  Global problems, such as pandemics and climate change, require rapid
international coordination and diffusion of policy. These phenomena are rare
however, with one notable example being the international policy response to
the COVID-19 pandemic in early 2020. Here we build an agent-based model of this
rapid policy diffusion, where countries constitute the agents and with the
principal mechanism for diffusion being peer mimicry. Since it is challenging
to predict accurately the policy diffusion curve, we utilize data assimilation,
that is an ``on-line'' feed of data to constrain the model against
observations. The specific data assimilation algorithm we apply is a particle
filter because of its convenient implementation, its ability to handle
categorical variables and because the model is not overly computationally
expensive, hence a more efficient algorithm is not required. We find that the
model alone is able to predict the policy diffusion relatively well with an
ensemble of at least 100 simulation runs. The particle filter however improves
the fit to the data, reliably so from 500 runs upwards, and increasing
filtering frequency results in improved prediction.
