Model-Based Evaluation of Transmissibility and Intervention Measures for a COVID-19 Outbreak in Xiamen City, China

被引:9
|
作者
Liu, Weikang [1 ]
Guo, Zhinan [2 ]
Abudunaibi, Buasiyamu [1 ]
Ouyang, Xue [2 ]
Wang, Demeng [2 ]
Yang, Tianlong [1 ]
Deng, Bin [1 ]
Huang, Jiefeng [1 ]
Zhao, Benhua [1 ]
Su, Yanhua [1 ]
Su, Chenghao [3 ]
Chen, Tianmu [1 ]
机构
[1] Xiamen Univ, Sch Publ Hlth, State Key Lab Mol Vaccinol & Mol Diagnost, Xiamen, Peoples R China
[2] Xiamen Ctr Dis Control & Prevent, Xiamen, Peoples R China
[3] Fudan Univ, Zhongshan Hosp, Xiamen Branch, Xiamen, Peoples R China
关键词
COVID-19; dynamics model; transmissibility; intervention; evaluation; PROVINCE; IMPACT;
D O I
10.3389/fpubh.2022.887146
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
BackgroundIn September 2021, there was an outbreak of coronavirus disease 2019 (COVID-19) in Xiamen, China. Various non-pharmacological interventions (NPIs) and pharmacological interventions (PIs) have been implemented to prevent and control the spread of the disease. This study aimed to evaluate the effectiveness of various interventions and to identify priorities for the implementation of prevention and control measures. MethodsThe data of patients with COVID-19 were collected from 8 to 30 September 2021. A Susceptible-Exposed-Infectious-Recovered (SEIR) dynamics model was developed to fit the data and simulate the effectiveness of interventions (medical treatment, isolation, social distancing, masking, and vaccination) under different scenarios. The effective reproductive number (R-eff) was used to assess the transmissibility and transmission risk. ResultsA total of 236 cases of COVID-19 were reported in Xiamen. The epidemic curve was divided into three phases (R-eff = 6.8, 1.5, and 0). Notably, the cumulative number of cases was reduced by 99.67% due to the preventive and control measures implemented by the local government. In the effective containment stage, the number of cases could be reduced to 115 by intensifying the implementation of interventions. The total number of cases (TN) could be reduced by 29.66-95.34% when patients voluntarily visit fever clinics. When only two or three of these measures are implemented, the simulated TN may be greater than the actual number. As four measures were taken simultaneously, the TN may be <100, which is 57.63% less than the actual number. The simultaneous implementation of five interventions could rapidly control the transmission and reduce the number of cases to fewer than 25. ConclusionWith the joint efforts of the government and the public, the outbreak was controlled quickly and effectively. Authorities could promptly cut the transmission chain and control the spread of the disease when patients with fever voluntarily went to the hospital. The ultimate effect of controlling the outbreak through only one intervention was not obvious. The combined community control and mask wearing, along with other interventions, could lead to rapid control of the outbreak and ultimately lower the total number of cases. More importantly, this would mitigate the impact of the outbreak on society and socioeconomics.
引用
收藏
页数:15
相关论文
共 50 条
  • [21] The Impact of the Control Measures during the COVID-19 Outbreak on Air Pollution in China
    Fan, Cheng
    Li, Ying
    Guang, Jie
    Li, Zhengqiang
    Elnashar, Abdelrazek
    Allam, Mona
    de Leeuw, Gerrit
    REMOTE SENSING, 2020, 12 (10)
  • [22] The effectiveness of contact tracing in mitigating COVID-19 outbreak: A model-based analysis in the context of India
    Das, Dhiraj Kumar
    Khatua, Anupam
    Kar, T. K.
    Jana, Soovoojeet
    APPLIED MATHEMATICS AND COMPUTATION, 2021, 404
  • [23] COVID-19 Outbreak Prediction Based on SEIQR Model
    Zhao, Yuankang
    He, Yi
    Zhao, Xiaosong
    PROCEEDINGS OF THE 39TH CHINESE CONTROL CONFERENCE, 2020, : 1133 - 1137
  • [25] Technology intervention for preventing COVID-19 outbreak
    Pandey, Prateek
    Litoriya, Ratnesh
    INFORMATION TECHNOLOGY & PEOPLE, 2021, 34 (04) : 1233 - 1251
  • [26] A COVID-19 Outbreak - Nangong City, Hebei Province, China, January 2021
    Liu, Shiwei
    Yuan, Shuhua
    Sung, Yinqi
    Zhang, Baoguo
    Wang, Huazhi
    Lu, Jinxing
    Tan, Wenjie
    Liu, Xiaoqiu
    Zhang, Qi
    Xia, Yunting
    Lyu, Xifang
    Li, Jianguo
    Guo, Yan
    CHINA CDC WEEKLY, 2021, 3 (19): : 401 - 404
  • [27] Non-pharmaceutical intervention strategies for outbreak of COVID-19 in Hangzhou, China
    Kong, Q.
    Jin, H.
    Sun, Z.
    Kao, Q.
    Chen, J.
    PUBLIC HEALTH, 2020, 182 : 185 - 186
  • [28] Design and evaluation of an educational intervention on the COVID-19 pandemic and prevention measures
    Portillo-Blanco, Ane
    Ramon Diez, Jose
    Barrutia, Oihana
    Garmendia, Mikel
    Guisasola, Jenaro
    REVISTA EUREKA SOBRE ENSENANZA Y DIVULGACION DE LAS CIENCIAS, 2022, 19 (01):
  • [29] Model-based forecasting for Canadian COVID-19 data
    Chen, Li-Pang
    Zhang, Qihuang
    Yi, Grace Y.
    He, Wenqing
    PLOS ONE, 2021, 16 (01):
  • [30] A model-based assessment of social isolation practices for COVID-19 outbreak response in residential care facilities
    Zachreson, Cameron
    Tobin, Ruarai
    Walker, Camelia
    Conway, Eamon
    Shearer, Freya M.
    McVernon, Jodie
    Geard, Nicholas
    BMC INFECTIOUS DISEASES, 2024, 24 (01)