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Studies have shown that stunting and wasting indicators are strongly correlated among children, with the potential of concurrently affecting their physical and cognitive development. However, the identification of subpopulations of children with varying risks of stunting and wasting could be valuable for targeted intervention.
Geostatistical analysis of health data is increasingly used to model spatial variation in malaria prevalence, burden, and other metrics. Traditional inference methods for geostatistical modelling are notoriously computationally intensive, motivating the development of newer, approximate methods for geostatistical analysis or, more broadly, computational modelling of spatial processes.