Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, GA, 30322, United States of America.
Department of Biological and Medical Physics, Moscow Institute of Physics and Technology, Dolgoprudny, 141701, Russia.
PLoS One. 2018 Jun 26;13(6):e0199674. doi: 10.1371/journal.pone.0199674. eCollection 2018.
For most pathogens, vaccination reduces the spread of the infection and total number of cases; thus, public policy usually advocates maximizing vaccination coverage. We use simple mathematical models to explore how this may be different for pathogens, such as influenza, which exhibit strain variation. Our models predict that the total number of seasonal influenza infections is minimized at an intermediate (rather than maximal) level of vaccination, and, somewhat counter-intuitively, further increasing the level of the vaccination coverage may lead to higher number of influenza infections and be detrimental to the public interest. This arises due to the combined effects of: competition between multiple co-circulating strains; limited breadth of protection afforded by the vaccine; and short-term strain-transcending immunity following natural infection. The study highlights the need for better quantification of the components of vaccine efficacy and longevity of strain-transcending cross-immunity in order to generate nuanced recommendations for influenza vaccine coverage levels.
对于大多数病原体,接种疫苗可减少感染的传播和总病例数;因此,公共政策通常主张最大限度地提高疫苗接种率。我们使用简单的数学模型来探讨对于表现出菌株变异的病原体(如流感),情况可能会有所不同。我们的模型预测,季节性流感感染的总数在中等(而不是最大)的疫苗接种水平下最小化,并且有点违反直觉的是,进一步提高疫苗接种率可能会导致更多的流感感染,并对公众利益造成损害。这是由于多种共同循环菌株之间的竞争;疫苗提供的保护范围有限;以及自然感染后短期的跨菌株免疫。该研究强调需要更好地量化疫苗功效的各个组成部分和跨菌株交叉免疫的持久性,以便为流感疫苗接种率水平提出细致入微的建议。