Design and Implementation of a City-based Model for Simulating COVID-19 Spread

Since 2019, COVID-19 has challenged health worldwide. To better understand the disease, scientists have developed agent-based and other models to simulate the spread of COVD-19. Simulations with agent-based models have become popular in recent years due to the development of new technologies and novel programming resources. Agent-based models create agents and allow agents to interact with each other based on a set of rules. Thanks to simulated results of agent-based models, scientists can better understand the spread of the disease and evaluate the effectiveness of particular measures against the spread of COVID-19. This thesis designed and implemented COVID19-CBABM, a city-based and agent-based model, to simulate the spread of COVID-19. The model simulates the spread of the pandemic disease inside a particular city based on given information about its population and its point-of-interests which are visited by people living in this city. COVID19-CBABM has two types of agents: Human agents and point-of-interest agents. Human agents mimic the movements and behaviors of citizens while point-of-interest agents model houses, schools, hospitals, offices, and other places in the city. COVID19-CBABM also reuses the SEIHRD framework to model how COVID-19 is transmitted among people. The model was initially developed to simulate the COVID-19 spread in New York City. We employed the Mesa framework to create an agent-based model in python 3.7. Mesa uses built-in components and customized classes to generate the agents in COVID19-CBABM. To evaluate the COVID19-CBABM model, we compared the simulated results with the actual data collected in New York City in August 2021. We also analyzed to which extent the developed model can be reused to simulate virus transmission in other cities, such as Houston City. One challenge of such a reuse is to adapt model parameters that were estimated based on New York City data, to match the specific characteristics of Houston. City

Name thesis:degree_name

Master of Science

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Houston

Year dc:date.issued

2022

Author dc:creator

• Vo, Hoang Duc

Advisor dc:contributor.advisor

• Eick, Christoph F.

Committee members dc:contributor.committeemember

• Laszka, Aron
• Gilbert, Lauren R.

• ABM
• SEIHRD
• COVID-19
• Agent-based model
• New York

Statement dc:rights

• The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/10657/13139

OAI identifier oai:identifier

oai:uh-ir.tdl.org:10657/13139