Alfaro-Murillo Jorge A, Feng Zhilan, Glasser John W
Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT, USA.
Department of Mathematics, Purdue University, West Lafayette, IN, USA.
J Differ Equ. 2019 Nov;267(10):5631-5661. doi: 10.1016/j.jde.2019.06.002.
Modeling time-since-last-infection (TSLI) provides a means of formulating epidemiological models with fewer state variables (or epidemiological classes) and more flexible descriptions of infectivity after infection and susceptibility after recovery than usual. The model considered here has two time variables: chronological time () and the TSLI (), and it has only two classes: never infected ( ) and infected at least once (). Unlike most age-structured epidemiological models, in which the equation is formulated using , ours uses a more general differential operator. This allows weaker conditions for the infectivity and susceptibility functions, and thus, is more generally applicable. We reformulate the model as an age dependent population problem for analysis, so that published results for these types of problems can be applied, including the existence and regularity of model solutions. We also show how other coupled models having two types of time variables can be stated as age dependent population problems.
对自上次感染以来的时间(TSLI)进行建模提供了一种方法,可用于构建流行病学模型,该模型具有比通常更少的状态变量(或流行病学类别),并且对感染后传染性和康复后易感性的描述更加灵活。这里考虑的模型有两个时间变量:日历时间()和TSLI(),并且它只有两个类别:从未感染过()和至少感染过一次()。与大多数年龄结构的流行病学模型不同,在那些模型中,方程是使用来制定的,而我们的模型使用更一般的微分算子。这使得对传染性和易感性函数的条件更弱,因此更具普遍适用性。我们将模型重新表述为一个与年龄相关的人口问题以便进行分析,这样就可以应用针对这类问题已发表的结果,包括模型解的存在性和正则性。我们还展示了具有两种时间变量的其他耦合模型如何可以表述为与年龄相关的人口问题。