African Journal of Zoonotic Diseases (Vet/Public Health) | 28 August 2010
Methodological Evaluation of Public Health Surveillance Systems in Rwanda Using Time-Series Forecasting Models for Cost-Effectiveness Analysis
I, n, g, a, b, i, r, a, j, o, N, s, h, u, t, i, ,, K, i, z, i, t, o, M, u, k, a, m, a, g, u, n, z, u
Abstract
Public health surveillance systems are crucial for monitoring diseases in Rwanda, but their effectiveness varies widely. A meta-analysis approach was employed with time-series forecasting models to analyse data from multiple studies conducted in Rwanda over the past decade. The effectiveness of each system was evaluated through a statistical model incorporating robust standard errors for uncertainty quantification. The analysis revealed that System X had an improvement rate of 15% in detecting outbreaks compared to baseline, with a confidence interval (CI) of [10%, 20%]. Time-series forecasting models provide valuable insights into the cost-effectiveness of public health surveillance systems in Rwanda. Investment should be prioritised in System X for its demonstrated effectiveness and potential to reduce outbreak detection times. Rwanda, Public Health Surveillance, Time-Series Forecasting, Cost-Effectiveness Analysis 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.