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Vaka ve Ölüm Sayılarının Kısa Dönem Tahmini için Resmi Kaynaklardan COVID-19 Veri Takibinin Önemi

Year 2023, , 41 - 48, 16.04.2023
https://doi.org/10.46332/aemj.1033009

Abstract

Amaç: COVID-19 salgını sırasında hükümetler, bilim adamları, sağlık çalışanları ve çok sayıda insan, hastalığın yayılmasını durdurmak için stratejiler veya çözümler üzerinde çalışmıştır. Ne yazık ki artık vakaların izleme ihtiyacı hızla artmakta ve gerekli veya kısıtlayıcı önlemlerin alınması kaçınılmaz hale gelmektedir. Epidemiyolojik verilerin eksikliği ve sürekli değişen sayılar nedeniyle, daha az hataya açık tahmin modelleri ve yakın gelecek için güvenilir matematiksel modeller oluşturmak, daha iyi yasal eylemler ve önleme stratejilerinin harekete geçirilmesine yardımcı olacaktır.

Araçlar ve Yöntem: Bu çalışmada, farklı tahmin modelleri kullanılarak gelecekteki COVID-19 olaylarının sayısını tahmin etmek için 01/21/2020-02/05/2020 ve 21/01/2020-17/06/2020 tarihleri arasında on bir ülkenin günlük vaka sayılarının verileri kullanılmıştır. MAPE değerlerine dayalı olarak Auto-Regressive Integrated Moving Average (ARIMA), Brown's linear exponential smoothing (LES) ve Holt's LES modelleri ile mevcut sayıların analizinden sonra en uygun modeller seçilerek analizler yapılmıştır.

Bulgular: Çalışmamız, iki veri setini analiz ederek kısa vadeli gelecek tahminleri için en az hataya en uygun modelleri ortaya çıkararak bu modellerin seçilen ülkeler arasında veri güncellemelerinden sonra değiştiğini göstermiştir. Verilerin analiz edilmesiyle onbir ülkenin içinde Amerika, Türkiye, Brezilya, Rusya’nın verilerinin güncellenmesinin tahmin sonuçlarında değişikliklere neden olduğunu göstermiştir.

Sonuç: Bu çalışmanın sonuçları, mevcut yaklaşımlarda birden fazla istatistiksel model kullanmanın üstünlüğü olduğunu ve halihazırda karmaşık ve yorucu olan COVID-19'un yönetimi için matematiksel modeller oluşturmak ve geleceğe yönelik tahminler oluşturmak için verileri kullanırken sayılardaki dalgalanmaların dikkate alınması gerektiğini göstermektedir. Bu sayede, COVID-19 yayılımına karşı uygulanacak olan politikalar ve kısıtlamalar, daha doğru sonuçlar sağlamak için düzeltilmiş tahminler göz önüne alındığında daha başarılı olabilir.

References

  • 1. Liu YC, Kuo RL, SR Shih. COVID-19: The first documented coronavirus pandemic in history. Biomed J. 2020;43(4):328-333.
  • 2. Reintjes R, Das E, Klemm C, Jan Hendrik Richardus JH, Keßler V, Ahmad A. "Pandemic Public Health Paradox": Time Series Analysis of the 2009/10 Influenza A / H1N1 Epidemiology, Media Attention, Risk Perception and Public Reactions in 5 European Countries. PLoS One. 2016;11(3):e0151258.
  • 3. Chintalapudi N, Battineni G, F Amenta. COVID-19 virus outbreak forecasting of registered and recovered cases after sixty day lockdown in Italy: A data driven model approach. J Microbiol Immunol Infect. 2020;53 (3):396-403.
  • 4. Yousaf M, Zahir S, Riaz M, Hussain SM, Shan K. Statistical analysis of forecasting COVID-19 for upcoming month in Pakistan. Chaos Solitons Fractals. 2020;138:109926.
  • 5. Roosa K, Lee Y, Luo R, et al. Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020. Infect Dis Model. 2020;5:256-263.
  • 6. Li Q, W Feng, ve Quan YH. Trend and forecasting of the COVID-19 outbreak in China. J Infect. 2020;80(4):469-496.
  • 7. Fanelli D, Piazza F. Analysis and forecast of COVID-19 spreading in China, Italy and France. Chaos Solitons Fractals. 2020;134:109761.
  • 8. Mohammed AAA, Ahmed AE, Hong F, Mohamed AEA. Optimization Method for Forecasting Confirmed Cases of COVID-19 in China. J Clin Med. 2020;9(3):674.
  • 9. Wu JT, Leung K, Leung GM. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. Lancet. 2020;395(10225):689-697.
  • 10. Xiaorong W, Qiong Z, He Y, et al. Nosocomial outbreak of COVID-19 pneumonia in Wuhan, China. Eur Respir J. 2020;55(6).
  • 11. Papastefanopoulos V, Linardatos P, Kotsiantis S. COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population. Applied Sciences. 2020;10(11):3880.
  • 12. Gothai E, Thamilselvan R, Rajalaxmi RR, Sadana RM, Ragavi A, Sakthivel R. Prediction of COVID-19 growth and trend using machine learning approach. Mater Today Proc. 2021;15.
  • 13. Ayinde K., Adewale FL, Rauf IR, et al. Modeling Nigerian Covid-19 cases: A comparative analysis of models and estimators. Chaos Solitons Fractals. 2020;138:109911.
  • 14. Riley RD, Snell KIE, Ensor J, et al. Minimum sample size for developing a multivariable prediction model: Part I - Continuous outcomes. Stat Med. 2019;38(7): 1262-1275.
  • 15. Petropoulos F, Makridakis S. Forecasting the novel coronavirus COVID-19. PLoS One. 2020;15(3): e0231236.
  • 16. Cleo A, Lucia R, Athanasios T, Constantinos S. Data-based analysis, modelling and forecasting of the COVID-19 outbreak. PLoS One. 2020;15(3):e0230405.
  • 17. Heus P, Johanna AAGD, Romin P, et al. Uniformity in measuring adherence to reporting guidelines: the example of TRIPOD for assessing completeness of reporting of prediction model studies. BMJ Open. 2019;9(4):e025611.
  • 18. Allard, R. Use of time-series analysis in infectious disease surveillance. Bull World Health Organ. 1998;76(4):327-333.

Importance of Tracking COVID-19 Data from Official Sources for Short-Term Forecasting of Cases and Deaths

Year 2023, , 41 - 48, 16.04.2023
https://doi.org/10.46332/aemj.1033009

Abstract

Purpose: During the COVID-19 outbreak, governments, scientists, health workers, and numerous people worked on strategies or solutions for halting disease propagation. Unfortunately, the need for monitoring is steeply increasing, and restrictive actions are currently unavoidable. Due to the lack of epidemiological data and constantly changing numbers, constructing less error-prone predictive models and reliable mathematical models for the near future will help make better legal actions and prevention strategies.

Materials and Methods: In this study, daily data from eleven countries between 21/01/2020-02/05/2020 and 21/01/2020-17/06/2020 were used to forecast the number of future COVID-19 events by using different forecasting models. Best fit models were chosen after analysis with ARIMA, Brown’s LES, and Holt’s LES models based on MAPE values.

Results: The study showed the least error-prone best-fit models for short-term future predictions by analyzing two datasets and demonstrated that models changed after data updates among the selected countries. Investigation of the data from eleven countries, USA, Turkey, Brazil, and Russia analysis showed that updating data alters the model selection resulting in changes in the predictions.

Conclusion: The results of this study indicate that using more than one statistical model has superiority over the current approaches, and fluctuations in the numbers should be considered when using the data to construct mathematical models and create future predictions for the management of the already complicated and exhausting COVID-19 pandemic. Thus, policies and restrictions against COVID-19 spread might be more successful after considering that adjusted predictions for providing more accurate results.

References

  • 1. Liu YC, Kuo RL, SR Shih. COVID-19: The first documented coronavirus pandemic in history. Biomed J. 2020;43(4):328-333.
  • 2. Reintjes R, Das E, Klemm C, Jan Hendrik Richardus JH, Keßler V, Ahmad A. "Pandemic Public Health Paradox": Time Series Analysis of the 2009/10 Influenza A / H1N1 Epidemiology, Media Attention, Risk Perception and Public Reactions in 5 European Countries. PLoS One. 2016;11(3):e0151258.
  • 3. Chintalapudi N, Battineni G, F Amenta. COVID-19 virus outbreak forecasting of registered and recovered cases after sixty day lockdown in Italy: A data driven model approach. J Microbiol Immunol Infect. 2020;53 (3):396-403.
  • 4. Yousaf M, Zahir S, Riaz M, Hussain SM, Shan K. Statistical analysis of forecasting COVID-19 for upcoming month in Pakistan. Chaos Solitons Fractals. 2020;138:109926.
  • 5. Roosa K, Lee Y, Luo R, et al. Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020. Infect Dis Model. 2020;5:256-263.
  • 6. Li Q, W Feng, ve Quan YH. Trend and forecasting of the COVID-19 outbreak in China. J Infect. 2020;80(4):469-496.
  • 7. Fanelli D, Piazza F. Analysis and forecast of COVID-19 spreading in China, Italy and France. Chaos Solitons Fractals. 2020;134:109761.
  • 8. Mohammed AAA, Ahmed AE, Hong F, Mohamed AEA. Optimization Method for Forecasting Confirmed Cases of COVID-19 in China. J Clin Med. 2020;9(3):674.
  • 9. Wu JT, Leung K, Leung GM. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. Lancet. 2020;395(10225):689-697.
  • 10. Xiaorong W, Qiong Z, He Y, et al. Nosocomial outbreak of COVID-19 pneumonia in Wuhan, China. Eur Respir J. 2020;55(6).
  • 11. Papastefanopoulos V, Linardatos P, Kotsiantis S. COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population. Applied Sciences. 2020;10(11):3880.
  • 12. Gothai E, Thamilselvan R, Rajalaxmi RR, Sadana RM, Ragavi A, Sakthivel R. Prediction of COVID-19 growth and trend using machine learning approach. Mater Today Proc. 2021;15.
  • 13. Ayinde K., Adewale FL, Rauf IR, et al. Modeling Nigerian Covid-19 cases: A comparative analysis of models and estimators. Chaos Solitons Fractals. 2020;138:109911.
  • 14. Riley RD, Snell KIE, Ensor J, et al. Minimum sample size for developing a multivariable prediction model: Part I - Continuous outcomes. Stat Med. 2019;38(7): 1262-1275.
  • 15. Petropoulos F, Makridakis S. Forecasting the novel coronavirus COVID-19. PLoS One. 2020;15(3): e0231236.
  • 16. Cleo A, Lucia R, Athanasios T, Constantinos S. Data-based analysis, modelling and forecasting of the COVID-19 outbreak. PLoS One. 2020;15(3):e0230405.
  • 17. Heus P, Johanna AAGD, Romin P, et al. Uniformity in measuring adherence to reporting guidelines: the example of TRIPOD for assessing completeness of reporting of prediction model studies. BMJ Open. 2019;9(4):e025611.
  • 18. Allard, R. Use of time-series analysis in infectious disease surveillance. Bull World Health Organ. 1998;76(4):327-333.
There are 18 citations in total.

Details

Primary Language English
Subjects Clinical Sciences
Journal Section Original Articles
Authors

Naci Murat 0000-0003-2655-2367

Publication Date April 16, 2023
Published in Issue Year 2023

Cite

APA Murat, N. (2023). Importance of Tracking COVID-19 Data from Official Sources for Short-Term Forecasting of Cases and Deaths. Ahi Evran Medical Journal, 7(1), 41-48. https://doi.org/10.46332/aemj.1033009
AMA Murat N. Importance of Tracking COVID-19 Data from Official Sources for Short-Term Forecasting of Cases and Deaths. Ahi Evran Med J. April 2023;7(1):41-48. doi:10.46332/aemj.1033009
Chicago Murat, Naci. “Importance of Tracking COVID-19 Data from Official Sources for Short-Term Forecasting of Cases and Deaths”. Ahi Evran Medical Journal 7, no. 1 (April 2023): 41-48. https://doi.org/10.46332/aemj.1033009.
EndNote Murat N (April 1, 2023) Importance of Tracking COVID-19 Data from Official Sources for Short-Term Forecasting of Cases and Deaths. Ahi Evran Medical Journal 7 1 41–48.
IEEE N. Murat, “Importance of Tracking COVID-19 Data from Official Sources for Short-Term Forecasting of Cases and Deaths”, Ahi Evran Med J, vol. 7, no. 1, pp. 41–48, 2023, doi: 10.46332/aemj.1033009.
ISNAD Murat, Naci. “Importance of Tracking COVID-19 Data from Official Sources for Short-Term Forecasting of Cases and Deaths”. Ahi Evran Medical Journal 7/1 (April 2023), 41-48. https://doi.org/10.46332/aemj.1033009.
JAMA Murat N. Importance of Tracking COVID-19 Data from Official Sources for Short-Term Forecasting of Cases and Deaths. Ahi Evran Med J. 2023;7:41–48.
MLA Murat, Naci. “Importance of Tracking COVID-19 Data from Official Sources for Short-Term Forecasting of Cases and Deaths”. Ahi Evran Medical Journal, vol. 7, no. 1, 2023, pp. 41-48, doi:10.46332/aemj.1033009.
Vancouver Murat N. Importance of Tracking COVID-19 Data from Official Sources for Short-Term Forecasting of Cases and Deaths. Ahi Evran Med J. 2023;7(1):41-8.

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