Measuring water pollution effects on antimicrobial resistance through explainable artificial intelligence

Antimicrobial resistance refers to the ability of pathogens to develop resistance to drugs designed to eliminate them, making the infections they cause more difficult to treat and increasing the likelihood of disease diffusion and mortality. As such, antimicrobial resistance is considered as one of the most significant and universal challenges to both health and society, as well as the environment. In our research, we employ the explainable artificial intelligence paradigm to identify the factors that most affect the onset of antimicrobial resistance in diversified territorial contexts, which can vary widely from each other in terms of climatic, economic and social conditions. Specifically, we employ a large set of indicators identified through the One Health framework to predict, at the country level, mortality resulting from antimicrobial resistance related to Acinetobacter baumannii, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Streptococcus pneumoniae. The analysis reveals the outstanding importance of indicators related to water accessibility and quality in determining mortality due to antimicrobial resistance to the considered pathogens across countries, providing perspective as a potential tool for decision support and monitoring.