Revisiting the Linear Chain Trick in epidemiological models: Implications of underlying assumptions for numerical solutions

TL;DR

This paper examines how assumptions about disease state durations in epidemiological models affect predictions, demonstrating that using Erlang distributions via the Linear Chain Trick improves realism and impacts epidemic peak timing and size.

Contribution

It introduces an advanced LCT SECIR model with demographic stratification, analyzes the effects of distribution assumptions on epidemic predictions, and evaluates computational performance in simulation frameworks.

Findings

Distribution assumptions significantly affect epidemic peak timing and size.

Simple ODE models can lead to distorted predictions and incorrect policy decisions.

Using Erlang distributions via LCT enhances model realism and accuracy.

Abstract

In order to simulate the spread of infectious diseases, many epidemiological models use systems of ordinary differential equations (ODEs) to describe the underlying dynamics. These models incorporate the implicit assumption, that the stay time in each disease state follows an exponential distribution. However, a substantial number of epidemiological, data-based studies indicate that this assumption is not plausible. One method to alleviate this limitation is to employ the Linear Chain Trick (LCT) for ODE systems, which realizes the use of Erlang distributed stay times. As indicated by data, this approach allows for more realistic models while maintaining the advantages of using ODEs. In this work, we propose an advanced LCT SECIR-type model incorporating eight infection states with demographic stratification. We review key properties of the corresponding LCT model and demonstrate that predictions derived from a simple ODE-based model can be significantly distorted, potentially leading to wrong political decisions. Our findings demonstrate that the influence of distribution assumptions on the behavior at change points and on the prediction of epidemic peaks is substantial, while the assumption has no effect on the final size of the epidemic. With respect to prior findings in literature, we demonstrate that the influence of the number of subcompartments on the timing and size of the epidemic peak is nontrivial and that a general statement cannot be obtained. We, then, show how these age-resolved LCT SECIR-type models capture the spread of SARS-CoV-2 in Germany in 2020. Eventually, we study the implications on the time-to-solution for different LCT models using fixed and adaptive step-size Runge-Kutta methods and provide computational performance for these models in the MEmilio software framework, also using distributed memory parallelism to speed up ensemble runs.