新发传染病早期疫情趋势及疫苗干预传染病动力学模型研究
Infectious disease modelling study of early epidemiological trends and vaccine interventions in emerging infectious diseases
刘豪梁 1李朝荷 2白文清 2谷玥 2李慧敏 1张佳艺 2周蕾1
作者信息
- 1. 南京医科大学公共卫生学院,南京 211166;中国疾病预防控制中心
- 2. 中国疾病预防控制中心
- 折叠
摘要
目的 构建传染病动力学模型,模拟新发传染病疫情早期流行趋势,评估不同情境下疫苗因素对早期疫情进展的影响,为暴发或大流行疫情早期应对策略,尤其是疫苗干预策略提供理论基础.方法 通过文献检索、贝叶斯推断和马尔可夫链蒙特卡罗算法(MCMC)确定关键参数,建立传染病动力学模型,并运用构建好的模型模拟疫苗在不同情境下的作用.结果 本研究构建的SAPHIREV模型拟合曲线能较好地反映原始疫情的流行趋势,通过情景模拟,假设疫苗效力为ρ=0.36,分别以v=15 000/d、v=95 000/d及v=140 000/d的速度进行接种,疫情规模分别下降18.04％、66.10％、74.23％.假设ρ提高至0.8,以v=95 000/d的速度进行接种,疫情规模变化不大,但会使确诊病例归零时间提前.结论 在面对新发传染病时,早期制定疫苗策略、研发高效疫苗、重视应急准备工作是控制疫情的关键措施.
Abstract
Objective To construct a kinetic model of infectious diseases to simulate the early epidemic trend of emerging infectious diseases,and to evaluate the impact of vaccine factors on the early epidemic progress in different scenarios,so as to provide a theoretical basis for early response strategies to outbreaks or pandemics,especially vaccine intervention strategies.Methods The key parameters were determined through literature retrieval,Bayesian inference,and Markov chain Monte Carlo algorithm(MCMC)to establish the kinetic model of infectious diseases,and the constructed model was used to simulate the role of vaccines in different scenarios.Results The fitting curve of the SAPHIREV model constructed in this study can better reflect the epidemic trend of the original epidemic.Through scenario simulation,assuming that the vaccine efficacy(ρ)is 0.36,and the vaccination rate is 15 000/day,95 000/day and 140 000/day,the epidemic scale decreases by 18.04％,66.10％and 74.23％,respectively.Assuming that ρ increased to 0.8,and the vaccination rate is 95 000/day,the epidemic scale changes little,but the time of returning to zero of confirmed cases is advanced.Conclusion In the face of emerging infectious diseases,early formulation of vaccine strategies,development of high-efficiency vaccines,and emphasis on emergency preparedness are key measures to control the epidemic.
关键词
SEIR模型/情景构建/参数估计/干预策略Key words
SEIR/Scenario construction/Parameter estimation/Intervention strategy引用本文复制引用
出版年
2024
NETL
NSTL
万方数据