Lee Bruce Y, Mueller Leslie E, Tilchin Carla G
Public Health Computational and Operations Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.
Public Health Computational and Operations Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.
Vaccine. 2017 Jan 20;35 Suppl 1(Suppl 1):A36-A42. doi: 10.1016/j.vaccine.2016.11.033. Epub 2016 Dec 22.
Vaccines reside in a complex multiscale system that includes biological, clinical, behavioral, social, operational, environmental, and economical relationships. Not accounting for these systems when making decisions about vaccines can result in changes that have little effect rather than solutions, lead to unsustainable solutions, miss indirect (e.g., secondary, tertiary, and beyond) effects, cause unintended consequences, and lead to wasted time, effort, and resources. Mathematical and computational modeling can help better understand and address complex systems by representing all or most of the components, relationships, and processes. Such models can serve as "virtual laboratories" to examine how a system operates and test the effects of different changes within the system. Here are ten lessons learned from using computational models to bring more of a systems approach to vaccine decision making: (i) traditional single measure approaches may overlook opportunities; (ii) there is complex interplay among many vaccine, population, and disease characteristics; (iii) accounting for perspective can identify synergies; (iv) the distribution system should not be overlooked; (v) target population choice can have secondary and tertiary effects; (vi) potentially overlooked characteristics can be important; (vii) characteristics of one vaccine can affect other vaccines; (viii) the broader impact of vaccines is complex; (ix) vaccine administration extends beyond the provider level; and (x) the value of vaccines is dynamic.
疫苗存在于一个复杂的多尺度系统中,该系统包括生物学、临床、行为、社会、运营、环境和经济等方面的关系。在做出有关疫苗的决策时,如果不考虑这些系统,可能会导致产生效果甚微的改变而非解决方案,导致不可持续的解决方案,忽略间接(如二级、三级及更深远的)影响,引发意外后果,并造成时间、精力和资源的浪费。数学和计算建模可以通过呈现所有或大部分组件、关系和过程,帮助更好地理解和应对复杂系统。此类模型可以充当“虚拟实验室”,用于研究系统如何运行,并测试系统内不同变化的影响。以下是从使用计算模型为疫苗决策引入更多系统方法中吸取的十条经验教训:(i)传统的单一衡量方法可能会忽略机会;(ii)许多疫苗、人群和疾病特征之间存在复杂的相互作用;(iii)考虑不同视角可以识别协同效应;(iv)不应忽视分发系统;(v)目标人群的选择可能会产生二级和三级影响;(vi)可能被忽视的特征可能很重要;(vii)一种疫苗的特征可能会影响其他疫苗;(viii)疫苗的更广泛影响很复杂;(ix)疫苗接种的范围超出了提供者层面;(x)疫苗的价值是动态的。