The municipal infrastructure assets in Senegal are under significant stress due to poor maintenance and aging structures, posing a risk to public safety and economic stability. A Bayesian hierarchical model was constructed using available data on municipal infrastructure assets. The model accounts for spatial heterogeneity and incorporates expert knowledge to quantify risk reduction strategies. The model identified a significant decrease of 20% in structural failure risks when comprehensive maintenance programmes are implemented across Senegal's municipalities. This study validates the effectiveness of Bayesian hierarchical modelling in accurately predicting and mitigating infrastructure asset failures, offering policymakers actionable insights for risk reduction strategies. Policymakers should prioritise investment in preventive maintenance programmes to align with the model's findings. Implementation of these recommendations could lead to substantial savings and improved public safety. Senegal, Municipal Infrastructure, Risk Reduction, Bayesian Hierarchical Model, Structural Failure The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.
Mamadou Sarr (Tue,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: