Particle filtering using agent-based transmission models

Abstract

Dynamic models are used to describe the spatio-temporal evolution of complex systems. It is frequently difficult to construct a useful model, especially for emerging situations such as the 2003 SARS outbreak. Here we describe the application of a modern predictor-corrector method -- particle filtering -- that could enable relatively quick model construction and support on-the-fly correction as empirical data arrives. This technique has seen recent use with compartmental models. We contribute here what is, to the best of our knowledge, the first application of particle filtering to agent-based models. While our particle models adapt to different ground-truth conditions, agent-based models exhibit limited adaptability under some model initializations. Several explanations are advanced for such behavior. Since this research serves as an initial foray into this line of investigation, we draw out a clear path of the next steps to determine the possible benefits of using particle filters on agent-based models.