Comment on: "The Two Cultures of Prevalence Mapping: Small Area Estimation and Model-Based Geostatistics"
Small Area Estimation (SAE) and Model-Based Geostatistics (MBG) provide complementary approaches to prevalence mapping, with their relative advantages depending on the inferential goals and characteristics of the available data. We argue that a fuller comparison should consider model interpretability, the role of epidemiologically motivated covariates, inferential objectives beyond area-level prediction, integration of data from different spatial partitions and surveys, and task-specific model validation. In particular, we question whether survey design variables should routinely be included in MBG models when their effects may instead be mediated by measurable environmental and socio-economic risk factors. We further argue that simulation-based validation, tailored to the operational objectives of prevalence mapping, can provide a more informative assessment of model performance than conventional cross-validation alone.