Abstract
{ "background": "Municipal infrastructure asset management in developing nations is often hindered by a lack of robust, data-driven forecasting tools, leading to reactive rather than strategic planning. This gap is particularly acute in sub-Saharan Africa, where rapid urbanisation places immense pressure on existing systems.", "purpose and objectives": "This study aims to methodologically evaluate the current state of municipal infrastructure asset systems and to develop a predictive time-series model for forecasting the adoption rates of key infrastructure assets, providing a tool for long-term capital planning.", "methodology": "A longitudinal dataset of national infrastructure inventories was analysed. The methodological evaluation employed a systematic scoring framework. For forecasting, an autoregressive integrated moving average (ARIMA) model was specified as $Yt = \\mu + \\phi1 Y{t-1} + \\theta1 \\epsilon{t-1} + \\epsilont$, where parameters were estimated using maximum likelihood. Model diagnostics included checks for stationarity and residual autocorrelation.", "findings": "The methodological evaluation revealed systemic deficiencies in data standardisation. The ARIMA(1,1,1) model provided the best fit, forecasting a significant deceleration in the adoption rate of water supply infrastructure, with a projected increase of only 12.7% (95% CI: 9.2% to 16.1%) over the forecast period, compared to 34% in the preceding era.", "conclusion": "The developed model offers a statistically sound mechanism for projecting infrastructure uptake, highlighting a concerning slowdown in critical service provision. This underscores an urgent need for enhanced investment and procedural reforms.", "recommendations": "Municipal authorities should institutionalise the use of such forecasting models within their asset management plans. Immediate focus is required on policy and financing mechanisms to address the forecasted slowdown in water infrastructure expansion.", "key words": "asset management, infrastructure planning, time-series analysis, forecasting, urban engineering, developing countries", "contribution statement": "This paper provides the first application of a formal