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A Prediction Framework for Turning Period Structures in COVID-19 Epidemic and Its Application to Practical Emergency Risk Management

Di Lan ¹ Gu Yudi ² ^* Qian Guoqi ³ Yuan George Xianzhi ^4,5

文摘	The aim of this paper is first to establish a general prediction framework for turning (period) term structures in COVID-19 epidemic related to the implementation of emergency risk management in the practice,which allows us to conduct the reliable estimation for the peak period based on the new concept of "Turning Period" (instead of the traditional one with the focus on "Turning Point") for infectious disease spreading such as the COVID-19 epidemic appeared early in year 2020.By a fact that emergency risk management is necessarily to implement emergency plans quickly,the identification of the Turning Period is a key element to emergency planning as it needs to provide a time line for effective actions and solutions to combat a pandemic by reducing as much unexpected risk as soon as possible.As applications,the paper also discusses how this "Turning Term (Period) Structure" is used to predict the peak phase for COVID-19 epidemic in Wuhan from January/2020 to early March/2020.Our study shows that the predication framework established in this paper is capable to provide the trajectory of COVID-19 cases dynamics for a few weeks starting from Feb.10/2020 to early March/2020,from which we successfully predicted that the turning period of COVID-19 epidemic in Wuhan would arrive within one week after Feb.14/2020,as verified by the true observation in the practice.The method established in this paper for the prediction of "Turning Term (Period) Structures" by applying COVID-19 epidemic in China happened early 2020 seems timely and accurate,providing adequate time for the government,hospitals,essential industry sectors and services to meet peak demands and to prepare aftermath planning,and associated criteria for the Turning Term Structure of COVID-19 epidemic is expected to be a useful and powerful tool to implement the so-called "dynamic zero-COVID-19 policy" ongoing basis in the practice.
来源	Journal of Systems Science and Information ,2022,10(4):309-337 【核心库】
DOI	10.21078/JSSI-2022-309-29
关键词	prediction framework ; turning period structure ; turing phase ; COVID-19 epidemic ; emergency risk management ; emergency plan ; Delta and Gamma ; iSEIR ; spatio-temporal model ; supersaturation phenomenon ; multiplex network ; dynamic zero-COVID-19 policy
地址	1. School of Artificial Intelligence and Computer Science,Jiangnan University, Wuxi, 214122 2. Center of Information Construct and Management,Jiangnan University, Wuxi, 214122 3. School of Mathematics & Statistics,University of Melbourne, Australia, Melbourne, VIC 3010 4. Business School,East China University of Science and Technology, Shanghai, 200237 5. Business School,Chengdu University,Chengdu 610106,China,Business School,Sun Yat-sen University, Guangzhou, 510275
语种	英文
文献类型	研究性论文
ISSN	1478-9906
学科	预防医学、卫生学
基金	国家自然科学基金
文献收藏号	CSCD:7322010

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