Precognition of Known And Unknown Biothreats: A Risk-Based Approach

Area of Science:

• Epidemiology
• Public Health
• Artificial Intelligence

Background:

• Infectious disease outbreaks, including Zika, Chikungunya, SARS-CoV-2, and Mpox, pose significant global health, trade, and security risks.
• Current predictive models demonstrate limited accuracy and sensitivity, hindering effective prevention of transnational disease propagation.
• Existing annual infectious disease indexes lack the granularity needed for effective stakeholder mitigation strategies.

Purpose of the Study:

• To highlight the necessity of comprehensive, international-level analysis of infectious disease risk profiles, vulnerabilities, and mitigation capacities.
• To underscore the limitations of current data analysis methods in handling the complexity and volume of real-time epidemiological data.
• To introduce autonomous biosurveillance as a solution for near real-time, risk-based policymaking and operational decision support.

Main Methods:

• Analysis of the limitations in current data mining and artificial intelligence algorithms for infectious disease outbreak prediction.
• Evaluation of the data requirements for quantitative risk assessment, including billions of near real-time data points from heterogeneous sources.
• Exploration of the potential of autonomous biosurveillance systems.

Main Results:

• Current models struggle with predicting novel pathogens and disease resurgences, impacting global health and security.
• Effective quantitative risk assessment necessitates integrating and analyzing vast amounts of complex, heterogeneous, and time-sensitive data, often exceeding human cognitive capacity.
• Autonomous biosurveillance presents a viable approach for near real-time, evidence-based decision support.

Conclusions:

• There is a critical need for advanced biosurveillance systems to overcome the limitations of current infectious disease prediction models.
• Autonomous biosurveillance can enable more effective, timely, and data-driven public health interventions and policy decisions.
• Integrating complex, real-time data through autonomous systems is essential for enhancing global preparedness and response to infectious disease threats.