{ "background": "Public health surveillance systems in Nigeria face significant methodological challenges, including inconsistent data quality and delayed outbreak detection, which undermine effective risk reduction. There is a critical need for robust, field-tested interventions to improve system performance and measure their impact on public health risk.", "purpose and objectives": "This study aimed to evaluate a novel, technology-enhanced surveillance protocol and quantitatively measure its efficacy in reducing notifiable disease risk compared to the standard national system.", "methodology": "A stratified, cluster-randomised field trial was conducted across 120 local government areas. The intervention arm implemented a structured data verification and real-time alert protocol, while the control arm continued with routine surveillance. The primary outcome was the composite risk score for disease-specific surveillance failures. Analysis used a mixed-effects linear model: $Y{ij} = \\beta0 + \\beta1 T{ij} + \\gamma X{ij} + uj + \\epsilon{ij}$, where $uj$ are cluster random effects, with inference based on cluster-robust standard errors.", "findings": "The intervention significantly reduced the mean composite risk score by 32.1 percentage points (95% CI: 24.8, 39.4; p<0.001). The greatest improvement was observed in the timeliness of outbreak reporting, with a median reduction of 4.2 days from case confirmation to national alert.", "conclusion": "The evaluated methodological intervention substantially enhanced surveillance system performance and directly reduced measurable risk. This demonstrates that targeted, protocol-driven enhancements can effectively mitigate systemic weaknesses.", "recommendations": "National policy should integrate core components of the verified data protocol into the standard surveillance framework. Further research should investigate cost-effectiveness and scalability across different regional health infrastructures.", "key words": "public health surveillance, randomised controlled trial, risk assessment, health systems strengthening, infectious disease, health informatics", "contribution statement": "This study provides the first experimental evidence from a large-scale field trial quantifying the causal effect