Feasibility of Booster Vaccination in High-Risk Populations for Controlling Coronavirus Variants — China, 2021

Li, Kangguo; Zhao, Zeyu; Wei, Hongyu; Rui, Jia; Huang, Jiefeng; Guo, Xiaoyu; Guo, Yichao; Yang, Shiting; Abudurusuli, Guzainuer; Luo, Li; Liu, Xingchun; Wang, Yao; Xu, Jingwen; Zhu, Yuanzhao; Yang, Meng; Yang, Tianlong; Liu, Weikang; Deng, Bin; Lee, Chan; Li, Zhuoyang; Li, Peihua; Yu, Shanshan; Yang, Zhong‐Bao; Su, Yanhua; Zhao, Bin; Yan, Ning; Chen, Tianmu

doi:10.46234/ccdcw2021.259

Cited by 7 publications

(8 citation statements)

References 8 publications

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“…The primary mathematical model employed was the SEIR (susceptible, exposed, infectious, and recovered) compartmental model, utilized in 47% [ 193 ]) of the retrieved articles [ 17 , 19 , 24 – 28 , 31 – 36 , 42 , 45 , 46 , 57 , 60 , 65 , 67 , 69 , 71 , 73 , 77 , 80 , 87 , 89 , 91 , 92 , 94 , 96 – 100 , 103 – 107 , 109 , 111 – 113 , 115 , 118 , 122 – 128 , 130 , 131 , 133 , 135 , 138 – 146 , 148 – 150 , 156 , 157 , 159 – 165 , 167 , 168 , 174 – 177 , 179 , 180 , 183 , 185 , 187 , 188 , 192 , 199 , 210 , 212 , 215 , 218 – 220 , 223 – 227 , 233 –…”

Section: Resultsmentioning

confidence: 99%

Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review

Espinosa,

Mora,

Sanabria

et al. 2024

Syst Rev

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Background The interaction between modelers and policymakers is becoming more common due to the increase in computing speed seen in recent decades. The recent pandemic caused by the SARS-CoV-2 virus was no exception. Thus, this study aims to identify and assess epidemiological mathematical models of SARS-CoV-2 applied to real-world data, including immunization for coronavirus 2019 (COVID-19). Methodology PubMed, JSTOR, medRxiv, LILACS, EconLit, and other databases were searched for studies employing epidemiological mathematical models of SARS-CoV-2 applied to real-world data. We summarized the information qualitatively, and each article included was assessed for bias risk using the Joanna Briggs Institute (JBI) and PROBAST checklist tool. The PROSPERO registration number is CRD42022344542. Findings In total, 5646 articles were retrieved, of which 411 were included. Most of the information was published in 2021. The countries with the highest number of studies were the United States, Canada, China, and the United Kingdom; no studies were found in low-income countries. The SEIR model (susceptible, exposed, infectious, and recovered) was the most frequently used approach, followed by agent-based modeling. Moreover, the most commonly used software were R, Matlab, and Python, with the most recurring health outcomes being death and recovery. According to the JBI assessment, 61.4% of articles were considered to have a low risk of bias. Interpretation The utilization of mathematical models increased following the onset of the SARS-CoV-2 pandemic. Stakeholders have begun to incorporate these analytical tools more extensively into public policy, enabling the construction of various scenarios for public health. This contribution adds value to informed decision-making. Therefore, understanding their advancements, strengths, and limitations is essential.

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Section: Resultsmentioning

confidence: 99%

Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review

Espinosa,

Mora,

Sanabria

et al. 2024

Syst Rev

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“…The authorities should continue to enhance vaccination strategies for the population to establish an immune barrier to prevent the spread of the virus ( 36 ). Consequently, we recommend the early detection and rational management of the outbreak through active consultation and treatment of febrile patients and timely isolation of high-risk populations, as well as continued enhancement of drug and vaccine development to prevent and control possible future COVID-19 outbreaks, ultimately reduce the adverse effects of urban containment and community control measures ( 37 , 38 ).…”

Section: Discussionmentioning

confidence: 99%

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

Liu

Guo²,

Abudunaibi

et al. 2022

Front. Public Health

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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 (Reff) 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 (Reff = 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.

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“…The most common ODE model is a system of differential equations based on susceptible-infectious-removed (SIR) and susceptible-exposed-infectious-removed (SEIR) compartments ( 32 ). ODE models can be developed by combining population dynamics, vaccination dynamics, isolation, and population exposures ( 33 - 34 ).…”

Section: Status Of Covid-19 Modelsmentioning

confidence: 99%

Mathematical Models Supporting Control of COVID-19

Deng

Yan

et al. 2022

China CDC Weekly

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Feasibility of Booster Vaccination in High-Risk Populations for Controlling Coronavirus Variants — China, 2021

Cited by 7 publications

References 8 publications

Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review

Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review

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

Mathematical Models Supporting Control of COVID-19

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