A data-driven model for influenza transmission incorporating media effects

Area of Science:

• Epidemiology
• Computational Biology
• Public Health

Background:

• Modeling infectious disease transmission, like influenza, has historically faced challenges due to limited quantitative data on public media engagement.
• The rise of big data, particularly from online social media, now offers unprecedented opportunities to track population-level media interaction during epidemics.

Purpose of the Study:

• To develop and validate a novel mathematical model for influenza dynamics that incorporates media engagement.
• To establish a functional relationship between online media data and traditional influenza surveillance data.
• To demonstrate the improved explanatory power of media-influenced models for historical outbreaks.

Main Methods:

• Integration of a large-scale online dataset (millions of shared messages related to influenza) with traditional influenza surveillance data.
• Development of a simple deterministic model for influenza transmission dynamics, including a new function for media effects.
• Application of model selection techniques to compare the proposed media function against previously published functions using historical outbreak data.

Main Results:

• A functional form for the relationship between online media engagement and influenza activity was identified.
• The developed deterministic model, incorporating media effects, successfully explained historical influenza outbreak dynamics.
• The proposed media function demonstrated a superior fit to historical data compared to alternative models from previous studies.

Conclusions:

• Quantitative analysis of big data from social media provides valuable insights into disease transmission dynamics.
• Incorporating media engagement into epidemiological models significantly enhances their ability to explain and predict disease outbreaks.
• This approach offers a more robust framework for understanding the interplay between public communication and infectious disease spread.