A Gillespie algorithm for non-Markovian stochastic processes

TL;DR

This paper introduces a fast, exact Gillespie algorithm for simulating non-Markovian renewal processes with long-tailed inter-event times, applicable to various complex systems like epidemics and social networks.

Contribution

It presents a novel Gillespie algorithm based on Laplace transforms for multivariate renewal processes, improving computational efficiency over existing methods.

Findings

The algorithm accurately simulates epidemic processes on networks.

Positive correlation in inter-event times has minimal impact on epidemic dynamics.

The method is applicable to systems with long-tailed inter-event time distributions.

Abstract

The Gillespie algorithm provides statistically exact methods for simulating stochastic dynamics modelled as interacting sequences of discrete events including systems of biochemical reactions or earthquake occurrences, networks of queuing processes or spiking neurons, and epidemic and opinion formation processes on social networks. Empirically, the inter-event times of various phenomena obey long-tailed distributions. The Gillespie algorithm and its variants either assume Poisson processes (i.e., exponentially distributed inter-event times), use particular functions for time courses of the event rate, or work for non-Poissonian renewal processes, including the case of long-tailed distributions of inter-event times, but at a high computational cost. In the present study, we propose an innovative Gillespie algorithm for renewal processes on the basis of the Laplace transform. The algorithm makes use of the fact that a class of point processes is represented as a mixture of Poisson processes with different event rates. The method is applicable to multivariate renewal processes whose survival function of inter-event times is completely monotone. It is an exact algorithm and works faster than a recently proposed Gillespie algorithm for general renewal processes, which is exact only in the limit of infinitely many processes. We also propose a method to generate sequences of event times with a tunable amount of positive correlation between inter-event times. We demonstrate our algorithm with exact simulations of epidemic processes on networks, finding that a realistic amount of positive correlation in inter-event times only slightly affects the epidemic dynamics.