Abstract

This study introduces a novel methodological framework for analyzing aggregated count data in the context of disease mapping and spatial regression, focusing on Acute Gastroenteritis (AGE) in the coastal wetlands of Douala, Cameroon. Building on previous work, our approach enhances prior spatialization selection and addresses ecological bias through mutual standardization techniques, refined model selection processes, and robust prior distribution specifications. Data collected between 2010 and 2013 from six health areas including comprehensive physico-chemical and bacteriological analyses of 40 wells during both dry and rainy seasons were analysed using Geographically Weighted Regression (GWR) and a hierarchical Maximum Entropy (MaxEnt) framework. While GWR demonstrated sensitivity to ecological bias, the improved MaxEnt approach yielded seven distinct models that effectively captured the spatio-temporal dynamics of AGE, with risk estimates in affected areas ranging from 0.50% to 0.76% and stable AUC values averaging 0.70. These findings not only underscore the endemic complexity of AGE in this coastal region but also illustrate the potential of enhanced geospatial models to inform public health interventions and environmental management strategies in the face of climate variability.

Keywords:Acute Gastroenteritis (AGE); Maximum Entropy (MaxEnt); Geographically Weighted Regression (GWR); Ecological Bias; Area Under the Curve (AUC); Environmental Variables; Spatial Regression