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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 | ||
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آمار تعداد مشاهده مقاله: 831 تعداد دریافت فایل اصل مقاله: 283 |
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