Real-Time Forecast of Influenza Outbreak Using Dynamic Network Marker Based on Minimum Spanning Tree

Area of Science:

• Epidemiology
• Computational Biology
• Public Health

Background:

• Influenza pandemics pose a significant global threat to health and economies.
• Real-time monitoring of influenza outbreaks is challenging due to complex spatial and temporal dynamics.
• Effective influenza outbreak prediction is a public health priority.

Purpose of the Study:

• To develop a computational method for identifying critical stages preceding influenza outbreaks.
• To provide an early-warning system for influenza pandemics.
• To enhance public health surveillance capabilities.

Main Methods:

• Developed a minimum-spanning-tree-based dynamical network marker (MST-DNM) computational method.
• Utilized historical influenza outpatient data from 2009-2018.
• Validated the MST-DNM strategy in three Japanese cities/regions: Tokyo, Osaka, and Hokkaido.

Main Results:

• The MST-DNM method accurately predicted influenza outbreaks in the studied regions.
• Early-warning signals were detected an average of 4 weeks before each outbreak.
• The method demonstrated effectiveness in identifying pre-outbreak tipping points.

Conclusions:

• The MST-DNM strategy shows significant potential for practical public health surveillance.
• This computational approach can aid in preparing for and mitigating influenza pandemics.
• Early detection of influenza outbreaks is feasible through network dynamics analysis.