Agent-Based Computational Epidemiological Modeling

Abstract

The study of epidemics is useful for not only understanding outbreaks and trying to limit their adverse effects, but also because epidemics are related to social phenomena such as government instability, crime, poverty, and inequality. One approach for studying epidemics is to simulate their spread through populations. In this work, we describe an integrated multi-dimensional approach to epidemic simulation, which encompasses: (1) a theoretical framework for simulation and analysis; (2) synthetic population (digital twin) generation; (3) (social contact) network construction methods from synthetic populations, (4) stylized network construction methods; and (5) simulation of the evolution of a virus or disease through a social network. We describe these aspects and end with a short discussion on simulation results that inform public policy.

A framework for modeling human behavior in large-scale agent-based epidemic simulations

“…Bhatele et al 44 have also demonstrated epidemic simulation scaling up to the size of the US population using EpiSimdemics. 42,46 The simulator was heavily optimized for the particular application and architecture, which allowed it to compute each simulated day in 57.8 ms on 655,360 cores of the Blue Waters supercomputer. The effect of NPIs was studied using a trigger-based approach, i.e., executing a predetermined function when certain conditions of a trigger were satisfied, e.g., to change the disease state or disease mitigation behavior of an agent.…”

Mobile human ad hoc networks: A communication engineering viewpoint on interhuman airborne pathogen transmission

“…In experimental studies, these data are obtained by oral surveys or exploiting wireless sensor network technology [16] . On the other hand, agent-based modeling (ABM) which uses agents possessing individual contact patterns and behaviors is applied in epidemiology [30] . Although ABM has the advantage of fine-grained results, it requires high computational power for the simulation of millions of agents, which makes it challenging [30] .…”

“…On the other hand, agent-based modeling (ABM) which uses agents possessing individual contact patterns and behaviors is applied in epidemiology [30] . Although ABM has the advantage of fine-grained results, it requires high computational power for the simulation of millions of agents, which makes it challenging [30] . Therefore, the standard SIR model is adopted in this paper.…”

Leader‐following consensus and qualitative analysis of a new multi‐agent‐based epidemic model

“…Building on the existing literature and shortening its gaps, the authors aim to develop a multi-agent-based model in this study. While some studies, such as [24][25][26], have used MASs in their research model and considered a person as an agent, this research aims to represent each region (e.g. cities in a country) by an agent that interacts with neighbouring agents to localize conventional epidemic models.…”