African Veterinary Pathology | 14 August 2005
Time-Series Forecasting Model Assessment in Tanzania's Public Health Surveillance Systems,
K, a, m, a, u, M, i, h, i, g, o, ,, S, s, e, r, u, n, k, u, w, a, C, h, i, t, u, w, o, ,, M, f, e, k, a, M, f, i, n, z, i
Abstract
This study evaluates the effectiveness of time-series forecasting models in Tanzania's public health surveillance systems. The analysis employs a SARIMA (Seasonal AutoRegressive Integrated Moving Average) model to forecast future trends. A 20% reduction in forecasting errors was observed, indicating improved predictive accuracy. The SARIMA model effectively enhances cost-effectiveness metrics for public health surveillance. Implementing the model could reduce resource wastage and improve disease management strategies. Treatment effect was estimated with $\text{logit}(p<em>i)=\beta</em>0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.