Big Data for Infectious Disease Surveillance and Modeling.

Area of Science:

• Public Health
• Epidemiology
• Health Informatics

Background:

• Traditional disease surveillance systems face limitations in timeliness and granularity.
• The increasing volume and variety of health-related data present new opportunities for public health.
• Advances in data science offer potential for more robust infectious disease monitoring and prediction.

Purpose of the Study:

• To review recent advancements in applying big data to public health challenges.
• To explore the use of diverse data sources, including electronic health records and digital traces, for disease surveillance.
• To identify key research areas and challenges in the big data landscape for infectious diseases.

Main Methods:

• A comprehensive review of nine independent contributions to a special issue on big data in infectious diseases.
• Consideration of a broad definition of big data, encompassing electronic health records, participatory surveillance, social media, internet searches, and cell-phone logs.
• Highlighting cross-cutting research themes such as representativeness, biases, volatility, and validation.

Main Results:

• Big data applications significantly strengthen disease surveillance, medical adverse event monitoring, and patient mobility tracking.
• Digital traces and electronic health records provide valuable insights into disease transmission dynamics and public sentiment.
• Nine distinct contributions showcase the diverse applications and potential of big data in public health.

Conclusions:

• Big data approaches promise to enhance the granularity and timeliness of epidemiological information.
• Hybrid systems, augmenting traditional surveillance, are crucial for accurate infectious disease modeling and forecasting.
• Further research is needed in areas like data representativeness, bias mitigation, and robust statistical validation for big data in public health.