Poghotanyan Gayane, Feng Zhilan, Glasser John W, Hill Andrew N
Department of Mathematics, Purdue University, West Lafayette, IN, USA.
Centers for Disease Control and Prevention, National Center for Immunization and Respiratory Diseases, Atlanta, GA, USA.
J Math Biol. 2018 Dec;77(6-7):1795-1831. doi: 10.1007/s00285-018-1216-z. Epub 2018 Feb 14.
The basic reproduction number ([Formula: see text]) can be considerably higher in an SIR model with heterogeneous mixing compared to that from a corresponding model with homogeneous mixing. For example, in the case of measles, mumps and rubella in San Diego, CA, Glasser et al. (Lancet Infect Dis 16(5):599-605, 2016. https://doi.org/10.1016/S1473-3099(16)00004-9 ), reported an increase of 70% in [Formula: see text] when heterogeneity was accounted for. Meta-population models with simple heterogeneous mixing functions, e.g., proportionate mixing, have been employed to identify optimal vaccination strategies using an approach based on the gradient of the effective reproduction number ([Formula: see text]), which consists of partial derivatives of [Formula: see text] with respect to the proportions immune [Formula: see text] in sub-groups i (Feng et al. in J Theor Biol 386:177-187, 2015. https://doi.org/10.1016/j.jtbi.2015.09.006 ; Math Biosci 287:93-104, 2017. https://doi.org/10.1016/j.mbs.2016.09.013 ). These papers consider cases in which an optimal vaccination strategy exists. However, in general, the optimal solution identified using the gradient may not be feasible for some parameter values (i.e., vaccination coverages outside the unit interval). In this paper, we derive the analytic conditions under which the optimal solution is feasible. Explicit expressions for the optimal solutions in the case of [Formula: see text] sub-populations are obtained, and the bounds for optimal solutions are derived for [Formula: see text] sub-populations. This is done for general mixing functions and examples of proportionate and preferential mixing are presented. Of special significance is the result that for general mixing schemes, both [Formula: see text] and [Formula: see text] are bounded below and above by their corresponding expressions when mixing is proportionate and isolated, respectively.
与具有均匀混合的相应模型相比,在具有异质混合的SIR模型中,基本再生数([公式:见正文])可能会高得多。例如,在加利福尼亚州圣地亚哥的麻疹、腮腺炎和风疹病例中,格拉瑟等人(《柳叶刀传染病》16(5):599 - 605,2016年。https://doi.org/10.1016/S1473 - 3099(16)00004 - 9)报告说,当考虑异质性时,[公式:见正文]增加了70%。具有简单异质混合函数(例如比例混合)的元种群模型已被用于使用基于有效再生数([公式:见正文])梯度的方法来确定最优疫苗接种策略,有效再生数由[公式:见正文]关于子群体i中免疫比例[公式:见正文]的偏导数组成(冯等人,《理论生物学杂志》386:177 - 187,2015年。https://doi.org/10.1016/j.jtbi.2015.09.006;《数学生物科学》287:93 - 104,2017年。https://doi.org/10.1016/j.mbs.2016.09.013)。这些论文考虑了存在最优疫苗接种策略的情况。然而,一般来说,使用梯度确定的最优解对于某些参数值可能不可行(即疫苗接种覆盖率超出单位区间)。在本文中,我们推导了最优解可行的解析条件。获得了[公式:见正文]个子群体情况下最优解的显式表达式,并推导了[公式:见正文]个子群体最优解的边界。这是针对一般混合函数完成的,并给出了比例混合和优先混合的示例。特别重要的结果是,对于一般混合方案,当混合分别为比例混合和孤立混合时,[公式:见正文]和[公式:见正文]分别由它们相应的表达式上下界定。