Prediction of the confirmed cases and deaths of global COVID-19 using artificial intelligence
Guo, Qingchun1,2; He, Zhenfang1,3
通讯作者Guo, Qingchun(guoqingchun@lcu.edu.cn) ; He, Zhenfang(hezhenfang@lcu.edu.cn)
2021-01-07
发表期刊ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
ISSN0944-1344
页码11
摘要The outbreak of coronavirus disease 2019 (COVID-19) has seriously affected the environment, ecology, economy, society, and human health. With the global epidemic dynamics becoming more and more serious, the prediction and analysis of the confirmed cases and deaths of COVID-19 has become an important task. We develop an artificial neural network (ANN) for modeling of the confirmed cases and deaths of COVID-19. The confirmed cases and deaths data are collected from January 20 to November 11, 2020 by the World Health Organization (WHO). By introducing root mean square error (RMSE), correlation coefficient (R), and mean absolute error (MAE), statistical indicators of the prediction model are verified and evaluated. The size of training and test confirmed cases and death base employed in the model is optimized. The best simulating performance with RMSE, R, and MAE is realized using the 7 past days' cases as input variables in the training and test dataset. And the estimated R are 0.9948 and 0.9683, respectively. Compared with different algorithms, experimental simulation shows that trainbr algorithm has better performance than other algorithms in reproducing the amount of the confirmed cases and deaths. This study shows that the ANN model is suitable for predicting the confirmed cases and deaths of COVID-19 in the future. Using the ANN model, we also predict the confirmed cases and deaths of COVID-19 from June 5, 2020 to November 11, 2020. During the predicting period, the R, RMSE, and MAE for new infected confirmed cases of COVID-19 are 0.9848, 17,554, and 12,229, respectively; the R, RMSE, and MAE for new confirmed deaths of COVID-19 are 0.8593, 631.8, and 463.7, respectively. The predicted confirmed cases and deaths of COVID-19 are very close to the actual confirmed cases and deaths. The results show that continuous and strict control measures should be taken to prevent the further spread of the epidemic.
关键词SARS-CoV-2 COVID-19 Epidemic Infected cases Deaths Artificial intelligence
DOI10.1007/s11356-020-11930-6
关键词[WOS]NEURAL-NETWORKS ; CORONAVIRUS ; CHINA
收录类别SCI ; SCI
语种英语
资助项目National Natural Science Fund of China[41572150] ; National Natural Science Fund of China[41472162] ; National Natural Science Fund of China[41702373] ; Shandong Social Sciences Planning Research Fund[18CKPJ34] ; Shandong Province Higher Educational Humanities and Social Science Fund[J18RA196] ; State Key Laboratory of Loess and Quaternary Geology Found[SKLLQG1907]
WOS研究方向Environmental Sciences & Ecology
项目资助者National Natural Science Fund of China ; Shandong Social Sciences Planning Research Fund ; Shandong Province Higher Educational Humanities and Social Science Fund ; State Key Laboratory of Loess and Quaternary Geology Found
WOS类目Environmental Sciences
WOS记录号WOS:000607363600019
出版者SPRINGER HEIDELBERG
引用统计
被引频次:35[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ieecas.cn/handle/361006/15843
专题黄土与第四纪地质国家重点实验室(2010~)
通讯作者Guo, Qingchun; He, Zhenfang
作者单位1.Liaocheng Univ, Sch Environm & Planning, Liaocheng 252000, Shandong, Peoples R China
2.Chinese Acad Sci, Inst Earth Environm, State Key Lab Loess & Quaternary Geol, Xian 710061, Peoples R China
3.Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Urban & Reg Ecol, Beijing 100085, Peoples R China
推荐引用方式
GB/T 7714
Guo, Qingchun,He, Zhenfang. Prediction of the confirmed cases and deaths of global COVID-19 using artificial intelligence[J]. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH,2021:11.
APA Guo, Qingchun,&He, Zhenfang.(2021).Prediction of the confirmed cases and deaths of global COVID-19 using artificial intelligence.ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH,11.
MLA Guo, Qingchun,et al."Prediction of the confirmed cases and deaths of global COVID-19 using artificial intelligence".ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH (2021):11.
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Guo, Qingchun]的文章
[He, Zhenfang]的文章
百度学术
百度学术中相似的文章
[Guo, Qingchun]的文章
[He, Zhenfang]的文章
必应学术
必应学术中相似的文章
[Guo, Qingchun]的文章
[He, Zhenfang]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。