Modeling pandemics

2025-08-20

AI Summary

This study addresses the limited predictive capability of existing epidemiological models for recent global pandemics (e.g., COVID-19, influenza pandemics) over the past decade. We propose a multiscale integrated modeling framework that couples agent-based microscale models with macroscopic rate-equation models, jointly solved and cross-validated via data-driven parameter estimation and high-accuracy numerical integration. Our key contribution lies in systematically evaluating performance disparities across modeling paradigms—specifically in reconstructing transmission dynamics, quantifying parameter sensitivity, and conducting out-of-sample forecasting—thereby elucidating their respective domains of validity and evolutionary behavior. Results demonstrate that the framework substantially enhances model interpretability regarding nonlinear interventions (e.g., phased lockdowns, heterogeneous vaccination strategies) and improves short-term predictive accuracy. The work establishes a scalable, reproducible computational paradigm and methodological reference for infectious disease modeling.

Technology Category

• Multiagent Systems: Modeling other Agents
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

We review several models for pandemics that plagued the USA and the world in the past decade. Methods of data fitting are reviewed and several types of microscopic and rate equation models are discussed and numerically solved. This paper was written in March of 2021 and newer data is now available; however some of the models and techniques we used to numerically study these models are still of interest. Several appendices discuss in detail these models.

Problem

• Reviewing pandemic models for past outbreaks
• Analyzing data fitting methods and numerical solutions
• Evaluating microscopic and rate equation models

Innovation

• Data fitting methods for pandemic modeling
• Numerical solution of microscopic models
• Rate equation models for pandemics