TY - JOUR
T1 - Using Agent-Based Simulation to Investigate Behavioral Interventions in a Pandemic Simulating Behavioral Interventions in a Pandemic
AU - de Mooij, Jan
AU - Dell’Anna, Davide
AU - Bhattacharya, Parantapa
AU - Dastani, Mehdi
AU - Logan, Brian
AU - Swarup, Samarth
N1 - Funding Information:
We 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 US Census block group level. PB and SS were supported in part by NSF Expeditions in Computing Grant CCF-1918656.
Publisher Copyright:
© 2021 Copyright for this paper by its authors.
PY - 2022
Y1 - 2022
N2 - Simulation is a useful tool for evaluating behavioral interventions when the adoption rate among a population is uncertain. Individual agent models are often prohibitively expensive, but, unlike stochastic models, allow studying compliance heterogeneity. In this paper we demonstrate the feasibility of evaluating behavioral intervention policies using large-scale data-driven agent-based simulations. We explain how the simulation is calibrated with respect to real-world data, and demonstrate the utility of our approach by studying the effectiveness of interventions used in Virginia in early 2020 through counterfactual simulations.
AB - Simulation is a useful tool for evaluating behavioral interventions when the adoption rate among a population is uncertain. Individual agent models are often prohibitively expensive, but, unlike stochastic models, allow studying compliance heterogeneity. In this paper we demonstrate the feasibility of evaluating behavioral intervention policies using large-scale data-driven agent-based simulations. We explain how the simulation is calibrated with respect to real-world data, and demonstrate the utility of our approach by studying the effectiveness of interventions used in Virginia in early 2020 through counterfactual simulations.
KW - Agent-based Computational Epidemiology
KW - Agent-based Modeling
KW - Belief-Desire-Intention
KW - Complex Social Simulation
KW - Multi-agent Simulation
KW - Normative Reasoning
KW - Policy Evaluation
KW - Synthetic Population
UR - http://www.scopus.com/inward/record.url?scp=85137105830&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85137105830
SN - 1613-0073
VL - 3182
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
T2 - 1st Workshop on Agent-Based Modeling and Policy-Making, AMPM 2021
Y2 - 8 December 2021
ER -