Time-Series Forecasting Model for Measuring Adoption Rates in Public Health Surveillance Systems in Rwanda: A Methodological Evaluation

Public health surveillance systems in Rwanda aim to monitor disease outbreaks and track adoption rates of new health interventions. A time-series forecasting model was developed using autoregressive integrated moving average (ARIMA) methodology to predict future adoption trends based on historical data from Rwanda's surveillance system. The ARIMA model demonstrated a significant predictive accuracy with an R² value of 0.85, indicating that the model could forecast adoption rates with moderate reliability. The time-series forecasting model proved effective in measuring and predicting adoption rates within Rwanda’s public health surveillance systems. Further research should be conducted to validate these findings across different health interventions and contexts. Public Health Surveillance, Adoption Rates, Time-Series Forecasting, ARIMA Model Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.