Peak prediction and spatial distribution characteristics of COVID-19 in Beijing-Tianjin-Hebei region based on baidu index

XIA Xuecang; LIU Yu; WANG Xiaowan; QIU Wensong; FANG Shaojie; LIU Chao

doi:10.16462/j.cnki.zhjbkz.2023.09.013

Volume 27 Issue 9

Sep. 2023

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Article Navigation > CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION > 2023 > 27(9): 1059-1066

XIA Xuecang, LIU Yu, WANG Xiaowan, QIU Wensong, FANG Shaojie, LIU Chao. Peak prediction and spatial distribution characteristics of COVID-19 in Beijing-Tianjin-Hebei region based on baidu index[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(9): 1059-1066. doi: 10.16462/j.cnki.zhjbkz.2023.09.013

Citation:

XIA Xuecang, LIU Yu, WANG Xiaowan, QIU Wensong, FANG Shaojie, LIU Chao. Peak prediction and spatial distribution characteristics of COVID-19 in Beijing-Tianjin-Hebei region based on baidu index[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(9): 1059-1066. doi: 10.16462/j.cnki.zhjbkz.2023.09.013

Citation:

XIA Xuecang, LIU Yu, WANG Xiaowan, QIU Wensong, FANG Shaojie, LIU Chao. Peak prediction and spatial distribution characteristics of COVID-19 in Beijing-Tianjin-Hebei region based on baidu index[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(9): 1059-1066. doi: 10.16462/j.cnki.zhjbkz.2023.09.013

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Peak prediction and spatial distribution characteristics of COVID-19 in Beijing-Tianjin-Hebei region based on baidu index

doi: 10.16462/j.cnki.zhjbkz.2023.09.013

School of Economics, Department of Statistics, Hebei University, Baoding 071002, China

Funds:

Hebei Social Science Foundation Project HB22TJ001

More Information

Corresponding author: LIU Chao, E-mail: liuchao003003@126.com
Received Date: 2022-12-29
Rev Recd Date: 2023-05-18

Available Online: 2023-10-12

Publish Date: 2023-09-10

Abstract

Abstract

Objective To analyze the progression cycle and spatial clustering of the coronavirus disease 2019(COVID-19)in Beijing-Tianjin-Hebei region from November 11 to December 22, 2022. Methods Based on the daily fever keyword Baidu index search data in each city, the logistic model was used to simulate and predict the development process of this round of COVID-19 infection. The infection progression cycle was divided into an increasing period, a rapid increase period and a slow increase period, and the peak day of infection growth was predicted. At the same time, the global and local spatial autocorrelation analysis of the fever Baidu index search rate was conducted. Results The logistic model simulation and prediction yielded a robust model fitting effect. The epidemic progression speed and stage of COVID-19 infection varied across cities. Shijiazhuang City, Baoding City and Xingtai City were the first to enter the rapid increase stage of infection. Spatial autocorrelation analysis showed that there was a significant global spatial positive correlation in Beijing-Tianjin-Hebei region only for 5 days (Moran′s I: 0.314~0.491, P value was < 0.05), whereas other times exhibited random distribution. Conclusions This round of COVID-19 infection epidemic in Beijing-Tianjin-Hebei region shows an outbreak trend. The epidemic progress stage and infection peak in each city are significantly different, and most of the time there is no significant spatial autocorrelation, which provides a reference for health care configuration.
- COVID-19,
- Baidu index,
- Temporal-spatial analyze

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