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

TL;DR

COVID19-CBABM is a city-specific agent-based model that simulates virus spread considering human mobility and interactions at Points of Interest, aiding strategic decision-making during the pandemic.

Contribution

The paper introduces a novel city-based agent modeling framework that incorporates real mobility data and POI interactions for more realistic COVID-19 spread simulation.

Findings

Realistic simulation of virus transmission considering mobility patterns.

Model's portability allows easy extension to complex scenarios.

Supports strategic decision-making for health authorities.

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.