  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.
