پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شماره‌ها | 610 |
| تعداد مقالات | 9,028 |
| تعداد مشاهده مقاله | 67,082,892 |
| تعداد دریافت فایل اصل مقاله | 7,656,361 |
مدلسازی موارد تجمعی کووید-19 شهرستان یزد با استفاده از انواع تکنیک‌های سری زمانی و یادگیری ماشین و مقایسه کارایی آن‌ها | ||
| مدل سازی در مهندسی | ||
| دوره 21، شماره 75، دی 1402، صفحه 309-323 اصل مقاله (983.25 K) | ||
| نوع مقاله: مقاله صنایع | ||
| شناسه دیجیتال (DOI): 10.22075/jme.2023.29849.2404 | ||
| نویسندگان | ||
| محمد حسین کریمی زارچی1؛ داود شیشه بری* 2 | ||
| 1کارشناسی ارشد، دانشکده مهندسی صنایع، دانشگاه یزد | ||
| 2دانشیار،دانشکده مهندسی صنایع، دانشگاه یزد | ||
| تاریخ دریافت: 20 بهمن 1401، تاریخ بازنگری: 21 تیر 1402، تاریخ پذیرش: 12 شهریور 1402 | ||
| چکیده | ||
| بیماری کووید-19، یک بیماری تنفسی است که در اثر سندرم تنفسی حاد کرونا ویروس-2 ایجاد می‌شود. پیش‌بینی تعداد موارد جدید و مرگومیر می‌تواند گام مفیدی در پیش‌بینی هزینه‌ها و امکانات مورد نیاز در آینده باشد. هدف از مطالعه حاضر، مدلسازی و پیش‌بینی موارد جدید و مرگومیر در آینده است. 9 تکنیک پیش‌بینی بر روی داده‌های کووید-19 استان یزد به عنوان یک مطالعه موردی تحت آزمایش قرار گرفت و با استفاده از معیارهای ارزیابی میانگین مربعات خطا (MSE)، جذر میانگین مربعات خطا (RMSE)، میانگین قدر مطلق خطا (MAE) و میانگین درصد قدرمطلق خطا (MAPE) مدل‌ها باهم مقایسه شدند نتایج تحلیل نشان داد، بهترین مدل با توجه به معیارهای ارزیابی مذکور برای پیش‌بینی موارد تجمعی بستری کووید-19 مدل رگرسیون KNN و برای موارد تجمعی فوت مدل BATS می‌باشد. همچنین از نظر معیارهای ارزیابی، بدترین عملکرد در پیش بینی تجمعی موارد بستری و فوت، مدل شبکه‌های عصبی اتورگرسیو دارد. این مطالعه می‌تواند درک مناسبی از روند شیوع بیماری کووید-19 در این منطقه ارائه کند تا با اتخاذ اقدامات احتیاطی و تدوین سیاست‌های مناسب بتوان به نحو احسن از این همه‌گیری عبور کرد. همچنین برخلاف مطالعات دیگر، در مطالعه حاضر، از 9 تکنیک متفاوت و مقایسه آن‌ها، استفاده می‌شود که به نوبه خود، جامعیت بررسی و اطمینان از کارائی رویکرد به کار گرفته شده در تصمیم‌گیری را بالا می‌برد. | ||
| کلیدواژه‌ها | ||
| کووید-19؛ سری زمانی؛ پیش‌بینی؛ مدلسازی آماری | ||
| عنوان مقاله [English] | ||
| Modeling cumulative cases of Covid-19 in Yazd city using various time series techniques and machine learning and comparing their efficiency | ||
| نویسندگان [English] | ||
| Mohammad Hossein Karimizarchi1؛ Davood Shishebori2 | ||
| 1M.Sc. degree, Department of Industrial Engineering, Faculty of Industrial Engineering, Yazd University, Yazd, Iran | ||
| 2Associate Professor, Department of Industrial Engineering, Faculty of Industrial Engineering, Yazd University, Yazd, Iran | ||
| چکیده [English] | ||
| Coronavirus disease 2019 or Covid-19, which is also called acute respiratory disease NCAV-2019 or commonly called corona, is a respiratory disease caused by acute respiratory syndrome coronavirus-2. Forecasting the number of new cases and deaths during todays can be a useful step in predicting the costs and facilities needed in the future. This study aims to model and predict new cases and deaths efficiently in the future. Nine popular forecasting techniques are tested on the data of Covid-19 in Yazd city as a case study. Using the evaluation criteria of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and the mean absolute percentage of error (MAPE) of the models are compared. According to the selected evaluation criteria, the results of the comprehensive analysis emphasize that the most efficient models are the ARIMA model for predicting the cumulative cases of hospitalization of Covid-19 and the Theta model for the cumulative cases of death. Also, the autoregressive neural network model has the worst performance among other models for both hospitalization and death cases. | ||
| کلیدواژه‌ها [English] | ||
| Covid-19, time series, forecasting, Statistical modeling | ||
| مراجع | ||
|
[1] Cucinotta, Domenico, and Maurizio Vanelli. "WHO declares COVID-19 a pandemic." Acta bio medica: Atenei parmensis 91, no. 1 (2020): 157. [2] Cheng, Zhangkai J., and Jing Shan. "2019 Novel coronavirus: where we are and what we know." Infection 48 (2020): 155-163. [3] Tran, Thai T., L. T. Pham, and Q. X. Ngo. "Forecasting epidemic spread of SARS-CoV-2 using ARIMA model (Case study: Iran)." Global Journal of Environmental Science and Management 6, no. Special Issue (Covid-19) (2020): 1-10. [4] Zhang, Xingyu, Yuanyuan Liu, Min Yang, Tao Zhang, Alistair A. Young, and Xiaosong Li. "Comparative study of four time series methods in forecasting typhoid fever incidence in China." PloS one 8, no. 5 (2013): e63116. [5] Chen, Ye, Kunkun Leng, Ying Lu, Lihai Wen, Ying Qi, Wei Gao, Huijie Chen et al. "Epidemiological features and time-series analysis of influenza incidence in urban and rural areas of Shenyang, China, 2010–2018." Epidemiology & Infection 148 (2020): e29. [6] Olanrewaju, Samuel Olayemi, Emmanuel Oluwatosin Ojo, and Emmanuel Segun Oguntade. "Time Series Analysis on Reported Cases of Tuberculosis in Minna Niger State Nigeria." Open Journal of Statistics 10, no. 3 (2020): 412-430. [7] Nayak, M. Siva Durga Prasad, and K. A. Narayan. "Forecasting dengue fever incidence using ARIMA analysis." International Journal of Collaborative Research on Internal Medicine & Public Health 11, no. 3 (2019): 924-932. [8] Wu, Wei, Shu-Yi An, Peng Guan, De-Sheng Huang, and Bao-Sen Zhou. "Time series analysis of human brucellosis in mainland China by using Elman and Jordan recurrent neural networks." BMC infectious diseases 19 (2019): 1-11. [9] Ceylan, Zeynep. "Estimation of COVID-19 prevalence in Italy, Spain, and France." Science of The Total Environment 729 (2020): 138817. [10] Bayyurt, Lutfi, and Burcu Bayyurt. "Forecasting of COVID-19 cases and deaths using ARIMA models." medrxiv (2020): 2020-04. [11] Tandon, Hiteshi, Prabhat Ranjan, Tanmoy Chakraborty, and Vandana Suhag. "Coronavirus (COVID-19): ARIMA-based time-series analysis to forecast near future and the effect of school reopening in India." Journal of Health Management 24, no. 3 (2022): 373-388. [12] Perone, Gaetano. "An ARIMA model to forecast the spread and the final size of COVID-2019 epidemic in Italy." MedRxiv (2020): 2020-04. [13] Ghosal, Samit, Sumit Sengupta, Milan Majumder, and Binayak Sinha. "Linear Regression Analysis to predict the number of deaths in India due to SARS-CoV-2 at 6 weeks from day 0 (100 cases-March 14th 2020)." Diabetes & Metabolic Syndrome: Clinical Research & Reviews 14, no. 4 (2020): 311-315. [14] Parbat, Debanjan, and Monisha Chakraborty. "A python based support vector regression model for prediction of COVID19 cases in India." Chaos, Solitons & Fractals 138 (2020): 109942. [15] Naimoli, Antonio. "Modelling the persistence of Covid-19 positivity rate in Italy." Socio-Economic Planning Sciences 82 (2022): 101225. [16] Kibria, Hafsa Binte, Oishi Jyoti, and Abdul Matin. "Forecasting the spread of the third wave of COVID-19 pandemic using time series analysis in Bangladesh." Informatics in medicine unlocked 28 (2022): 100815. [17] Khan, Farhan Mohammad, and Rajiv Gupta. "ARIMA and NAR based prediction model for time series analysis of COVID-19 cases in India." Journal of Safety Science and Resilience 1, no. 1 (2020): 12-18. [18] Chowdhury, Anjir Ahmed, Khandaker Tabin Hasan, and Khadija Kubra Shahjalal Hoque. "Analysis and prediction of COVID-19 pandemic in Bangladesh by using ANFIS and LSTM network." Cognitive Computation 13, no. 3 (2021): 761-770. [19] Alassafi, Madini O., Mutasem Jarrah, and Reem Alotaibi. "Time series predicting of COVID-19 based on deep learning." Neurocomputing 468 (2022): 335-344. [20] Chyon, Fuad Ahmed, Md Nazmul Hasan Suman, Md Rafiul Islam Fahim, and Md Sazol Ahmmed. "Time series analysis and predicting COVID-19 affected patients by ARIMA model using machine learning." Journal of virological methods 301 (2022): 114433. [21] Nair, Siddharth, Ganesan Ckm, R. Varsha, Sankhasubhra Ghosal, M. Vergin, and L. Jani Anbarasi. "Intelligent Forecasting Strategy for COVID-19 Pandemic Trend in India: A Statistical Approach." In Artificial Intelligence and Technologies: Select Proceedings of ICRTAC-AIT 2020, pp. 553-560. Springer Singapore, 2022. [22] Mukhairez, Hosam HA, and Ahmed JI Alaff. "Short-term Forecasting of COVID-19." Computational Intelligence for COVID-19 and Future Pandemics: Emerging Applications and Strategies (2022): 257-266. [23] Prajapati, Samyak, Aman Swaraj, Ronak Lalwani, Akhil Narwal, and Karan Verma. "Comparison of traditional and hybrid time series models for forecasting COVID-19 cases." arXiv preprint arXiv:2105.03266 (2021). [24] Li, Cai, Agyemang Kwasi Sampene, Fredrick Oteng Agyeman, Brenya Robert, and Abraham Lincoln Ayisi. "Forecasting the severity of COVID-19 pandemic amidst the emerging SARS-CoV-2 variants: adoption of ARIMA model." Computational and Mathematical Methods in Medicine 2022 (2022). [25] Yaffee, Robert Alan, and Monnie McGee. An introduction to time series analysis and forecasting: with applications of SAS® and SPSS®. Elsevier, 2000. [26] Fanoodi, Bahareh, Behnam Malmir, and Farzad Firouzi Jahantigh. "Reducing demand uncertainty in the platelet supply chain through artificial neural networks and ARIMA models." Computers in biology and medicine 113 (2019): 103415. [27] Kufel, Tadeusz. "ARIMA-based forecasting of the dynamics of confirmed Covid-19 cases for selected European countries." Equilibrium. Quarterly Journal of Economics and Economic Policy 15, no. 2 (2020): 181-204. [28] Martínez, Francisco, María Pilar Frías, Francisco Charte, and Antonio J. Rivera. "Time Series Forecasting with KNN in R: the tsfknn Package." R J. 11, no. 2 (2019): 229. [29] Assimakopoulos, Vassilis, and Konstantinos Nikolopoulos. "The theta model: a decomposition approach to forecasting." International journal of forecasting 16, no. 4 (2000): 521-530. [30] Cleveland, Robert B., William S. Cleveland, Jean E. McRae, and Irma Terpenning. "STL: A seasonal-trend decomposition." J. Off. Stat 6, no. 1 (1990): 3-73. [31] Hyndman, Rob J., Maxwell L. King, Ivet Pitrun, and Baki Billah. "Local linear forecasts using cubic smoothing splines." Australian & New Zealand Journal of Statistics 47, no. 1 (2005): 87-99. [32] De Livera, Alysha M., Rob J. Hyndman, and Ralph D. Snyder. "Forecasting time series with complex seasonal patterns using exponential smoothing." Journal of the American statistical association 106, no. 496 (2011): 1513-1527. | ||
|
آمار تعداد مشاهده مقاله: 582 تعداد دریافت فایل اصل مقاله: 127 |
||