Time-Series Forecasting Model Evaluation for Public Health Surveillance Systems in Kenya, 2014

This study addresses a current research gap in Medicine concerning Methodological evaluation of public health surveillance systems systems in Kenya: time-series forecasting model for measuring yield improvement in Kenya. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A structured analytical approach was used, integrating formal modelling with domain evidence. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Methodological evaluation of public health surveillance systems systems in Kenya: time-series forecasting model for measuring yield improvement, Kenya, Africa, Medicine, short report This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.