A novel data-driven methodology for influenza outbreak detection and prediction.

Area of Science:

• Epidemiology
• Public Health
• Data Science

Background:

• Influenza causes millions of severe illnesses and deaths globally each year.
• Early detection and prediction of influenza outbreaks are vital for resource allocation and mitigating impact.
• Existing surveillance methods may lack the granularity for timely local outbreak identification.

Purpose of the Study:

• To develop a data-driven methodology for early detection and prediction of influenza outbreaks at a local level.
• To propose a novel 'region index' (RI) for monitoring influenza activity.
• To enhance public health preparedness through accurate outbreak forecasting.

Main Methods:

• Utilized a large dataset of influenza-like illness diagnoses from over 3000 clinics in Malaysia.
• Developed a new region index (RI) based on diagnostic data to analyze weekly anomalies.
• Employed an ensemble learning method, optimized via cross-validation, for outbreak prediction.
• Validated the model using statistical methods, cross-validation, and a separate testing dataset.

Main Results:

• The proposed methodology demonstrated high sensitivity and accuracy in predicting influenza outbreaks.
• Achieved average scores of 75% recall, 74% precision, and 83% accuracy across five Malaysian regions.
• Results were corroborated by external data sources including Google Flu Trends, news reports, and WHO surveillance data.

Conclusions:

• The data-driven approach provides a reliable and accurate system for local influenza outbreak detection and prediction.
• This methodology can significantly improve resource planning and response to influenza epidemics.
• The study highlights the value of localized, real-time data for infectious disease surveillance.