{ "background": "Public health surveillance systems are critical for disease control, yet robust methodological frameworks for evaluating their long-term operational reliability in low-resource settings remain underdeveloped.", "purpose and objectives": "This study aimed to develop and apply a quasi-experimental design to quantitatively assess the reliability of the national integrated disease surveillance and response (IDSR) system, focusing on data completeness, timeliness, and consistency.", "methodology": "We employed a controlled interrupted time series analysis, comparing surveillance metrics from intervention districts implementing an enhanced electronic reporting protocol with matched control districts using the legacy system. Reliability was modelled using a generalised estimating equations approach: $Y{it} = \\beta0 + \\beta1 Tt + \\beta2 X{it} + \\beta3 (Tt \\times X{it}) + \\epsilon{it}$, where $Y{it}$ is the composite reliability score for district $i$ at time $t$, $Tt$ is time, and $X_{it}$ is the intervention indicator. Robust standard errors were clustered at the district level.", "findings": "The enhanced reporting protocol significantly improved system reliability. The intervention was associated with a 22.4 percentage point increase (95% CI: 18.1 to 26.7) in the composite reliability score, driven predominantly by improvements in reporting timeliness.", "conclusion": "Targeted enhancements to surveillance infrastructure, when deployed within a structured support framework, can substantially improve the operational reliability of public health surveillance in resource-limited contexts.", "recommendations": "National health authorities should institutionalise the quasi-experimental evaluation framework for ongoing system assessment and consider scaling up the enhanced electronic reporting protocol, with dedicated training and infrastructural support.", "key words": "surveillance evaluation, health information systems, interrupted time series, health systems strengthening, data quality", "contribution statement": "This paper provides a novel, replicable quasi-experimental methodology for quantifying surveillance system reliability, generating robust causal