A machine learning-based universal outbreak risk prediction tool.

Area of Science:

• Epidemiology
• Public Health
• Data Science

Background:

• Existing epidemic risk prediction tools often lack universality, being limited to specific diseases or countries.
• This limitation hinders effective global pandemic prevention and control efforts.
• Cross-country and cross-disease prediction models face challenges due to diverse national and disease-specific factors.

Purpose of the Study:

• To develop a universal risk prediction system capable of assessing outbreak risks across diverse countries and diseases.
• To overcome the limitations of current single-disease, single-country prediction models.
• To enhance global preparedness and response to emerging infectious disease outbreaks.

Main Methods:

• Utilized outbreak data from 43 diseases across 206 countries.
• Developed an ensemble prediction system integrating five machine learning models: Neural Network XGBoost, Logistic Boost, Random Forest, and Kernel SVM.
• Employed economic, cultural, social, and epidemiological factors for prediction.
• Validated model performance using three distinct datasets simulating realistic scenarios.

Main Results:

• Achieved prediction accuracy ranging from 80% to 90%.
• Demonstrated strong predictive ability, adaptability, and generality across different contexts.
• The system provides universal outbreak risk assessments, unconstrained by borders or disease types.

Conclusions:

• The developed universal risk prediction system offers a significant advancement in pandemic preparedness.
• It facilitates rapid response, informed government decision-making, and strengthened international cooperation.
• This tool enhances the global capacity to manage and mitigate the impact of infectious disease outbreaks.