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February 28, 20260 citationsOpen Access

Time-Series Forecasting Model for Public Health Surveillance in Senegal

Time-Series Forecasting Model for Evaluating Public Health Surveillance Systems in Senegal: A Methodological Assessment

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Authors

SMSow Ndiaye MackyMDMamoudou Diop

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Overview

This methodological assessment evaluates forecasting accuracy in public health surveillance systems, indicating potential for improvements.

Key Points

  • The research aims to assess the effectiveness of public health surveillance systems in Senegal through a time-series forecasting model.
  • Developed a time-series forecasting model based on historical public health data.
  • Utilized an autoregressive integrated moving average (ARIMA) model for predictions.
  • Evaluated the model's predictive performance with R² calculations.
  • The ARIMA model achieved a strong predictive performance with an R² value of 0.85.
  • The forecasting accurately represented monthly case notifications for a year.
  • Identified key areas for operational improvements, such as staff training and automated reporting tools.
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Cite This Study

Macky et al. (2004) studied this question.

synapsesocial.com/papers/69a2877b0a974eb0d3c03369https://doi.org/10.5281/zenodo.18780448
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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 Models: A Longitudinal Study2008
  2. 2Methodological Evaluation of Public Health Surveillance Systems in Senegal Using Time-Series Forecasting Models2003
  3. 3Methodological Evaluation of Public Health Surveillance Systems in Senegal Using Time-Series Forecasting Models2013
  4. 4Methodological Evaluation of Public Health Surveillance Systems in Senegal Using Time-Series Forecasting Models2013
  5. 5Methodological Assessment of Public Health Surveillance Systems in Senegal Using Time-Series Forecasting Models for Risk Reduction Analysis2009