Prediction of COVID-19 spread by sliding mSEIR observer
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. RESEARCH PAPER .
December 2020, Vol. 63 222203:1–222203:13 https://doi.org/10.1007/s11432-020-3034-y
Prediction of COVID-19 spread by sliding mSEIR observer Duxin CHEN1† , Yifan YANG1† , Yifan ZHANG2 & Wenwu YU1* 1
Jiangsu Key Laboratory of Networked Collective Intelligence, School of Mathematics, Southeast University, Nanjing 210096, China; 2 School of Information Science and Engineering, Southeast University, Nanjing 210096, China Received 12 March 2020/Revised 8 June 2020/Accepted 1 August 2020/Published online 12 November 2020
Abstract The outbreak of COVID-19 has brought unprecedented challenges not only in China but also in the whole world. Thousands of people have lost their lives, and the social operating system has been affected seriously. Thus, it is urgent to study the determinants of the virus and the health conditions in specific populations and to reveal the strategies and measures in preventing the epidemic spread. In this study, we first adopt the long short-term memory algorithm to predict the infected population in China. However, it gives no interpretation of the dynamics of the spread process. Also the long-term prediction error is too large to be accepted. Thus, we introduce the susceptible-exposed-infected-removed (SEIR) model and further the metapopulation SEIR (mSEIR) model to capture the spread process of COVID-19. By using a sliding window algorithm, we suggest that the parameter estimation and the prediction of the SEIR populations are well performed. In addition, we conduct extensive numerical experiments to show the trend of the infected population for several provinces. The results may provide some insight into the research of epidemics and the understanding of the spread of the current COVID-19. Keywords
epidemic spread, prediction, sliding window algorithm, COVID-19
Citation Chen D X, Yang Y F, Zhang Y F, et al. Prediction of COVID-19 spread by sliding mSEIR observer. Sci China Inf Sci, 2020, 63(12): 222203, https://doi.org/10.1007/s11432-020-3034-y
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Introduction
Human society has been severely influenced by epidemic outbreaks many times in history. The spread of the epidemic is full of accidental factors, which is a complex dynamical phenomenon [1, 2]. Infectious and infected individuals are all proactive individuals whose behaviors have a serious impact on the development of the disease, and they also adapt to changes in the external situation. This adds more complexity to the research on epidemic spreading dynamics and the prediction [3, 4]. With the rapid development of modern transportation and communication tools, the human activity radius has grown rapidly, and the contact between any two individuals has become possible. However, the development of transportation and communication tools has brought convenience yet also brought certain disasters. On the one hand, the small-world network feature of our living society makes the radius of the spread of diseases or rumors bigger and bigger, which makes it easier to spread diseases [5,6]. On the other hand, the real soci
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