  We consider inference for a collection of partially observed, stochastic,
interacting, nonlinear dynamic processes. Each process is identified with a
label called its unit, and our primary motivation arises in biological
metapopulation systems where a unit corresponds to a spatially distinct
sub-population. Metapopulation systems are characterized by strong dependence
through time within a single unit and relatively weak interactions between
units, and these properties make block particle filters an effective tool for
simulation-based likelihood evaluation. Iterated filtering algorithms can
facilitate likelihood maximization for simulation-based filters. We introduce
an iterated block particle filter applicable when parameters are unit-specific
or shared between units. We demonstrate this algorithm by performing inference
on a coupled epidemiological model describing spatiotemporal measles case
report data for twenty towns.
