A unified multiscale field/network/agent based modeling framework for human and ecological health risk analysis

Area of Science:

• Environmental Science
• Computational Modeling
• Risk Assessment

Background:

• Assessing human and ecological health risks requires understanding complex environmental dynamics.
• Existing models often lack the multi-scale integration needed for comprehensive risk assessment.

Purpose of the Study:

• To present a conceptual framework for multi-scale modeling supporting human and ecological health risk assessments.
• To demonstrate the framework's utility through case studies in environmental exposure and risk.

Main Methods:

• Developed a framework integrating interacting networks, agents, fields, and decision-making actors.
• Utilized bidirectional mapping between discrete (agent-based) and continuous (equation-based) modeling approaches.
• Applied deterministic and stochastic modeling techniques.

Main Results:

• The framework effectively represents environmental dynamics across multiple scales.
• Case studies successfully applied the model to human health, ecological risk, and environmentally caused disease scenarios.
• Demonstrated the bidirectional mapping's utility for diverse modeling problems.

Conclusions:

• The proposed multi-scale modeling framework provides a robust approach for integrated health risk assessment.
• This framework enhances the simulation of environmental exposures and their associated risks.
• The approach supports informed decision-making in environmental health management.