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Kong L, Duan M, Shi J, Hong J, Zhou X, Yang X, Zhao Z, Huang J, Chen X, Yin Y, Li K, Liu Y, Liu J, Wang X, Zhang P, Xie X, Li F, Chang Z, Zhang Z. Optimization of COVID-19 prevention and control measures during the Beijing 2022 Winter Olympics: a model-based study. Infect Dis Poverty 2022; 11:95. [PMID: 36068625 PMCID: PMC9447360 DOI: 10.1186/s40249-022-01019-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 08/24/2022] [Indexed: 11/10/2022] Open
Abstract
Background The continuous mutation of severe acute respiratory syndrome coronavirus 2 has made the coronavirus disease 2019 (COVID-19) pandemic complicated to predict and posed a severe challenge to the Beijing 2022 Winter Olympics and Winter Paralympics held in February and March 2022. Methods During the preparations for the Beijing 2022 Winter Olympics, we established a dynamic model with pulse detection and isolation effect to evaluate the effect of epidemic prevention and control measures such as entry policies, contact reduction, nucleic acid testing, tracking, isolation, and health monitoring in a closed-loop management environment, by simulating the transmission dynamics in assumed scenarios. We also compared the importance of each parameter in the combination of intervention measures through sensitivity analysis. Results At the assumed baseline levels, the peak of the epidemic reached on the 57th day. During the simulation period (100 days), 13,382 people infected COVID-19. The mean and peak values of hospitalized cases were 2650 and 6746, respectively. The simulation and sensitivity analysis showed that: (1) the most important measures to stop COVID-19 transmission during the event were daily nucleic acid testing, reducing contact among people, and daily health monitoring, with cumulative infections at 0.04%, 0.14%, and 14.92% of baseline levels, respectively (2) strictly implementing the entry policy and reducing the number of cases entering the closed-loop system could delay the peak of the epidemic by 9 days and provide time for medical resources to be mobilized; (3) the risk of environmental transmission was low. Conclusions Comprehensive measures under certain scenarios such as reducing contact, nucleic acid testing, health monitoring, and timely tracking and isolation could effectively prevent virus transmission. Our research results provided an important reference for formulating prevention and control measures during the Winter Olympics, and no epidemic spread in the closed-loop during the games indirectly proved the rationality of our research results. Graphical Abstract ![]()
Supplementary Information The online version contains supplementary material available at 10.1186/s40249-022-01019-2.
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Affiliation(s)
- Lingcai Kong
- Department of Mathematics and Physics, North China Electric Power University, Baoding, 071003, China.,Hebei Key Laboratory of Physics and Energy Technology, North China Electric Power University, Baoding, 071003, China
| | - Mengwei Duan
- Department of Mathematics and Physics, North China Electric Power University, Baoding, 071003, China.,Hebei Key Laboratory of Physics and Energy Technology, North China Electric Power University, Baoding, 071003, China
| | - Jin Shi
- Department of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China
| | - Jie Hong
- Department of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China
| | - Xuan Zhou
- Department of Mathematics and Physics, North China Electric Power University, Baoding, 071003, China.,Hebei Key Laboratory of Physics and Energy Technology, North China Electric Power University, Baoding, 071003, China
| | - Xinyi Yang
- Department of Mathematics and Physics, North China Electric Power University, Baoding, 071003, China.,Hebei Key Laboratory of Physics and Energy Technology, North China Electric Power University, Baoding, 071003, China
| | - Zheng Zhao
- Department of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China
| | - Jiaqi Huang
- Department of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China
| | - Xi Chen
- Department of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China
| | - Yun Yin
- Department of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China
| | - Ke Li
- Department of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China
| | - Yuanhua Liu
- Department of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China
| | - Jinggang Liu
- Department of Mathematics and Physics, North China Electric Power University, Baoding, 071003, China.,Hebei Key Laboratory of Physics and Energy Technology, North China Electric Power University, Baoding, 071003, China
| | - Xiaozhe Wang
- Department of Mathematics and Physics, North China Electric Power University, Baoding, 071003, China.,Hebei Key Laboratory of Physics and Energy Technology, North China Electric Power University, Baoding, 071003, China
| | - Po Zhang
- Department of Mathematics and Physics, North China Electric Power University, Baoding, 071003, China.,Hebei Key Laboratory of Physics and Energy Technology, North China Electric Power University, Baoding, 071003, China
| | - Xiyang Xie
- Department of Mathematics and Physics, North China Electric Power University, Baoding, 071003, China.,Hebei Key Laboratory of Physics and Energy Technology, North China Electric Power University, Baoding, 071003, China
| | - Fei Li
- Department of Power Engineering, North China Electric Power University, Baoding, 071003, China
| | - Zhaorui Chang
- Division of Infectious Disease, Key Laboratory of Surveillance and Early-Warning On Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China.
| | - Zhijie Zhang
- Department of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China.
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Kai-Jie L, Shun-Xiang C, Wen L, Jing X, Su-Jian P, Hua-Xun Z. [Analysis of malaria epidemic situation and control in Hubei Province from 1974 to 2015]. Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi 2018; 28:393-396. [PMID: 29376279 DOI: 10.16250/j.32.1374.2016017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
OBJECTIVE To analyze the malaria control measures and epidemic trend in Hubei Province from 1974 to 2015, so as to provide the evidence for malaria elimination path analysis. METHODS The malaria control data in Hubei Province from 1974 to 2015 were collected and analyzed retrospectively by descriptive epidemiological methods. RESULTS The epidemic process of malaria in Hubei Province was divided into four stages. From 1974 to 1979, it was high prevalence state of malaria, and the average annual incidence was 174.47/10 000. From 1980 to 1999, the main control strategies were to control the infection source and mosquitoes, and the average annual incidence was 17.30/10 000, significantly downward. From 2000 to 2009, through the surveillance of infection sources and controlling malaria outbreaks and strengthening the floating population management, the average annual incidence was 0.42/10 000. After 2010, followed by the elimination phase of malaria, the incidence continued to decline. In 2013, there was no local infection for the first time. The difference of average annual incidence among above-mentioned stages was statistically significant (χ2 = 1 254.36, P < 0.05). CONCLUSIONS The malaria epidemic process in Hubei Provincial experienced the high epidemic stage, sharply drop stage, low incidence phase and the elimination phase. However, the natural factors affecting malaria still exist. Therefore, strengthening the control of imported malaria and surveillance should be the main task in the process of eliminating malaria in the future.
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Affiliation(s)
- Li Kai-Jie
- Hubei Provincial Center for Disease Control and Prevention, Wuhan 430079, China
| | - Cai Shun-Xiang
- Hubei Provincial Center for Disease Control and Prevention, Wuhan 430079, China
| | - Lin Wen
- Hubei Provincial Center for Disease Control and Prevention, Wuhan 430079, China
| | - Xia Jing
- Hubei Provincial Center for Disease Control and Prevention, Wuhan 430079, China
| | - Pei Su-Jian
- Hubei Provincial Center for Disease Control and Prevention, Wuhan 430079, China
| | - Zhang Hua-Xun
- Hubei Provincial Center for Disease Control and Prevention, Wuhan 430079, China
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Xue-Dong W, Ling J, Bin L. [Epidemiological analysis and countermeasures discussion on imported malaria in Zhangjiagang City]. Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi 2017; 29:517-519. [PMID: 29508597 DOI: 10.16250/j.32.1374.2016251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Objective To analyze the epidemiological characteristics of imported malaria in Zhangjiagang City. Methods The epidemiological data were collected and retrospectively analyzed for the distribution, cost, and exit-entry mode and port of imported malaria cases in Zhangjiagang City from 2005 to 2015. Results There were 25 imported malaria cases in Zhangjiagang City from 2005 to 2015, and among them, there were 16 cases of falciparum malaria (64%), 6 cases of vivax malaria (24%), and 3 cases of ovale malaria (12%); there was 1 cases of critically ill (4%), there were 8 cases of serious ill (32%) and 16 cases of mild ill (64%). The time of onset was in accordance with the circular distribution. The peak of the incidence of the imported malaria was one month earlier than that of the domestic infection. The seasonal peak was gentle, and there was also the occurrence in the non-epidemic season in the city. The imported malaria patients were mainly from Africa, followed by Southeast Asia and Oceania. Conclusion The information technology should be applied to improve the key population coverage on the basis of improving the ability of malaria diagnosis and treatment of medical staff and the multi-sector's cooperation for the imported malaria prevention and control in Zhangjiagang City.
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Affiliation(s)
- W Xue-Dong
- Zhangjiagang Center for Disease Control and Prevention, Jiangsu Province, Zhangjiagang 215600, China
| | - J Ling
- Zhangjiagang Center for Disease Control and Prevention, Jiangsu Province, Zhangjiagang 215600, China
| | - L Bin
- Zhangjiagang Center for Disease Control and Prevention, Jiangsu Province, Zhangjiagang 215600, China
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