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ARIMA Modeli Kullanılarak Dünyadaki Maymun Çiçeği Vakalarının Tahmini

Yıl 2023, Sayı: 46, 37 - 45, 31.01.2023
https://doi.org/10.31590/ejosat.1190981

Öz

Dünyada Covid-19 salgını henüz bitmemişken maymun çiçeği salgını başladı. Maymun çiçeği virüsü 4 ayda 59'dan fazla ülkeye yayıldı. Bu yayılmayı etkin bir şekilde kontrol etmek için bilgisayar destekli tahmin modellerine ihtiyaç vardır. Zaman serisi modellerinin salgının etkisinin tahmin edilmesinde ve gerekli önlemlerin alınmasında etkili olduğu daha önceki salgınlarda görülmüştür. Bu çalışmada, dünyadaki maymun çiçeği vaka sayısını başarılı bir şekilde tahmin etmek için farklı Otomatik Regresif Entegre Hareketli Ortalama (ARIMA) modelleri geliştirilmiştir. Çalışmada 07 Mayıs-12 Temmuz 2022 tarihleri arasında teyit edilen günlük maymun çiçeği vakaları verileri kullanılmıştır. ARIMA modellerinin eğitiminde 07 Mayıs 2022-02 Temmuz verileri kullanılmıştır. Modellerin tahmin performansları 03 Temmuz-12 Temmuz 2022 verileri ile test edilmiştir. Test sonuçlarına göre en düşük RMSE=483, MAE=410 ve MAPE=4.82 değerine sahip ARIMA(2,2,1) modeli en başarılı model olarak belirlendi. Belirlenen ARIMA modelinin yaklaşık %5 civarında bir hata değeri ile gerçek değerlerle iyi bir uyum içinde olduğu tespit edilmiştir. İleriki 7 gün için maymun çiçeği vakalarının sayısı ARIMA(2,2,1) modeli kullanılarak tahmin edildi. Model, 19 Temmuz 2022 için maymun çiçeği vakalarının sayısını 15056 olarak tahmin ederken, gerçek vaka sayısının 15032 olması modelin başarısını kanıtlamaktadır. Bu çalışma ARIMA yöntemini kullanarak maymun çiçeği vakalarının sayısını tahmin eden ilk çalışmadır ve sonuçlar ARIMA modelinin maymun çiçeği vaka sayısını tahmin etmek için uygun bir yöntem olduğunu göstermektedir.

Kaynakça

  • Angelo, K. M., Petersen, B. W., Hamer, D. H., Schwartz, E., & Brunette, G. (2019). Monkeypox transmission among international travellers—serious monkey business?. Journal of travel medicine, 26(5), taz002. https://doi.org/10.1093/jtm/taz002
  • Arita, I., Jezek, Z., Khodakevich, L., & Ruti, K. (1985). Human monkeypox: a newly emerged orthopoxvirus zoonosis in the tropical rain forests of Africa. The American journal of tropical medicine and hygiene, 34(4), 781-789. https://doi.org/10.4269/ajtmh.1985.34.781
  • Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
  • Brockwell, P. J., & Davis, R. A. (Eds.). (2002). Introduction to time series and forecasting. New York, NY: Springer New York.
  • Bunge, E. M., Hoet, B., Chen, L., Lienert, F., Weidenthaler, H., Baer, L. R., & Steffen, R. (2022). The changing epidemiology of human monkeypox—A potential threat? A systematic review. PLoS neglected tropical diseases, 16(2), e0010141. https://doi.org/10.1371/journal.pntd.0010141
  • Carvalho, A. R. S., Guimarães, A., Garcia, T. D. S. O., Madeira Werberich, G., Ceotto, V. F., Bozza, F. A., ... & França, M. (2021). Estimating COVID-19 pneumonia extent and severity from chest computed tomography. Frontiers in Physiology, 12, 617657. https://doi.org/10.3389/fphys.2021.617657
  • Ceylan, Z. (2020). Estimation of COVID-19 prevalence in Italy, Spain, and France. Science of The Total Environment, 729, 138817. https://doi.org/10.1016/j.scitotenv.2020.138817
  • Cheung, Y. W., & Lai, K. S. (1995). Lag order and critical values of the augmented Dickey–Fuller test. Journal of Business & Economic Statistics, 13(3), 277-280.
  • Cihan, P. (2021). ARIMA-based forecasting of total COVID-19 cases in the USA and India. In 2021 29th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE. https://doi.org/10.1109/SIU53274.2021.9477773.
  • Cihan, P. (2021b). Forecasting fully vaccinated people against COVID-19 and examining future vaccination rate for herd immunity in the US, Asia, Europe, Africa, South America, and the World. Applied Soft Computing, 111, 107708. https://doi.org/10.1016/j.asoc.2021.107708
  • Cihan, P. (2022). The machine learning approach for predicting the number of intensive care, intubated patients and death: The COVID-19 pandemic in Turkey. Sigma Journal of Engineering and Natural Sciences, 40(1), 85-94. https://doi.org/10.14744/sigma.2022.00007
  • Cihan, P. (2022b). Impact of the COVID-19 lockdowns on electricity and natural gas consumption in the different industrial zones and forecasting consumption amounts: Turkey case study. International Journal of Electrical Power & Energy Systems, 134, 107369. https://doi.org/10.1016/j.ijepes.2021.107369
  • Cihan, P., & Ozger, Z. B. (2022). A new approach for determining SARS-CoV-2 epitopes using machine learning-based in silico methods. Computational Biology and Chemistry, 98, 107688. https://doi.org/10.1016/j.compbiolchem.2022.107688
  • Elevli, S., Uzgören, N., Bingöl, D., & Elevli, B. (2016). Drinking water quality control: control charts for turbidity and pH. Journal of Water, Sanitation and Hygiene for Development, 6(4), 511-518. https://doi.org/10.2166/washdev.2016.016
  • Gao, A., & Gao, S. (2022). In Silico Identification of Non-cross-reactive Epitopes for Monkeypox Cell Surface-Binding Protein. https://doi.org/10.21203/rs.3.rs-1693979/v1
  • Gökler, S. H. Prediction of the Number of COVID-19 confirmed cases using the hybrid FUCOM-Pareto analysis-random forest method. Pamukkale University Journal of Engineering Sciences, 1000(1000), 0-0.
  • Heymann, D. L., Szczeniowski, M., & Esteves, K. (1998). Re-emergence of monkeypox in Africa: a review of the past six years. British medical bulletin, 54(3), 693-702.
  • Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International journal of forecasting, 22(4), 679-688.
  • Marennikova, S. S., Šeluhina, E. M., Mal'Ceva, N. N., Čimiškjan, K. L., & Macevič, G. R. (1972). Isolation and properties of the causal agent of a new variola-like disease (monkeypox) in man. Bulletin of the World Health Organization, 46(5), 599.
  • Mathieu, E., Spooner, F., Dattani, S., Ritchie, H., & Roser, M. (2022). Monkeypox. Our World in Data.
  • Okyay, R. A., Bayrak, E., Kaya, E., Şahin, A. R., Koçyiğit, B. F., Taşdoğan, A. M., ... & Sümbül, H. E. (2022). Another Epidemic in the Shadow of Covid 19 Pandemic: A Review of Monkeypox. proteins, 7, 10. https://doi.org/10.14744/ejmo.2022.2022
  • Ozger, Z. B., & Cihan, P. (2022). A novel ensemble fuzzy classification model in SARS-CoV-2 B-cell epitope identification for development of protein-based vaccine. Applied soft computing, 116, 108280. https://doi.org/10.1016/j.asoc.2021.108280
  • Petersen, E., Abubakar, I., Ihekweazu, C., Heymann, D., Ntoumi, F., Blumberg, L., ... & Zumla, A. (2019). Monkeypox—Enhancing public health preparedness for an emerging lethal human zoonotic epidemic threat in the wake of the smallpox post-eradication era. International journal of infectious diseases, 78, 78-84. https://doi.org/10.1016/j.ijid.2018.11.008
  • Zumla, A., Valdoleiros, S. R., Haider, N., Asogun, D., Ntoumi, F., Petersen, E., & Kock, R. (2022). Monkeypox outbreaks outside endemic regions: scientific and social priorities. The Lancet. Infectious Diseases. https://doi.org/10.1016/S1473-3099(22)00354-1

Forecasting of Monkeypox Cases in the World Using the ARIMA Model

Yıl 2023, Sayı: 46, 37 - 45, 31.01.2023
https://doi.org/10.31590/ejosat.1190981

Öz

While the Covid-19 epidemic in the world was not over yet, the monkeypox epidemic started. The monkeypox virus spread to more than 59 countries in 4 months. Computer-aided forecasting models are needed to effectively control this spread. It has been seen in previous outbreaks that time-series models are effective in estimating the impact of the epidemic and taking necessary precautions. In this study, different Automatic Regressive Integrated Moving Average (ARIMA) models were developed to successfully forecast the number of monkeypox cases in the World. Daily confirmed monkeypox cases data from 07 May-12 July 2022 were used in the study. 07 May 2022-02 July data were used in the training of ARIMA models. The prediction performances of the models were tested with the data of 03 July-12 July 2022. According to the test results, the ARIMA(2,2,1) model with the lowest RMSE=483, MAE=410, and MAPE=4.82 was determined as the most successful model. It has been determined that the determined ARIMA model is in good agreement with the real values with an average error value of around 5%. The number of monkeypox cases for the next 7-day was forecasted using ARIMA(2,2,1) model. While the model predicts the number of monkeypox cases to be 15056 for 19 July 2022, the actual number of cases is 15032 proves the model's success. This is the first study to estimate the number of monkeypox cases using the ARIMA method, and the results show that the ARIMA model is a convenient method for estimating the number of monkeypox cases.

Kaynakça

  • Angelo, K. M., Petersen, B. W., Hamer, D. H., Schwartz, E., & Brunette, G. (2019). Monkeypox transmission among international travellers—serious monkey business?. Journal of travel medicine, 26(5), taz002. https://doi.org/10.1093/jtm/taz002
  • Arita, I., Jezek, Z., Khodakevich, L., & Ruti, K. (1985). Human monkeypox: a newly emerged orthopoxvirus zoonosis in the tropical rain forests of Africa. The American journal of tropical medicine and hygiene, 34(4), 781-789. https://doi.org/10.4269/ajtmh.1985.34.781
  • Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
  • Brockwell, P. J., & Davis, R. A. (Eds.). (2002). Introduction to time series and forecasting. New York, NY: Springer New York.
  • Bunge, E. M., Hoet, B., Chen, L., Lienert, F., Weidenthaler, H., Baer, L. R., & Steffen, R. (2022). The changing epidemiology of human monkeypox—A potential threat? A systematic review. PLoS neglected tropical diseases, 16(2), e0010141. https://doi.org/10.1371/journal.pntd.0010141
  • Carvalho, A. R. S., Guimarães, A., Garcia, T. D. S. O., Madeira Werberich, G., Ceotto, V. F., Bozza, F. A., ... & França, M. (2021). Estimating COVID-19 pneumonia extent and severity from chest computed tomography. Frontiers in Physiology, 12, 617657. https://doi.org/10.3389/fphys.2021.617657
  • Ceylan, Z. (2020). Estimation of COVID-19 prevalence in Italy, Spain, and France. Science of The Total Environment, 729, 138817. https://doi.org/10.1016/j.scitotenv.2020.138817
  • Cheung, Y. W., & Lai, K. S. (1995). Lag order and critical values of the augmented Dickey–Fuller test. Journal of Business & Economic Statistics, 13(3), 277-280.
  • Cihan, P. (2021). ARIMA-based forecasting of total COVID-19 cases in the USA and India. In 2021 29th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE. https://doi.org/10.1109/SIU53274.2021.9477773.
  • Cihan, P. (2021b). Forecasting fully vaccinated people against COVID-19 and examining future vaccination rate for herd immunity in the US, Asia, Europe, Africa, South America, and the World. Applied Soft Computing, 111, 107708. https://doi.org/10.1016/j.asoc.2021.107708
  • Cihan, P. (2022). The machine learning approach for predicting the number of intensive care, intubated patients and death: The COVID-19 pandemic in Turkey. Sigma Journal of Engineering and Natural Sciences, 40(1), 85-94. https://doi.org/10.14744/sigma.2022.00007
  • Cihan, P. (2022b). Impact of the COVID-19 lockdowns on electricity and natural gas consumption in the different industrial zones and forecasting consumption amounts: Turkey case study. International Journal of Electrical Power & Energy Systems, 134, 107369. https://doi.org/10.1016/j.ijepes.2021.107369
  • Cihan, P., & Ozger, Z. B. (2022). A new approach for determining SARS-CoV-2 epitopes using machine learning-based in silico methods. Computational Biology and Chemistry, 98, 107688. https://doi.org/10.1016/j.compbiolchem.2022.107688
  • Elevli, S., Uzgören, N., Bingöl, D., & Elevli, B. (2016). Drinking water quality control: control charts for turbidity and pH. Journal of Water, Sanitation and Hygiene for Development, 6(4), 511-518. https://doi.org/10.2166/washdev.2016.016
  • Gao, A., & Gao, S. (2022). In Silico Identification of Non-cross-reactive Epitopes for Monkeypox Cell Surface-Binding Protein. https://doi.org/10.21203/rs.3.rs-1693979/v1
  • Gökler, S. H. Prediction of the Number of COVID-19 confirmed cases using the hybrid FUCOM-Pareto analysis-random forest method. Pamukkale University Journal of Engineering Sciences, 1000(1000), 0-0.
  • Heymann, D. L., Szczeniowski, M., & Esteves, K. (1998). Re-emergence of monkeypox in Africa: a review of the past six years. British medical bulletin, 54(3), 693-702.
  • Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International journal of forecasting, 22(4), 679-688.
  • Marennikova, S. S., Šeluhina, E. M., Mal'Ceva, N. N., Čimiškjan, K. L., & Macevič, G. R. (1972). Isolation and properties of the causal agent of a new variola-like disease (monkeypox) in man. Bulletin of the World Health Organization, 46(5), 599.
  • Mathieu, E., Spooner, F., Dattani, S., Ritchie, H., & Roser, M. (2022). Monkeypox. Our World in Data.
  • Okyay, R. A., Bayrak, E., Kaya, E., Şahin, A. R., Koçyiğit, B. F., Taşdoğan, A. M., ... & Sümbül, H. E. (2022). Another Epidemic in the Shadow of Covid 19 Pandemic: A Review of Monkeypox. proteins, 7, 10. https://doi.org/10.14744/ejmo.2022.2022
  • Ozger, Z. B., & Cihan, P. (2022). A novel ensemble fuzzy classification model in SARS-CoV-2 B-cell epitope identification for development of protein-based vaccine. Applied soft computing, 116, 108280. https://doi.org/10.1016/j.asoc.2021.108280
  • Petersen, E., Abubakar, I., Ihekweazu, C., Heymann, D., Ntoumi, F., Blumberg, L., ... & Zumla, A. (2019). Monkeypox—Enhancing public health preparedness for an emerging lethal human zoonotic epidemic threat in the wake of the smallpox post-eradication era. International journal of infectious diseases, 78, 78-84. https://doi.org/10.1016/j.ijid.2018.11.008
  • Zumla, A., Valdoleiros, S. R., Haider, N., Asogun, D., Ntoumi, F., Petersen, E., & Kock, R. (2022). Monkeypox outbreaks outside endemic regions: scientific and social priorities. The Lancet. Infectious Diseases. https://doi.org/10.1016/S1473-3099(22)00354-1
Toplam 24 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Pinar Cihan 0000-0001-7958-7251

Erken Görünüm Tarihi 31 Ocak 2023
Yayımlanma Tarihi 31 Ocak 2023
Yayımlandığı Sayı Yıl 2023 Sayı: 46

Kaynak Göster

APA Cihan, P. (2023). Forecasting of Monkeypox Cases in the World Using the ARIMA Model. Avrupa Bilim Ve Teknoloji Dergisi(46), 37-45. https://doi.org/10.31590/ejosat.1190981