Using Agent-Based Simulator to Assess Interventions Against COVID-19 in a Small Community Generated from Map Data

ABSTRACT

During the COVID-19 pandemic, governments have struggled to devise strategies to slow down the spread of the virus. This struggle happens because pandemics are complex scenarios with many unknown variables. In this context, simulated models are used to evaluate strategies for mitigating this and future pandemics. This paper proposes a simulator that analyses small communities by using real geographical data to model the road interactions and the agent's behaviors. Our simulator consists of three different modules Environment, Mobility, and Infection module. The environment module recreates an area based on map data, including houses, restaurants, and roads. The mobility module determines the agents' movement in the map based on their work schedule and needs, such as eating at restaurants, doing groceries, and going to work. The infection module simulates four cases of infection on the road, at home, at a building, and off the map. We simulate the surrounding areas of the University of Tsukuba and design three intervention strategies, comparing them to a scenario without any intervention. The interventions are 1) PCR testing and self-isolation if positive;2) applying lockdown measures to restaurants and barbershops 3) closing grocery stores and restaurants and providing delivery instead. For all scenarios, we observe two areas where most infection happens hubs, where people from different occupations can meet (e.g., restaurants), and non-hubs, where people with the same occupation meet (e.g., offices). The simulations show that most interventions reduce the total number of infected agents by a large margin. We observed that interventions targeting hubs (2-4) did not impact the infection at non-hubs. In addition, the intervention targeting people's behavior (1) ended up creating a cluster at the testing center. © 2022 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved