Abstract
{ "background": "Universal access to safe drinking water remains a critical engineering challenge in sub-Saharan Africa. Understanding the dynamics and drivers of water treatment technology adoption is essential for infrastructure planning and investment, yet robust predictive models are lacking.", "purpose and objectives": "This study aimed to develop and validate a novel Bayesian hierarchical model to evaluate the adoption rates of different water treatment systems, and to project future adoption trajectories under varying policy scenarios.", "methodology": "We developed a Bayesian hierarchical model where the log-odds of adoption $\eta{ij} = \log(\\frac{p{ij}}{1-p{ij}})$ for technology $i$ in region $j$ is modelled as $\eta{ij} = \alpha + \beta Xj + ui + vj$, with $ui \sim N(0, \sigmau^2)$ and $vj \sim N(0, \sigma_v^2)$. The model was fitted using Markov Chain Monte Carlo methods to national survey and infrastructure audit data.", "findings": "The model identified a strong positive association between household education levels and the adoption of point-of-use filters, with a posterior mean odds ratio of 1.85 (95% credible interval: 1.62 to 2.11). Projections indicate community-scale chlorination systems will remain the most prevalent technology, but adoption growth is highest for household-level solutions.", "conclusion": "The Bayesian hierarchical framework provides a robust tool for quantifying adoption heterogeneity and uncertainty, revealing that socio-economic factors are more influential than geographical ones in technology uptake.", "recommendations": "Infrastructure policy should prioritise targeted educational programmes alongside technology deployment. Future engineering research should integrate similar probabilistic models into lifecycle cost-benefit analyses for water systems.", "key words": "Bayesian statistics, hierarchical model, water treatment, technology adoption, infrastructure planning, probabilistic projection", "contribution statement": "This paper presents a novel probabilistic modelling framework that explicitly quantifies uncertainty in technology adoption forecasts, providing a superior