A variety of demographic statistical models exist for studying population dynamics when individuals can be tracked over time. In cases where data are missing
due to imperfect detection of individuals, the associated measurement error can
be accommodated under certain study designs (e.g., those that involve multiple
surveys or replication). However, the interaction of the measurement error and
the underlying dynamic process can complicate the implementation of statistical
agent-based models (ABMs) for population demography. In a Bayesian setting,
traditional computational algorithms for fitting hierarchical demographic models can be prohibitively cumbersome to construct. Thus, we discuss a variety of
approaches for fitting statistical ABMs to data and demonstrate how to use multistage recursive Bayesian computing and statistical emulators to fit models in such
a way that alleviates the need to have analytical knowledge of the ABM likelihood.
Using two examples, a demographic model for survival and a compartment model
for COVID-19, we illustrate statistical procedures for implementing ABMs. The
approaches we describe are intuitive and accessible for practitioners and can be
parallelized easily for additional computational eciency.
