Sequential Monte Carlo algorithms for agent-based models of disease transmission

TL;DR

This paper develops advanced particle filtering algorithms for agent-based disease models that do not rely on simplifying assumptions, enabling more accurate inference in complex, realistic scenarios.

Contribution

It introduces improved particle filters that incorporate future observations and handle complex agent configurations, advancing inference methods for detailed disease transmission models.

Findings

Order of magnitude improvement over bootstrap particle filters

Algorithms effectively incorporate future observations

Theoretical support validates the approximation methods

Abstract

Agent-based models of disease transmission involve stochastic rules that specify how a number of individuals would infect one another, recover or be removed from the population. Common yet stringent assumptions stipulate interchangeability of agents and that all pairwise contact are equally likely. Under these assumptions, the population can be summarized by counting the number of susceptible and infected individuals, which greatly facilitates statistical inference. We consider the task of inference without such simplifying assumptions, in which case, the population cannot be summarized by low-dimensional counts. We design improved particle filters, where each particle corresponds to a specific configuration of the population of agents, that take either the next or all future observations into account when proposing population configurations. Using simulated data sets, we illustrate that orders of magnitude improvements are possible over bootstrap particle filters. We also provide theoretical support for the approximations employed to make the algorithms practical.