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March 8, 2026Open Access

Methodological Assessment of Public Health Surveillance Systems in Senegal Using Time-Series Forecasting Models for Risk Reduction Analysis

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Analysis demonstrates predictive accuracy in disease forecasting in Senegal, suggesting machine learning could enhance surveillance.

To assess public health surveillance systems in Senegal using time-series forecasting models for risk reduction.
Utilized ARIMA model for historical healthcare data analysis
Quantified uncertainty with robust standard errors
Forecasted disease incidence from surveillance data
60% of forecasted disease incidence aligned with reported cases
ARIMA model showcased predictive accuracy
Highlights need for machine learning integration to improve predictions

Ndiaye et al. (2009) studied this question.

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March 8, 2026Open Access

View Full Paper

Ask AI

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Authors

Analysis demonstrates predictive accuracy in disease forecasting in Senegal, suggesting machine learning could enhance surveillance.

To assess public health surveillance systems in Senegal using time-series forecasting models for risk reduction.
Utilized ARIMA model for historical healthcare data analysis
Quantified uncertainty with robust standard errors
Forecasted disease incidence from surveillance data
60% of forecasted disease incidence aligned with reported cases
ARIMA model showcased predictive accuracy
Highlights need for machine learning integration to improve predictions

Ndiaye et al. (2009) studied this question.

View Full Paper

Ask AI

Bookmark