Rochman Nash D, Wolf Yuri I, Koonin Eugene V
National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894.
medRxiv. 2020 Dec 24:2020.12.19.20248554. doi: 10.1101/2020.12.19.20248554.
The start of 2021 will be marked by a global vaccination campaign against the novel coronavirus SARS-CoV-2. Formulating an optimal distribution strategy under social and economic constraints is challenging. Optimal distribution is additionally constrained by the potential emergence of vaccine resistance. Analogous to chronic low-dose antibiotic exposure, recently inoculated individuals who are not yet immune play an outsized role in the emergence of resistance. Classical epidemiological modelling is well suited to explore how the behavior of the inoculated population impacts the total number of infections over the entirety of an epidemic.
A deterministic model of epidemic evolution is analyzed, with 7 compartments defined by their relationship to the emergence of vaccine-resistant mutants and representing three susceptible populations, three infected populations, and one recovered population. This minimally computationally intensive design enables simulation of epidemics across a broad parameter space. The results are used to identify conditions minimizing the cumulative number of infections.
When an escape variant is only modestly less infectious than the originating strain within a naïve population, there exists an optimal rate of vaccine distribution. Exceeding this rate increases the cumulative number of infections due to vaccine escape. Analysis of the model also demonstrates that inoculated individuals play a major role in the mitigation or exacerbation of vaccine-resistant outbreaks. Modulating the rate of host-host contact for the inoculated population by less than an order of magnitude can alter the cumulative number of infections by more than 20%.
Mathematical modeling shows that optimization of the vaccination rate and limiting post-vaccination contacts can affect the course of an epidemic. Given the relatively short window between inoculation and the acquisition of immunity, these results might merit consideration for an immediate, practical public health response.
2021年初将开展针对新型冠状病毒SARS-CoV-2的全球疫苗接种运动。在社会和经济限制条件下制定最优分配策略具有挑战性。最优分配还受到疫苗抗性可能出现的限制。类似于长期低剂量抗生素暴露,最近接种但尚未产生免疫力的个体在抗性出现中起了很大作用。经典流行病学模型非常适合探索接种人群的行为如何影响整个疫情期间的总感染数。
分析了一个疫情演变的确定性模型,该模型有7个区室,根据它们与疫苗抗性突变体出现的关系来定义,代表三个易感人群、三个感染人群和一个康复人群。这种计算量最小的设计能够在广泛的参数空间内模拟疫情。结果用于确定使累计感染数最小化的条件。
当逃逸变异株在未接触过疫苗的人群中传染性仅略低于原始毒株时,存在一个最优疫苗分配率。超过这个率会因疫苗逃逸而增加累计感染数。对该模型的分析还表明,接种个体在减轻或加剧疫苗抗性疫情爆发中起主要作用。对接种人群的宿主间接触率进行小于一个数量级的调节,可使累计感染数改变超过20%。
数学模型表明,优化疫苗接种率和限制接种后接触可影响疫情进程。鉴于接种与获得免疫力之间的窗口期相对较短,这些结果可能值得立即考虑用于实际的公共卫生应对措施。