"background": "Municipal infrastructure asset management in developing nations often relies on deterministic models, which inadequately capture systemic inefficiencies and uncertainty. In Rwanda, the need for robust diagnostic tools to evaluate the performance of water, road, and sanitation asset systems is pressing for strategic investment. ", "purpose and objectives": "This short report presents a novel Bayesian hierarchical model to diagnose efficiency gains within municipal infrastructure asset management systems. The objective is to provide a probabilistic framework for quantifying performance improvements and identifying underperforming asset categories. ", "methodology": "A Bayesian hierarchical model was developed, formalised as y{it \ (\ + \, \²), where yit is the observed efficiency metric for asset i in period t, \ captures asset-specific random effects, and \ represents a temporal trend. Posterior distributions were estimated using Hamiltonian Monte Carlo, with inferences drawn from 95% credible intervals. ", "findings": "The model identified a positive temporal trend (\), with a 95% credible interval of 0. 12, 0. 18, indicating a consistent annual improvement in aggregate system efficiency. A key finding was the marked underperformance of sanitation assets relative to water and road networks, which constrained overall system gains. ", "conclusion": "The Bayesian hierarchical model provides a statistically robust diagnostic tool, confirming measurable efficiency gains while highlighting persistent disparities between infrastructure types. It successfully quantifies uncertainty in performance assessment. ", "recommendations": "Asset management policy should prioritise targeted interventions for sanitation infrastructure. We recommend the adoption of this probabilistic modelling framework for routine performance audits and long-term strategic planning at the municipal level. ", "key words": "Bayesian inference, infrastructure performance, asset management, efficiency diagnostics, hierarchical modelling", "contribution statement": "This paper introduces a novel probabilistic framework for infrastructure efficiency analysis, explicitly modelling uncertainty and heterogeneity. A concrete result is the quantification of a positive systemic efficiency
Niyigena et al. (Thu,) studied this question.