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March 15, 2026Open Access

Methodological Evaluation of Public Health Surveillance Systems in Senegal Using Time-Series Forecasting Models

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Authors

MMMustapha MbackéMDMohamed DialloINIbrahima Ndiaye

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Overview

Methodological evaluation shows time series forecasting improves public health surveillance in Senegal, indicating effective monitoring strategies.

Key Points

  • This research aims to evaluate the effectiveness of public health surveillance systems in Senegal using forecasting models.
  • Employed ARIMA(p,d,q) model for forecasting disease incidence data
  • Quantified uncertainty with robust standard errors and 95% confidence intervals
  • Analyzed trends and provided insights for public health strategies.
  • ARIMA(1,1,0) model indicated a 42% reduction in reported cases over the study period
  • Time-series forecasting models enhanced monitoring of public health surveillance systems
  • Recommendations include continuous evaluation and adaptation of surveillance strategies.

Cite This Study

Mbacké et al. (2013) studied this question.

synapsesocial.com/papers/69b606d583145bc643d1d4a1https://doi.org/10.5281/zenodo.18997702
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Methodological Evaluation of Public Health Surveillance Systems in Senegal Using Time-Series Forecasting Models2013
  2. 2Methodological Evaluation of Public Health Surveillance Systems in Senegal Using Time-Series Forecasting Models for Yield Improvement Assessment2012
  3. 3Methodological Assessment of Public Health Surveillance Systems in Senegal Utilising Time-Series Forecasting Models2000
  4. 4Methodological Evaluation of Public Health Surveillance Systems in Senegal Using Time-Series Forecasting Models for Risk Reduction Measurement2011
  5. 5Methodological Assessment of Public Health Surveillance Systems in Senegal Using Time-Series Forecasting Models for Risk Reduction Analysis2009