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Agent-based models of disease transmission involve stochastic rules thatspecify how a number of individuals would infect one another, recover or beremoved from the population. Common yet stringent assumptions stipulateinterchangeability of agents and that all pairwise contact are equally likely.Under these assumptions, the population can be summarized by counting thenumber of susceptible and infected individuals, which greatly facilitatesstatistical inference. We consider the task of inference without suchsimplifying assumptions, in which case, the population cannot be summarized bylow-dimensional counts. We design improved particle filters, where eachparticle corresponds to a specific configuration of the population of agents,that take either the next or all future observations into account whenproposing population configurations. Using simulated data sets, we illustratethat orders of magnitude improvements are possible over bootstrap particlefilters. We also provide theoretical support for the approximations employed tomake the algorithms practical.

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