Abstract
Agent-based modeling is increasingly being used in computational epidemiology to characterize important behavioral dimensions, such as the heterogeneity of the individual responses to interventions, when studying the spread of a
disease. Existing agent-based simulation frameworks and platforms currently fall in one of two categories: those that can simulate millions of individuals with simple behaviors (e.g., based on simple state machines), and those that
consider more complex and social behaviors (e.g., agents that act according to their own agenda and preferences, and deliberate about norm compliance) but, due to the computational complexity of reasoning involved, have limited
scalability. In this paper, we present a novel framework that enables large-scale distributed epidemic simulations with
complex behaving social agents whose decisions are based on a variety of concepts and internal attitudes such as sense, knowledge, preferences, norms, and plans. The proposed framework supports simulations with millions of such
agents that can individually deliberate about their own knowledge, goals and preferences, and can adapt their behavior based on other agents’ behaviors and on their attitude towards complying with norms. We showcase the applicability
and scalability of the proposed framework by developing a model of the spread of COVID-19 in the US state of Virginia. Results illustrate that the framework can be effectively employed to simulate disease spreading with millions of complex
behaving agents and investigate behavioral interventions over a period of time of months.
disease. Existing agent-based simulation frameworks and platforms currently fall in one of two categories: those that can simulate millions of individuals with simple behaviors (e.g., based on simple state machines), and those that
consider more complex and social behaviors (e.g., agents that act according to their own agenda and preferences, and deliberate about norm compliance) but, due to the computational complexity of reasoning involved, have limited
scalability. In this paper, we present a novel framework that enables large-scale distributed epidemic simulations with
complex behaving social agents whose decisions are based on a variety of concepts and internal attitudes such as sense, knowledge, preferences, norms, and plans. The proposed framework supports simulations with millions of such
agents that can individually deliberate about their own knowledge, goals and preferences, and can adapt their behavior based on other agents’ behaviors and on their attitude towards complying with norms. We showcase the applicability
and scalability of the proposed framework by developing a model of the spread of COVID-19 in the US state of Virginia. Results illustrate that the framework can be effectively employed to simulate disease spreading with millions of complex
behaving agents and investigate behavioral interventions over a period of time of months.
| Original language | English |
|---|---|
| Pages (from-to) | 1183-1211 |
| Number of pages | 26 |
| Journal | SIMULATION |
| Volume | 99 |
| Issue number | 12 |
| Early online date | 8 Aug 2023 |
| DOIs | |
| Publication status | E-pub ahead of print - 8 Aug 2023 |
Bibliographical note
AcknowledgementsWe thank Cuebiq; mobility data is provided by Cuebiq, a location intelligence and measurement platform. Through its Data for Good program, Cuebiq provides access to aggregated mobility data for academic research and humanitarian initiatives. This first-party data is collected from anonymized users who have opted-in to provide access to their location data anonymously, through a GDPR and CCPA compliant framework. To further preserve privacy, portions of the data are aggregated to the census-block group level. For the purpose of open access, the authors have applied a Creative Commons Attribution (CC BY) licence to any Author
Accepted Manuscript version arising from this submission.
Keywords
- Agent-based modeling
- Social simulation
- Synthetic population
- Computational epidemiology
- COVID-19
- PanSim
- Sim2APL