TY - GEN
T1 - Quantifying the Effects of Norms on COVID-19 Cases Using an Agent-Based Simulation
AU - de Mooij, Jan
AU - Dell'anna, Davide
AU - Bhattacharya, Parantapa
AU - Dastani, Mehdi
AU - Logan, Brian
AU - Swarup, Samarth
N1 - Funding Information:
PB and SS were supported in part by NSF Expeditions in Computing Grant CCF-1918656 and DTRA subcontract/ARA S-D00189-15-TO-01-UVA.
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 census-block group level. PB and SS were supported in part by NSF Expeditions in Computing Grant CCF-1918656 and DTRA subcontract/ARA S-D00189-15-TO-01-UVA.
Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022/1/15
Y1 - 2022/1/15
N2 - Modelling social phenomena in large-scale agent-based simulations has long been a challenge due to the computational cost of incorporating agents whose behaviors are determined by reasoning about their internal attitudes and external factors. However, COVID-19 has brought the urgency of doing this to the fore, as, in the absence of viable pharmaceutical interventions, the progression of the pandemic has primarily been driven by behaviors and behavioral interventions. In this paper, we address this problem by developing a large-scale data-driven agent-based simulation model where individual agents reason about their beliefs, objectives, trust in government, and the norms imposed by the government. These internal and external attitudes are based on actual data concerning daily activities of individuals, their political orientation, and norms being enforced in the US state of Virginia. Our model is calibrated using mobility and COVID-19 case data. We show the utility of our model by quantifying the benefits of the various behavioral interventions through counterfactual runs of our calibrated simulation.
AB - Modelling social phenomena in large-scale agent-based simulations has long been a challenge due to the computational cost of incorporating agents whose behaviors are determined by reasoning about their internal attitudes and external factors. However, COVID-19 has brought the urgency of doing this to the fore, as, in the absence of viable pharmaceutical interventions, the progression of the pandemic has primarily been driven by behaviors and behavioral interventions. In this paper, we address this problem by developing a large-scale data-driven agent-based simulation model where individual agents reason about their beliefs, objectives, trust in government, and the norms imposed by the government. These internal and external attitudes are based on actual data concerning daily activities of individuals, their political orientation, and norms being enforced in the US state of Virginia. Our model is calibrated using mobility and COVID-19 case data. We show the utility of our model by quantifying the benefits of the various behavioral interventions through counterfactual runs of our calibrated simulation.
KW - Computational epidemiology
KW - Large-scale social simulation
KW - Norm reasoning agents
UR - http://www.scopus.com/inward/record.url?scp=85124133030&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-94548-0_8
DO - 10.1007/978-3-030-94548-0_8
M3 - Conference contribution
SN - 978-3-030-94547-3
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 99
EP - 112
BT - Multi-Agent-Based Simulation XXII. MABS 2021.
A2 - Van Dam, Koen H.
A2 - Verstaevel, Nicolas
PB - Springer
T2 - The 22nd International Workshop on Multi-Agent-Based Simulation
Y2 - 4 May 2021 through 4 May 2021
ER -