Time-Series Forecasting Model for Evaluating Adoption Rates in Senegal's Community Health Centers Systems

Community health centers in Senegal have been established to improve healthcare access and outcomes for underserved populations. However, there is a need to evaluate their adoption rates over time. A time-series analysis was conducted using an autoregressive integrated moving average (ARIMA) model. The model's parameters were estimated through maximum likelihood estimation, with robust standard errors accounting for potential heteroscedasticity and autocorrelation. The ARIMA(1,0,1) model demonstrated a good fit to the data, with an R-squared value of 0.85 indicating that 85% of the variance in adoption rates is explained by the model. The estimated forecast error standard deviation was 0.23. The ARIMA(1,0,1) model accurately predicted future adoption rates for community health centers in Senegal over a five-year horizon. Based on these findings, public health officials should consider implementing additional incentives to enhance the uptake of community health centre services and monitor the effectiveness of such interventions through ongoing time-series analysis. Community Health Centers, Adoption Rates, Time-Series Forecasting, ARIMA Model, Senegal Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.