2019冠状病毒病群地理位置预测

IF 0.6 Q3 MULTIDISCIPLINARY SCIENCES
Divyansh Agarwal, Nishita Patnaik, Aravind Harinarayanan, S. Senthilkumar, Brindha Krishnamurthy, Kathiravan Srinivasan
{"title":"2019冠状病毒病群地理位置预测","authors":"Divyansh Agarwal, Nishita Patnaik, Aravind Harinarayanan, S. Senthilkumar, Brindha Krishnamurthy, Kathiravan Srinivasan","doi":"10.47836/pjst.31.4.23","DOIUrl":null,"url":null,"abstract":"Thanks to the growth in data storage capacity, nowadays, researchers can use years’ worth of mathematical models and depend on past datasets. A pattern of all pandemics can be identified through the assistance of Machine Learning. The movement of the COVID-19 herd and any future pandemic can be predicted. These predictions will vary based on the dataset, but it will allow the preparation beforehand and stop the spreading of COVID-19. This study focuses on developing Spatio-temporal models using Machine Learning to produce a predictive visualized heat regional map of COVID-19 worldwide. Different models of Machine Learning are compared using John Hopkins University dataset. This study has compared well-known basic models like Support Vector Machine (SVM), Prophet, Bayesian Ridge Regression, and Polynomial Regression. Based on the comparison of various metrics of the Support Vector Machine, Polynomial Regression Model was found to be better and hence can be assumed to give good results for long-term prediction. On the other hand, ARIMA, Prophet Model, and Bayesian Ridge Reduction models are good for short-term predictions. The metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE) are better for Support Vector Machines compared to other models. The metrics such as R2 Score and Adjusted R-Square are better for the polynomial Regression model.","PeriodicalId":46234,"journal":{"name":"Pertanika Journal of Science and Technology","volume":"52 1","pages":""},"PeriodicalIF":0.6000,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Forecasting Geo Location of COVID-19 Herd\",\"authors\":\"Divyansh Agarwal, Nishita Patnaik, Aravind Harinarayanan, S. Senthilkumar, Brindha Krishnamurthy, Kathiravan Srinivasan\",\"doi\":\"10.47836/pjst.31.4.23\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Thanks to the growth in data storage capacity, nowadays, researchers can use years’ worth of mathematical models and depend on past datasets. A pattern of all pandemics can be identified through the assistance of Machine Learning. The movement of the COVID-19 herd and any future pandemic can be predicted. These predictions will vary based on the dataset, but it will allow the preparation beforehand and stop the spreading of COVID-19. This study focuses on developing Spatio-temporal models using Machine Learning to produce a predictive visualized heat regional map of COVID-19 worldwide. Different models of Machine Learning are compared using John Hopkins University dataset. This study has compared well-known basic models like Support Vector Machine (SVM), Prophet, Bayesian Ridge Regression, and Polynomial Regression. Based on the comparison of various metrics of the Support Vector Machine, Polynomial Regression Model was found to be better and hence can be assumed to give good results for long-term prediction. On the other hand, ARIMA, Prophet Model, and Bayesian Ridge Reduction models are good for short-term predictions. The metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE) are better for Support Vector Machines compared to other models. The metrics such as R2 Score and Adjusted R-Square are better for the polynomial Regression model.\",\"PeriodicalId\":46234,\"journal\":{\"name\":\"Pertanika Journal of Science and Technology\",\"volume\":\"52 1\",\"pages\":\"\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2023-06-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Pertanika Journal of Science and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.47836/pjst.31.4.23\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pertanika Journal of Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.47836/pjst.31.4.23","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

由于数据存储容量的增长,如今,研究人员可以使用多年的数学模型,并依赖于过去的数据集。通过机器学习的帮助,可以确定所有流行病的模式。COVID-19畜群的移动和任何未来的大流行都可以预测。这些预测将根据数据集而有所不同,但它将允许提前准备并阻止COVID-19的传播。本研究的重点是利用机器学习开发时空模型,以生成COVID-19全球预测可视化热区域图。使用约翰霍普金斯大学的数据集对不同的机器学习模型进行了比较。本研究比较了支持向量机(SVM)、先知回归(Prophet)、贝叶斯岭回归(Bayesian Ridge Regression)和多项式回归(Polynomial Regression)等知名的基本模型。通过对支持向量机各指标的比较,发现多项式回归模型效果更好,因此可以假设多项式回归模型对长期预测的效果较好。另一方面,ARIMA、Prophet Model和Bayesian Ridge Reduction模型对短期预测效果较好。与其他模型相比,平均绝对误差(MAE)、均方误差(MSE)和均方根误差(RMSE)等指标更适合支持向量机。R2 Score和Adjusted R-Square等指标更适合多项式回归模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Forecasting Geo Location of COVID-19 Herd
Thanks to the growth in data storage capacity, nowadays, researchers can use years’ worth of mathematical models and depend on past datasets. A pattern of all pandemics can be identified through the assistance of Machine Learning. The movement of the COVID-19 herd and any future pandemic can be predicted. These predictions will vary based on the dataset, but it will allow the preparation beforehand and stop the spreading of COVID-19. This study focuses on developing Spatio-temporal models using Machine Learning to produce a predictive visualized heat regional map of COVID-19 worldwide. Different models of Machine Learning are compared using John Hopkins University dataset. This study has compared well-known basic models like Support Vector Machine (SVM), Prophet, Bayesian Ridge Regression, and Polynomial Regression. Based on the comparison of various metrics of the Support Vector Machine, Polynomial Regression Model was found to be better and hence can be assumed to give good results for long-term prediction. On the other hand, ARIMA, Prophet Model, and Bayesian Ridge Reduction models are good for short-term predictions. The metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE) are better for Support Vector Machines compared to other models. The metrics such as R2 Score and Adjusted R-Square are better for the polynomial Regression model.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Pertanika Journal of Science and Technology
Pertanika Journal of Science and Technology MULTIDISCIPLINARY SCIENCES-
CiteScore
1.50
自引率
16.70%
发文量
178
期刊介绍: Pertanika Journal of Science and Technology aims to provide a forum for high quality research related to science and engineering research. Areas relevant to the scope of the journal include: bioinformatics, bioscience, biotechnology and bio-molecular sciences, chemistry, computer science, ecology, engineering, engineering design, environmental control and management, mathematics and statistics, medicine and health sciences, nanotechnology, physics, safety and emergency management, and related fields of study.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信