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ORIGINAL RESEARCH article
Front. Public Health
Sec. Digital Public Health
Volume 12 - 2024 |
doi: 10.3389/fpubh.2024.1442728
This article is part of the Research Topic Health Literacy and Digital Health Literacy among Older Adults: Public Health Interventions View all articles
Multi-dimensional Epidemiology and Informatics Data on COVID-19 Wave at the End of Zero COVID Policy in China
Provisionally accepted- 1 Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, Shantou, Guangdong Province, China
- 2 College of Medicine, Shantou University, Shantou, Guangdong Province, China
- 3 Hong Kong Polytechnic University, Kowloon, Hong Kong, SAR China
- 4 Shantou University, Shantou, Guangdong Province, China
- 5 Queensland University of Technology, Brisbane, Queensland, Australia
- 6 First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong Province, China
- 7 Other, Shantou City, China
- 8 Other, Chaozhou City, China
- 9 The First People’s Hospital of Yulin, Yulin, Shaanxi Province, China
- 10 Guangdong Hybribio Biotech Co., Ltd, Chaozhou, Guangdong, China
- 11 Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
- 12 Jinping District People's Hospital, Shantou City, China
- 13 Zhengzhou Second Hospital, Zhengzhou, China
- 14 Medical College, Shaoguan University, Shaoguan, Guangdong Province, China
- 15 Centre for Infectious Disease Epidemiology and Research, Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- 16 National Centre for Infectious Diseases, Singapore, Singapore
- 17 School of Medicine, Tsinghua University, Beijing, Beijing Municipality, China
Background: China exited strict Zero-COVID policy with a surge in Omicron variant infections in December 2022. Given China's pandemic policy and population immunity, employing Baidu Index (BDI) to analyze the evolving disease landscape and estimate the nationwide pneumonia hospitalizations in the post Zero COVID period, validated by hospital data, holds informative potential for future outbreaks. Methods: Retrospective observational analyses were conducted at the conclusion of the Zero-COVID policy, integrating internet search data alongside offline records. Methodologies employed were multidimensional, encompassing lagged Spearman correlation analysis, growth rate assessments, independent sample T-tests, Granger causality examinations, and Bayesian structural time series (BSTS) models for comprehensive data scrutiny.Results: Various diseases exhibited a notable upsurge in the BDI after the policy change , consistent with the broader trajectory of the COVID-19 pandemic. Robust connections emerged between COVID-19 and diverse health conditions, predominantly impacting the respiratory, circulatory, ophthalmological, and neurological domains. Notably, 34 diseases displayed a relatively high correlation (r>0.5) with COVID-19. Among these, 12 exhibited a growth rate exceeding 50% postpolicy transition, with myocarditis escalating by 1708% and pneumonia by 1332%.In these 34 diseases, causal relationships have been confirmed for 23 of them, while 28 garnered validation from hospital-based evidence. Notably, 19 diseases obtained concurrent validation from both Granger causality and hospital-based data. Finally, the BSTS models approximated approximately 4,332,655 inpatients diagnosed with pneumonia nationwide during the two months subsequent to the policy relaxation.This investigation elucidated substantial associations between COVID-19 and respiratory, circulatory, ophthalmological, and neurological disorders. The outcomes from comprehensive multi-dimensional cross-over studies notably augmented the robustness of our comprehension of COVID-19's disease spectrum, advocating for the prospective utility of internetderived data. Our research highlights the potential of Internet behavior in predicting pandemic-related syndromes, emphasizing its importance for public health strategies, resource allocation, and preparedness for future outbreaks.
Keywords: COVID-19, zero-COVID policy, Baidu search index, Granger causality test, Bayesian structural time series
Received: 02 Jun 2024; Accepted: 30 Jul 2024.
Copyright: © 2024 Yu, TAN, Tang, Zhao, Ji, Lin, He, Gu, Liang, Wang, Yequn, Yang, Xie, Wang, Liu, He, Chen, Wang, Wu, Zhao, Liu, Wang, Hao, Cen, Yao, Zhang, Liu, Lye, Hao, Wong and Cen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Xin-shen Yu, Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, Shantou, Guangdong Province, China
Wanting Tang, Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, Shantou, Guangdong Province, China
Fang-fang Zhao, Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, Shantou, Guangdong Province, China
Youxin Gu, Queensland University of Technology, Brisbane, 4001, Queensland, Australia
Jia-Jian Liang, Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, Shantou, Guangdong Province, China
Meng Wang, Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, Shantou, Guangdong Province, China
Jiancheng Yang, Other, Shantou City, China
Longxu Xie, Other, Chaozhou City, China
Qian Wang, The First People’s Hospital of Yulin, Yulin, Shaanxi Province, China
Mengyu Liu, College of Medicine, Shantou University, Shantou, 515041, Guangdong Province, China
Yang He, Guangdong Hybribio Biotech Co., Ltd, Chaozhou, Guangdong, China
Lan Chen, Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, Shantou, Guangdong Province, China
Zhaoxiong Wu, Jinping District People's Hospital, Shantou City, China
Gang Zhao, Zhengzhou Second Hospital, Zhengzhou, China
Yi Liu, Zhengzhou Second Hospital, Zhengzhou, China
Yun Wang, Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, Shantou, Guangdong Province, China
Dongning Hao, The First People’s Hospital of Yulin, Yulin, Shaanxi Province, China
Jingyun Cen, Medical College, Shaoguan University, Shaoguan, 512026, Guangdong Province, China
Shi-Qi Yao, Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, Shantou, Guangdong Province, China
Dan Zhang, Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, Shantou, Guangdong Province, China
Lifang Liu, Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, Shantou, Guangdong Province, China
David Chien Lye, Centre for Infectious Disease Epidemiology and Research, Saw Swee Hock School of Public Health, National University of Singapore, Singapore, 117549, Singapore
Zhifeng Hao, Shantou University, Shantou, 515063, Guangdong Province, China
Tien Yin Wong, School of Medicine, Tsinghua University, Beijing, 100084, Beijing Municipality, China
Ling-Ping Cen, Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, Shantou, Guangdong Province, China
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