Cassidy Tyler, Gillich Peter, Humphries Antony R, van Dorp Christiaan H
Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
Department of Mathematics and Statistics, McGill University, Montreal, Quebec 3A 0G4, Canada.
IMA J Appl Math. 2022 Dec 13;87(6):1043-1089. doi: 10.1093/imamat/hxac027. eCollection 2022 Dec.
Gamma distributed delay differential equations (DDEs) arise naturally in many modelling applications. However, appropriate numerical methods for generic gamma distributed DDEs have not previously been implemented. Modellers have therefore resorted to approximating the gamma distribution with an Erlang distribution and using the linear chain technique to derive an equivalent system of ordinary differential equations (ODEs). In this work, we address the lack of appropriate numerical tools for gamma distributed DDEs in two ways. First, we develop a functional continuous Runge-Kutta (FCRK) method to numerically integrate the gamma distributed DDE without resorting to Erlang approximation. We prove the fourth-order convergence of the FCRK method and perform numerical tests to demonstrate the accuracy of the new numerical method. Nevertheless, FCRK methods for infinite delay DDEs are not widely available in existing scientific software packages. As an alternative approach to solving gamma distributed DDEs, we also derive a hypoexponential approximation of the gamma distributed DDE. This hypoexponential approach is a more accurate approximation of the true gamma distributed DDE than the common Erlang approximation but, like the Erlang approximation, can be formulated as a system of ODEs and solved numerically using standard ODE software. Using our FCRK method to provide reference solutions, we show that the common Erlang approximation may produce solutions that are qualitatively different from the underlying gamma distributed DDE. However, the proposed hypoexponential approximations do not have this limitation. Finally, we apply our hypoexponential approximations to perform statistical inference on synthetic epidemiological data to illustrate the utility of the hypoexponential approximation.
伽马分布延迟微分方程(DDEs)在许多建模应用中自然出现。然而,此前尚未实现适用于一般伽马分布DDEs的合适数值方法。因此,建模者们 resort to 使用埃尔朗分布来近似伽马分布,并使用线性链技术来推导一个等效的常微分方程(ODEs)系统。在这项工作中,我们通过两种方式解决伽马分布DDEs缺乏合适数值工具的问题。首先,我们开发了一种函数连续龙格 - 库塔(FCRK)方法,用于对伽马分布DDEs进行数值积分,而无需借助埃尔朗近似。我们证明了FCRK方法的四阶收敛性,并进行了数值测试以证明新数值方法的准确性。然而,用于无限延迟DDEs的FCRK方法在现有的科学软件包中并不广泛可用。作为求解伽马分布DDEs的另一种方法,我们还推导了伽马分布DDEs的次指数近似。这种次指数方法是比常见的埃尔朗近似更准确地逼近真实伽马分布DDEs的方法,但与埃尔朗近似一样,可以被表述为一个ODEs系统,并使用标准的ODE软件进行数值求解。使用我们的FCRK方法提供参考解,我们表明常见的埃尔朗近似可能会产生与基础伽马分布DDEs在性质上不同的解。然而,所提出的次指数近似没有这个局限性。最后,我们应用我们的次指数近似对合成的流行病学数据进行统计推断,以说明次指数近似的效用。