School of Computing and Information Systems, The University of Melbourne, Parkville, Victoria, Australia.
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia.
Vaccine. 2023 Oct 26;41(45):6630-6636. doi: 10.1016/j.vaccine.2023.09.025. Epub 2023 Oct 2.
The ability for vaccines to protect against infectious diseases varies among individuals, but computational models employed to inform policy typically do not account for this variation. Here we examine this issue: we implement a model of vaccine efficacy developed in the context of SARS-CoV-2 in order to evaluate the general implications of modelling correlates of protection on the individual level. Due to high levels of variation in immune response, the distributions of individual-level protection emerging from this model tend to be highly dispersed, and are often bimodal. We describe the specification of the model, provide an intuitive parameterisation, and comment on its general robustness. We show that the model can be viewed as an intermediate between the typical approaches that consider the mode of vaccine action to be either "all-or-nothing" or "leaky". Our view based on this analysis is that individual variation in correlates of protection is an important consideration that may be crucial to designing and implementing models for estimating population-level impacts of vaccination programs.
疫苗预防传染病的能力在个体之间存在差异，但用于为政策提供信息的计算模型通常没有考虑到这种差异。在这里，我们研究了这个问题：我们实施了一种在 SARS-CoV-2 背景下开发的疫苗效力模型，以评估在个体层面上对保护相关性建模的一般意义。由于免疫反应的高度变异性，该模型得出的个体保护分布往往高度分散，且通常呈双峰分布。我们描述了模型的规范，提供了直观的参数化，并对其一般稳健性进行了评论。我们表明，该模型可以被视为介于考虑疫苗作用模式为“全有或全无”或“渗漏”的典型方法之间的一种模型。我们基于此分析的观点是，保护相关性的个体差异是一个重要的考虑因素，对于设计和实施估计疫苗接种计划对人群影响的模型可能至关重要。
