Temporary impact on medical system and effectiveness of mitigation strategies after COVID-19 policy adjustment in China: a modeling study

Chitin Hon; Chitin Hon; Chitin Hon; Jingyi Liang; Jingyi Liang; Ruihan Chen; Ruihan Chen; Zhijie Lin; Zhijie Lin; Yangqianxi Wang; Yangqianxi Wang; Wei He; Wei He; Ruibin Liu; Ruibin Liu; Jiaxi Sun; Qianyin Li; Lixi Liang; Minyi Zhang; Zichen Chang; Yinqiu Guo; Wenting Zeng; Tie Liu; Arlindo L. Oliveira; Arlindo L. Oliveira; Arlindo L. Oliveira

doi:10.3389/fpubh.2023.1259084

Frontiers in Public Health (Dec 2023)

Temporary impact on medical system and effectiveness of mitigation strategies after COVID-19 policy adjustment in China: a modeling study

Chitin Hon,
Chitin Hon,
Chitin Hon,
Jingyi Liang,
Jingyi Liang,
Ruihan Chen,
Ruihan Chen,
Zhijie Lin,
Zhijie Lin,
Yangqianxi Wang,
Yangqianxi Wang,
Wei He,
Wei He,
Ruibin Liu,
Ruibin Liu,
Jiaxi Sun,
Qianyin Li,
Lixi Liang,
Minyi Zhang,
Zichen Chang,
Yinqiu Guo,
Wenting Zeng,
Tie Liu,
Arlindo L. Oliveira,
Arlindo L. Oliveira,
Arlindo L. Oliveira

Affiliations

Chitin Hon: Department of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macao SAR, China
Chitin Hon: Guangzhou Laboratory, Guangzhou, Guangdong, China
Chitin Hon: Respiratory Disease AI Laboratory on Epidemic and Medical Big Data Instrument Applications, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, China
Jingyi Liang: Department of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macao SAR, China
Jingyi Liang: Respiratory Disease AI Laboratory on Epidemic and Medical Big Data Instrument Applications, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, China
Ruihan Chen: Department of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macao SAR, China
Ruihan Chen: Respiratory Disease AI Laboratory on Epidemic and Medical Big Data Instrument Applications, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, China
Zhijie Lin: Department of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macao SAR, China
Zhijie Lin: Respiratory Disease AI Laboratory on Epidemic and Medical Big Data Instrument Applications, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, China
Yangqianxi Wang: Department of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macao SAR, China
Yangqianxi Wang: Respiratory Disease AI Laboratory on Epidemic and Medical Big Data Instrument Applications, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, China
Wei He: Department of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macao SAR, China
Wei He: Respiratory Disease AI Laboratory on Epidemic and Medical Big Data Instrument Applications, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, China
Ruibin Liu: Department of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macao SAR, China
Ruibin Liu: Respiratory Disease AI Laboratory on Epidemic and Medical Big Data Instrument Applications, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, China
Jiaxi Sun: Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, China
Qianyin Li: State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
Lixi Liang: State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
Minyi Zhang: Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, China
Zichen Chang: Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, China
Yinqiu Guo: Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, China
Wenting Zeng: Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, China
Tie Liu: Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, China
Arlindo L. Oliveira: Respiratory Disease AI Laboratory on Epidemic and Medical Big Data Instrument Applications, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, China
Arlindo L. Oliveira: Instituto de Engenharia de Sistemas e Computadores: Investigação e Desenvolvimento em Lisboa, Lisboa, Portugal
Arlindo L. Oliveira: Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal

DOI: https://doi.org/10.3389/fpubh.2023.1259084
Journal volume & issue: Vol. 11

Abstract

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BackgroundAs China amends its “zero COVID” strategy, a sudden increase in the number of infections may overwhelm medical resources and its impact has not been quantified. Specific mitigation strategies are needed to minimize disruption to the healthcare system and to prepare for the next possible epidemic in advance.MethodWe develop a stochastic compartmental model to project the burden on the medical system (that is, the number of fever clinic visits and admission beds) of China after adjustment to COVID-19 policy, which considers the epidemiological characteristics of the Omicron variant, age composition of the population, and vaccine effectiveness against infection and severe COVD-19. We also estimate the effect of four-dose vaccinations (heterologous and homologous), antipyretic drug supply, non-pharmacological interventions (NPIs), and triage treatment on mitigating the domestic infection peak.ResultAs to the impact on the medical system, this epidemic is projected to result in 398.02 million fever clinic visits and 16.58 million hospitalizations, and the disruption period on the healthcare system is 18 and 30 days, respectively. Antipyretic drug supply and booster vaccination could reduce the burden on emergency visits and hospitalization, respectively, while neither of them could not reduce to the current capacity. The synergy of several different strategies suggests that increasing the heterologous booster vaccination rate for older adult to over 90% is a key measure to alleviate the bed burden for respiratory diseases on the basis of expanded healthcare resource allocation.ConclusionThe Omicron epidemic followed the adjustment to COVID-19 policy overloading many local health systems across the country at the end of 2022. The combined effect of vaccination, antipyretic drug supply, triage treatment, and PHSMs could prevent overwhelming medical resources.

Published in Frontiers in Public Health

ISSN: 2296-2565 (Online)
Publisher: Frontiers Media S.A.
Country of publisher: Switzerland
LCC subjects: Medicine: Public aspects of medicine
Website: https://www.frontiersin.org/journals/public-health

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