Intelligent computing on time-series data analysis and prediction of COVID-19 pandemics.
Intelligent computing on time-series data analysis and prediction of COVID-19 pandemics.
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DOI：
10.1016/j.patrec.2021.07.027
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发表时间：
2021-11
影响因子：
5.1
通讯作者：
Pani SK
中科院分区：
文献类型：
--
作者：
Dash S;Chakraborty C;Giri SK;Pani SK
由新型冠状病毒(SARS-CoV-2)引起的新冠肺炎病是2019年12月下旬在湖北省武汉市由中国引发的一种高传染性疫情。世界卫生组织于2020年3月12日宣布新冠肺炎为大流行。研究人员和政策制定者正在设计控制疫情的策略，以便将其对人类健康和经济的影响全天候降至最低。SARS-CoV-2病毒主要通过呼吸道飞沫和人体受污染的表面传播。在疫情大流行期间确保适当水平的安全是一个非常棘手的问题，因为交通运输行业受到了新冠肺炎的重创。本文致力于开发一个用于新冠肺炎爆发预测的智能计算模型。脸书预言家模型预测未来90天的数值，包括新冠肺炎确诊病例的高峰期，覆盖全球6个疫情最严重的国家，包括印度和印度的6个高发邦。该模型还确定了印度确诊病例增长曲线上的五个重大转折点，表明印度政府采取的干预措施对感染增长率的影响。该模型的拟合优度测量了印度所有六个国家和六个邦85%的MAPE。上述计算分析或许能够为医疗系统和基础设施的规划和管理提供一些启示。
Covid-19 disease caused by novel coronavirus (SARS-CoV-2) is a highly contagious epidemic that originated in Wuhan, Hubei Province of China in late December 2019. World Health Organization (WHO) declared Covid-19 as a pandemic on 12th March 2020. Researchers and policy makers are designing strategies to control the pandemic in order to minimize its impact on human health and economy round the clock. The SARS-CoV-2 virus transmits mostly through respiratory droplets and through contaminated surfacesin human body.Securing an appropriate level of safety during the pandemic situation is a highly problematic issue which resulted from the transportation sector which has been hit hard by COVID-19. This paper focuses on developing an intelligent computing model for forecasting the outbreak of COVID-19. The Facebook Prophet model predicts 90 days future values including the peak date of the confirmed cases of COVID-19 for six worst hit countries of the world including India and six high incidence states of India. The model also identifies five significant changepoints in the growth curve of confirmed cases of India which indicate the impact of the interventions imposed by Government of India on the growth rate of the infection. The goodness-of-fit of the model measures 85% MAPE for all six countries and all six states of India. The above computational analysis may be able to throw some light on planning and management of healthcare system and infrastructure.
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影响因子：
6.7
作者：
Yang, Qiuying;Wang, Jie;Wang, Xihao
通讯作者：
Wang, Xihao
DOI：
10.1007/s12262-021-02962-4
发表时间：
2022-04
期刊：
The Indian journal of surgery
影响因子：
--
作者：
Dash S;Samadder S;Srivastava A;Meena R;Ranjan P
通讯作者：
Ranjan P
影响因子：
3.4
作者：
Ludwig, Nicole;Feuerriegel, Stefan;Neumann, Dirk
通讯作者：
Neumann, Dirk
影响因子：
3.8
作者：
Purcaro G;Rees CA;Wieland-Alter WF;Schneider MJ;Wang X;Stefanuto PH;Wright PF;Enelow RI;Hill JE
通讯作者：
Hill JE
影响因子：
82.9
作者：
Ye, QH;Qin, LX;Wang, XW
通讯作者：
Wang, XW