Emerging data inputs for infectious diseases surveillance and decision making

Area of Science:

• Public Health
• Epidemiology
• Infectious Disease Surveillance

Background:

• Infectious diseases pose a significant global health and social burden, necessitating effective surveillance for public health policy.
• The COVID-19 pandemic accelerated the adoption of new data inputs for disease monitoring.

Purpose of the Study:

• To review current and emerging data inputs for infectious disease surveillance.
• To summarize the benefits and limitations of these novel data sources.

Main Methods:

• Literature review of emerging technologies in infectious disease surveillance.
• Analysis of data inputs including technology-enabled physiological measurements, crowd sourcing, field experiments, and artificial intelligence (AI).

Main Results:

• Novel data inputs offer potential for improved timeliness in surveillance.
• These methods may reduce resource requirements compared to traditional surveillance approaches.
• Key benefits and limitations of each data input are identified.

Conclusions:

• Emerging technologies are transforming infectious disease surveillance.
• AI and other novel data sources present opportunities for more effective public health decision-making.
• Continued research is needed to optimize the use of these technologies.