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

TL;DR

This study introduces "super-agents" to simulate infection spread in cities, reducing computational complexity while retaining individual-level interactions in Agent-Based Models, and leverages real-world mobility data and strategic geospatial tessellations for efficiency.

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.

Figures (12)

• Figure 1: Diagram illustrating the fundamental components of our customized ABM model of Pandemic - the Agent-in-Cell (AIC) model.
• Figure 2: Schematic view of the infection model.
• Figure 3: The infection model for trips. (a) Building location and geometry. (b) Estimated congestion. (c) Physical transmission models (base on agrawal2021probability)
• Figure 4: This map of Seattle depicts census tracts with red boundaries and census block groups with gray boundaries (Source: seattleCensusTractMap2010).
• Figure 5: Seattle map. (a): Voronoi Diagram depicting tessellation for shopping centers; (b): Voronoi Diagram showcasing tessellation for schools; (c): K-means clustering generating 242 distinct cells.