Integrating quantitative microbial risk assessment and epidemiologic modeling to evaluate Cryptosporidium infection risk in children under two

This study assessed Cryptosporidium parvum infection risks associated with protected and unprotected drinking-water sources in three rural kebeles of Oromia, Ethiopia. Using Escherichia coli data collected in rainy and dry seasons, we developed a Monte Carlo-based quantitative microbial risk assessment (QMRA) to estimate Cryptosporidium infection risk indirectly from E. coli indicators and compute household-level annual probabilities for children under two. We then applied generalized estimating equations, clustered by kebele and adjusted for season, to derive epidemiologically interpretable odds ratios (OR) and risk ratios (RR). Annual infection probabilities substantially exceeded the WHO benchmark of 10−4 infections per person-year for all sources. While unprotected water sources exhibited moderately higher QMRA-derived risks, protection status was not a significant predictor in adjusted models (OR = 1.34 (95% CI: 0.62–3.44) and RR = 1.07 (95% CI: 0.66–1.72); both p > 0.3). In contrast, rainy season samples showed markedly higher risks (OR = 6.07; RR = 3.10; both p < 0.001), indicating strong hydrological amplification during rainfall. These findings highlight the need for targeted, season-responsive interventions and integrated behavioral strategies to reduce waterborne disease risk among vulnerable children.