Unraveling the Geography of Infection Spread: Harnessing Super-Agents for Predictive Modeling

TL;DR

This paper introduces 'super-agents' to improve infectious disease modeling in urban areas by balancing detail and computational efficiency, using real mobility data and advanced geospatial tessellations.

Contribution

It presents a novel intermediate-level modeling approach that combines the accuracy of ABMs with the efficiency of compartmental models, utilizing super-agents and optimized tessellations.

Findings

Voronoi tessellations outperform Census Block Group tessellations

Hybrid approach balances accuracy and efficiency

Benchmarking shows key optimizations in urban disease modeling

Abstract

Our study presents an intermediate-level modeling approach that bridges the gap between complex Agent-Based Models (ABMs) and traditional compartmental models for infectious diseases. We introduce "super-agents" to simulate infection spread in cities, reducing computational complexity while retaining individual-level interactions. This approach leverages real-world mobility data and strategic geospatial tessellations for efficiency. Voronoi Diagram tessellations, based on specific street network locations, outperform standard Census Block Group tessellations, and a hybrid approach balances accuracy and efficiency. Benchmarking against existing ABMs highlights key optimizations. This research improves disease modeling in urban areas, aiding public health strategies in scenarios requiring geographic specificity and high computational efficiency.