linear trend) was used for the five countries. Results: The analyses showed that five countries have followed a similar epidemic curve. For 60-day forecasts, it was estimated respectively that 10322701, 22434809, 9552781, 16937127, and 767819 total cases would be in Turkey, Brazil, Germany, the UK, and The UAE until February 19. For 30-day forecasts, it was estimated respectively that 12809393, 28752324, 12655999, 18857395, and 905537 total cases would be in Turkey, Brazil, Germany, the UK, and The UAE until February 19. For 15-day forecasts, it was estimated respectively that 13635838, 29678270, 14241248, 20006207, and 885958 total cases would be in Turkey, Brazil, Germany, the UK, and The UAE until February 19. Conclusion: The short-time forecasting methods will help to plan the necessary interventions to control the pandemic, and to see whether health resources such as allocated health personnel and intensive care units are sufficient.

Anahtar Kelimeler

Kaynakça

  1. 1. Our World in Data. Coronavirus Pandemic (COVID-19). https:// ourworldindata.org/coronavirus#coronavirus-country-profiles. 2022. Access date: 15.03.2022.
  2. 2. COVID-19 Coronovirus Pandemic. https://www.worldometers. info/coronavirus/. 2022. Access date: 20.03.2022.
  3. 3. JHU-CSSE. Johns Hopkins. Coronavirus Resource Center. https:// coronavirus.jhu.edu/map.html. 2022. Access date: 20.03.2022.
  4. 4. Guan W-j, Ni Z-y, Hu Y, et al. Clinical Characteristics of Coronavirus Disease 2019 in China N Engl J Med. 2020 Apr 30;382(18):1708- 1720. (doi: 10.1056/NEJMoa2002032)
  5. 5. WHO. Draft landscape and tracker of COVID-19 candidate vaccines. 2022. https://www.who.int/publications/m/item/ draft-landscape-of-covid-19-candidate-vaccines. Access date: 20.03.2022.
  6. 6. Our World in Data. Coronavirus (COVID-19) Vaccinations. https:// ourworldindata.org/covid-vaccinations. 2022. Access date: 20.03.2022.
  7. 7. Montgomery DC, Johnson LA, Gardiner JS. Forecasting and time series analysis. 2 ed. New York: McGraw-Hill Companies; 1990.
  8. 8. Xiao H, Jiang X, Chen C, et al. Using time series analysis to forecast the health-related quality of life of post-menopausal women with non-metastatic ER+ breast cancer: A tutorial and case study. Res Social Adm Pharm 2019.

Ayrıntılar

Birincil Dil

İngilizce

Konular

Sağlık Kurumları Yönetimi

Bölüm

Araştırma Makalesi

Yazarlar

Yayımlanma Tarihi

30 Haziran 2022

Gönderilme Tarihi

24 Nisan 2022

Kabul Tarihi

13 Haziran 2022

Yayımlandığı Sayı

Yıl 2022 Cilt: 13 Sayı: 2

Kaynak Göster

APA
Yapar, D. (2022). Forecasting for the number of the COVID-19 cases with Brown’s linear exponential smoothing method: Comparison of the growth trends with 15 days, 30 and 60 days forecasts. Turkish Journal of Clinics and Laboratory, 13(2), 232-241. https://doi.org/10.18663/tjcl.1108320
AMA
1.Yapar D. Forecasting for the number of the COVID-19 cases with Brown’s linear exponential smoothing method: Comparison of the growth trends with 15 days, 30 and 60 days forecasts. TJCL. 2022;13(2):232-241. doi:10.18663/tjcl.1108320
Chicago
Yapar, Dilek. 2022. “Forecasting for the number of the COVID-19 cases with Brown’s linear exponential smoothing method: Comparison of the growth trends with 15 days, 30 and 60 days forecasts”. Turkish Journal of Clinics and Laboratory 13 (2): 232-41. https://doi.org/10.18663/tjcl.1108320.
EndNote
Yapar D (01 Haziran 2022) Forecasting for the number of the COVID-19 cases with Brown’s linear exponential smoothing method: Comparison of the growth trends with 15 days, 30 and 60 days forecasts. Turkish Journal of Clinics and Laboratory 13 2 232–241.
IEEE
[1]D. Yapar, “Forecasting for the number of the COVID-19 cases with Brown’s linear exponential smoothing method: Comparison of the growth trends with 15 days, 30 and 60 days forecasts”, TJCL, c. 13, sy 2, ss. 232–241, Haz. 2022, doi: 10.18663/tjcl.1108320.
ISNAD
Yapar, Dilek. “Forecasting for the number of the COVID-19 cases with Brown’s linear exponential smoothing method: Comparison of the growth trends with 15 days, 30 and 60 days forecasts”. Turkish Journal of Clinics and Laboratory 13/2 (01 Haziran 2022): 232-241. https://doi.org/10.18663/tjcl.1108320.
JAMA
1.Yapar D. Forecasting for the number of the COVID-19 cases with Brown’s linear exponential smoothing method: Comparison of the growth trends with 15 days, 30 and 60 days forecasts. TJCL. 2022;13:232–241.
MLA
Yapar, Dilek. “Forecasting for the number of the COVID-19 cases with Brown’s linear exponential smoothing method: Comparison of the growth trends with 15 days, 30 and 60 days forecasts”. Turkish Journal of Clinics and Laboratory, c. 13, sy 2, Haziran 2022, ss. 232-41, doi:10.18663/tjcl.1108320.
Vancouver
1.Dilek Yapar. Forecasting for the number of the COVID-19 cases with Brown’s linear exponential smoothing method: Comparison of the growth trends with 15 days, 30 and 60 days forecasts. TJCL. 01 Haziran 2022;13(2):232-41. doi:10.18663/tjcl.1108320

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