有效的COVID-19疫苗措施

S. Chakrabartty
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摘要

目的:在多种疫苗可获得以及不同目标和战略的疫苗接种规划不均衡的背景下,COVID-19疫苗的有效性对于更好地免疫和持续吸收越来越重要。本文探讨了在人群水平上疫苗效率(VE)的不同衡量标准,以便根据风险对个体群体进行比较和分类,并发现暴露与疾病传播之间的关系。方法:回顾数学/统计模型对SARS-CoV-2疫苗有效性有意义的比较和评估的无模型度量和结果度量,并提出更好的度量方法。结果:具有不切实际假设和局限性的数学模型可能低估了大规模疫苗接种的影响。中断时间序列比较疫苗接种前和疫苗接种后时期的趋势具有广泛的适用性。两种建议的VE测量方法是(i)接种前后时间斜率的差异和(ii)相对危险度(RR),反映暴露与结果之间的关联。结论:从两个线性趋势的斜率之差或RR计算出的VE,便于用可用的统计检验方法对两组疫苗效率的平等性进行统计检验。以比率和比例为基础的测量方法易于计算、解释和促进有意义的比较,包括假设的统计检验。这些措施可以根据年龄、性别、合并症、遗传和生物因素、适应性免疫、之前通过感染或疫苗接种接触过SARS-CoV-2等因素定义的亚人群进行计算。可独立考虑用于评价的无模型措施和考虑疫苗机制管理的模型。未来的研究表明。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Measures of Efficacious COVID-19 Vaccine
Purpose: Effectiveness of COVID-19 vaccine is increasingly important for better immunization, sustain uptake, in the background of availability of a number of vaccines and uneven vaccination programmes with different objectives and strategies. The paper explores different measures of vaccine efficiency (VE) at population level for comparing and classifying groups of individuals in terms of risk and to find relationships between exposures and spread of disease. Methods: To review model-free measures and outcome measures of mathematical/ statistical models towards meaningful comparisons and assessment of efficiency of SARS-CoV-2 vaccine and to suggest better measures. Results: Mathematical models with unrealistic assumptions and limitations may underestimate the impact of mass vaccination. Interrupted time-series comparing trends in pre-vaccination and post-vaccine periods have wide applicability. Two suggested measures of VE are (i) difference of slopes of pre- and post-vaccination periods and (ii) Relative Risk (RR), reflecting association between the exposure and the outcome. Conclusions: VE computed from difference of slopes of two linear trends or RR facilitates statistical testing of equality of the vaccine efficiency for two different groups with available statistical tests. Measures based on ratios and proportions are simple to compute, interpret and facilitate meaningful comparisons including statistical testing of hypothesis. Such measures may be computed for sub-populations defined in terms of the factors of COVID 19 like age, gender, co-morbidities, genetic & biological factors, adaptive immunity, prior exposure to SARS-CoV-2 via infection or via vaccination, etc. Model-free measures for evaluation and models considering administration of vaccine mechanism may be considered independently. Future studies suggested.
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