气候变化领域集成服务门户(CCPortal): Big data assimilation to improve the predictability of COVID-19

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DOI	10.1016/j.geosus.2020.11.005
	Big data assimilation to improve the predictability of COVID-19
	Li, Xin; Zhao, Zebin; Liu, Feng
通讯作者	Li, X (通讯作者)
发表日期	2020
ISSN	2096-7438
EISSN	2666-6839
起始页码	317
结束页码	320
卷号	1 期号:4
英文摘要	The global outbreak of COVID-19 requires us to accurately predict the spread of disease and decide how adopting corresponding strategies to ensure the sustainable development. Most of the existing infectious disease forecasting methods are based on the classical Susceptible-Infectious-Removed (SIR) model. However, due to the highly nonlinearity, nonstationarity, sensitivities to initial values and parameters, SIR type models would produce large deviations in the forecast results. Here, we propose a framework of using the Markov Chain Monte Carlo method to estimate the model parameters, and then the data assimilation based on the Ensemble Kalman Filter to update model trajectory by cooperating with the real time confirmed cases, so as to improve the predictability of the pandemic. Based on this framework, we have developed a global COVID-19 real time forecasting system. Moreover, we suggest that big data associated with the spatiotemporally heterogeneous pathological characteristics, and social environment in different countries should be assimilated to further improve the COVID-19 predictability. It is hoped that the accurate prediction of COVID-19 will contribute to the adjustments of prevention and control strategies to contain the pandemic, and help achieve the SDG goal of Good Health and Well-Being.
关键词	MODEL
英文关键词	COVID-19; Data assimilation; Big data; Prediction; Sustainable development; SDG
语种	英语
WOS研究方向	Science & Technology - Other Topics ; Physical Geography
WOS类目	Green & Sustainable Science & Technology ; Geography, Physical
WOS记录号	WOS:000646628200008
来源期刊	GEOGRAPHY AND SUSTAINABILITY
来源机构	中国科学院青藏高原研究所
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/259705
推荐引用方式 GB/T 7714	Li, Xin,Zhao, Zebin,Liu, Feng. Big data assimilation to improve the predictability of COVID-19[J]. 中国科学院青藏高原研究所,2020,1(4).
APA	Li, Xin,Zhao, Zebin,&Liu, Feng.(2020).Big data assimilation to improve the predictability of COVID-19.GEOGRAPHY AND SUSTAINABILITY,1(4).
MLA	Li, Xin,et al."Big data assimilation to improve the predictability of COVID-19".GEOGRAPHY AND SUSTAINABILITY 1.4(2020).