Estimate the Trend of COVID-19 Outbreak in China: a Statistical and Inferential Analysis on Provincial-level Data

被引:0
|
作者
Li, Kun [1 ]
Zhang, Yangyang [2 ]
Wang, Chao [3 ]
机构
[1] Beijing Normal Univ, Business Sch, Houzhulou Bldg,19 Xinjiekouwai St, Beijing 100875, Peoples R China
[2] Armed Police Beijing Corps Hosp, Dept Pharm, Beijing 100600, Peoples R China
[3] ZCE Futures & Derivat Inst Co LTD, Res Dept, 31 Longhuwaihuan Eest Rd, Zhengzhou 450000, Peoples R China
基金
中国国家自然科学基金;
关键词
COVID-19; Basic Reproduction Numbers; Trend; China; Provincial-level Data; Statistical and Inferential Analysis;
D O I
10.1016/j.procs.2021.04.092
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The ongoing COVID-19 epidemic spreads with strong transmission power in every part of China. Analyses of the trend is highly need when the Chinese government makes plans and policies on epidemic control. This paper provides an estimation process on the trend of COVID-19 outbreak using the provincial-level data of the confirmed cases. On the basis of the previous studies, we introduce an effective and practical method to compute accurate basic reproduction numbers (R(0)s) in each province-level division of China. The statistical results show a non-stop downward trend of the R(0)s in China, and confirm that China has made significant progress on the epidemic control by lowering the provincial R(0)s from 10 or above to 3.21 or less. In the inferential analysis, we introduce an effective AR(n) model for the trend forecasting. The inferential results imply that the nationwide epidemic risk will fall to a safe level by the end of April in China, which matches the actual situation. The results provide more accurate method and information about COVID-19. (C) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the International Conference on Identification, Information and Knowledge in the internet of Things, 2020.
引用
收藏
页码:512 / 517
页数:6
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