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COVID19-CBABM: A City-Based Agent Based Disease Spread Modeling Framework

Raunak Sarbajna, Karima Elgarroussi, Hoang D Vo, Jianyuan Ni, Christoph F. Eick

TL;DR

COVID19-CBABM introduces a city-scale, POI-aware agent-based framework for COVID-19 spread by fusing SEIHRD dynamics with realistic mobility data. The model uses two agent types (Individuals and POIs) within a geospatial ABM powered by Mesa-Geo, and it leverages SafeGraph and CDC datasets to extract parameters and validate against city-level data. Key contributions include automated SafeGraph parameter extraction, a portable city-focused architecture, and scenario-based policy evaluation capabilities that reflect local mobility patterns. The work demonstrates the approach on NYC boroughs (notably Brooklyn and the Bronx) and provides a pathway for governments to assess targeted interventions using city-specific mobility behavior, with future directions toward comparative validation with Covasim and extended citizen trajectories.

Abstract

In response to the ongoing pandemic and health emergency of COVID-19, several models have been used to understand the dynamics of virus spread. Some employ mathematical models like the compartmental SEIHRD approach and others rely on agent-based modeling (ABM). In this paper, a new city-based agent-based modeling approach called COVID19-CBABM is introduced. It considers not only the transmission mechanism simulated by the SEHIRD compartments but also models people movements and their interactions with their surroundings, particularly their interactions at different types of Points of Interest (POI), such as supermarkets. Through the development of knowledge extraction procedures for Safegraph data, our approach simulates realistic conditions based on spatial patterns and infection conditions considering locations where people spend their time in a given city. Our model was implemented in Python using the Mesa-Geo framework. COVID19-CBABM is portable and can be easily extended by adding more complicated scenarios. Therefore, it is a useful tool to assist the government and health authorities in evaluating strategic decisions and actions efficiently against this epidemic, using the unique mobility patterns of each city.

COVID19-CBABM: A City-Based Agent Based Disease Spread Modeling Framework

TL;DR

COVID19-CBABM introduces a city-scale, POI-aware agent-based framework for COVID-19 spread by fusing SEIHRD dynamics with realistic mobility data. The model uses two agent types (Individuals and POIs) within a geospatial ABM powered by Mesa-Geo, and it leverages SafeGraph and CDC datasets to extract parameters and validate against city-level data. Key contributions include automated SafeGraph parameter extraction, a portable city-focused architecture, and scenario-based policy evaluation capabilities that reflect local mobility patterns. The work demonstrates the approach on NYC boroughs (notably Brooklyn and the Bronx) and provides a pathway for governments to assess targeted interventions using city-specific mobility behavior, with future directions toward comparative validation with Covasim and extended citizen trajectories.

Abstract

In response to the ongoing pandemic and health emergency of COVID-19, several models have been used to understand the dynamics of virus spread. Some employ mathematical models like the compartmental SEIHRD approach and others rely on agent-based modeling (ABM). In this paper, a new city-based agent-based modeling approach called COVID19-CBABM is introduced. It considers not only the transmission mechanism simulated by the SEHIRD compartments but also models people movements and their interactions with their surroundings, particularly their interactions at different types of Points of Interest (POI), such as supermarkets. Through the development of knowledge extraction procedures for Safegraph data, our approach simulates realistic conditions based on spatial patterns and infection conditions considering locations where people spend their time in a given city. Our model was implemented in Python using the Mesa-Geo framework. COVID19-CBABM is portable and can be easily extended by adding more complicated scenarios. Therefore, it is a useful tool to assist the government and health authorities in evaluating strategic decisions and actions efficiently against this epidemic, using the unique mobility patterns of each city.
Paper Structure (24 sections, 1 equation, 4 figures, 1 table)

This paper contains 24 sections, 1 equation, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. SEIR-CABM Model Overview
  4. SEIR Models
  5. Model Architecture
  6. Simulation of Time
  7. Simulation of Space
  8. Agent Categories
  9. Individual Agents
  10. Points of Interest Agents
  11. Movement Rules
  12. Parameter Selection
  13. Initial Values for POI
  14. Initial Values for Individual Agents
  15. Model Portability
  16. ...and 9 more sections

Figures (4)

  • Figure 1: Model architecture for COVID-CBABM
  • Figure 2: COVID-CBABM Model State on Day 1
  • Figure 3: COVID-CBABM Model State on Day 15
  • Figure 4: Colour Labels for maps