  Disaggregation regression has become an important tool in spatial disease
mapping for making fine-scale predictions of disease risk from aggregated
response data. By including high resolution covariate information and modelling
the data generating process on a fine scale, it is hoped that these models can
accurately learn the relationships between covariates and response at a fine
spatial scale. However, validating these high resolution predictions can be a
challenge, as often there is no data observed at this spatial scale. In this
study, disaggregation regression was performed on simulated data in various
settings and the resulting fine-scale predictions are compared to the simulated
ground truth. Performance was investigated with varying numbers of data points,
sizes of aggregated areas and levels of model misspecification. The
effectiveness of cross validation on the aggregate level as a measure of
fine-scale predictive performance was also investigated. Predictive performance
improved as the number of observations increased and as the size of the
aggregated areas decreased. When the model was well-specified, fine-scale
predictions were accurate even with small numbers of observations and large
aggregated areas. Under model misspecification predictive performance was
significantly worse for large aggregated areas but remained high when response
data was aggregated over smaller regions. Cross-validation correlation on the
aggregate level was a moderately good predictor of fine-scale predictive
performance. While the simulations are unlikely to capture the nuances of
real-life response data, this study gives insight into the effectiveness of
disaggregation regression in different contexts.
