Public health surveillance systems are crucial for monitoring diseases and guiding responses in Senegal. However, their reliability can vary over time. We utilised ARIMA (AutoRegressive Integrated Moving Average) model for forecasting disease incidence trends and assessed system performance through robust standard errors. The forecast accuracy indicated an 85% confidence interval around the predicted number of cases, suggesting moderate reliability in the surveillance systems. Our findings underscored the need for continuous improvement to enhance the reliability of public health surveillance systems in Senegal. Investment in data infrastructure and training programmes is recommended to ensure system robustness and timely disease detection. Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Diop et al. (Mon,) studied this question.