Predictive spatiotemporal digital twin modeling for university COVID‑19 assessment using agent-based simulation and real‑time data integration
CAMPUS-SAFE aims to validate the potential of digital twins to predict disease transmission in educational environments by integrating real-time spatiotemporal data and using agent-based modeling. Deployed across two major university campuses, this testbed demonstrate that digital twins can accurately predict COVID-19 infection rates within a 10% margin while optimizing intervention policies to achieve 30% risk reduction, establishing evidence-based standards for pandemic response in higher education.
The testbed integrates agent-based modeling platforms with real-time data pipelines and physics-based transmission models, creating a comprehensive digital twin of campus ecosystems. By combining campus movement patterns, environmental conditions, and COVID-19 case reports through spatiotemporal data integration, CAMPUS-SAFE achieves 40% improvement in prediction accuracy compared to traditional epidemiological models. This sophisticated approach enables real-time monitoring and predictive analytics that transform campus health management from reactive to proactive.
Expected outcomes extend beyond individual campus safety to establish industry-wide standards for digital health monitoring systems in educational environments. The testbed delivers validated ROI metrics, implementation cost documentation, and optimal intervention policies that demonstrate measurable value. Results contribute to digital twin reference architectures for educational health monitoring, providing higher education institutions and public health agencies with evidence-based frameworks for pandemic preparedness and response optimization.
The CAMPUS-SAFE testbed aims to contribute to industry advancement in the following ways:
Reference Architecture Standards: Establishing a comprehensive digital twin reference architecture specifically designed for educational health monitoring systems, providing scalable frameworks that integrate agent-based modeling, real-time data pipelines, and physics-based transmission models for pandemic response in campus environments.
Pandemic Response Optimization: Developing validated best practices for campus pandemic response through systematic A/B testing of intervention policies, delivering evidence-based frameworks that demonstrate 30% risk reduction through optimized strategies including social distancing protocols, occupancy management, and environmental control measures.
Predictive Accuracy Validation: Advancing industry knowledge through rigorous validation of digital twin predictive capabilities, demonstrating 90% accuracy for weekly infection rate predictions and establishing measurement methodologies with 95% confidence intervals that set new standards for spatiotemporal epidemiological modeling.
Spatiotemporal Integration Methods: Contributing foundational methodologies for real-time spatiotemporal data integration in digital health systems, proving 40% improvement over traditional models and establishing technical approaches for combining campus movement patterns, environmental monitoring, and health data streams.
Implementation Cost Frameworks: Delivering validated ROI metrics and comprehensive implementation cost documentation including equipment, infrastructure, and expertise requirements, providing higher education institutions with evidence-based business cases for digital twin adoption in health monitoring applications.
Privacy-Compliant Data Systems: Establishing best practices for handling sensitive health data in digital twin environments while maintaining HIPAA compliance, addressing privacy concerns and ethical tracking considerations through validated frameworks that balance health monitoring effectiveness with data protection requirements.
Lead Developer
Contributing Technology Providers
- University of North Carolina
- NSF Spatiotemporal I/UCRC