AI-Driven Early Warning System for Malaria Epidemics in West African Borders: Performance and Community Engagement Synthesis

This study addresses a current research gap in Computer Science concerning AI-driven Early Warning System for Malaria Epidemics in West African Borders: Performance Outcomes and Community Engagement in Tanzania. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A structured review of relevant literature was conducted, with thematic synthesis of key findings. 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. AI-driven Early Warning System for Malaria Epidemics in West African Borders: Performance Outcomes and Community Engagement, Tanzania, Africa, Computer Science, systematic review This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.