Public health surveillance systems in Kenya are crucial for monitoring infectious diseases and ensuring timely interventions. However, their reliability and effectiveness vary significantly across different regions. A mixed-method approach was employed, combining quantitative panel-data estimation techniques with qualitative interviews. The study utilised a generalized linear mixed model (GLMM) for estimating system performance. Panel data revealed that the incidence rate of malaria in western Kenya was significantly higher than in eastern regions (p < 0.05). The findings suggest that current surveillance systems need targeted improvements to address regional disparities effectively. Enhancements should focus on increasing resource allocation and personnel training in high-incidence areas, along with strengthening collaboration between health authorities and local communities. Public Health Surveillance, Panel Data Analysis, Malaria Incidence Rate, System Reliability Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.