Using Agent-Based Simulation to Investigate Behavioral Interventions in a Pandemic Simulating Behavioral Interventions in a Pandemic

Jan de Mooij; Davide Dell’Anna; Parantapa Bhattacharya; Mehdi Dastani; Brian Logan; Samarth Swarup

Using Agent-Based Simulation to Investigate Behavioral Interventions in a Pandemic Simulating Behavioral Interventions in a Pandemic

Jan de Mooij^*
, Davide Dell’Anna
, Parantapa Bhattacharya
, Mehdi Dastani
, Brian Logan
, Samarth Swarup

^*Corresponding author for this work

University of Virginia

Research output: Contribution to journal › Conference article › Academic › peer-review

Abstract

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.

Original language	English
Number of pages	8
Journal	CEUR Workshop Proceedings
Volume	3182
Publication status	Published - 2022
Event	1st Workshop on Agent-Based Modeling and Policy-Making, AMPM 2021 - Virtual, Vulnius, Lithuania Duration: 8 Dec 2021 → …

Bibliographical note

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.

Funding

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.

Keywords

Agent-based Computational Epidemiology
Agent-based Modeling
Belief-Desire-Intention
Complex Social Simulation
Multi-agent Simulation
Normative Reasoning
Policy Evaluation
Synthetic Population

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https://ceur-ws.org/Vol-3182Licence: CC BY

Cite this

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title = "Using Agent-Based Simulation to Investigate Behavioral Interventions in a Pandemic Simulating Behavioral Interventions in a Pandemic",

abstract = "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.",

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author = "\{de Mooij\}, Jan and Davide Dell{\textquoteright}Anna and Parantapa Bhattacharya and Mehdi Dastani and Brian Logan and Samarth Swarup",

note = "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: {\textcopyright} 2021 Copyright for this paper by its authors.; 1st Workshop on Agent-Based Modeling and Policy-Making, AMPM 2021 ; Conference date: 08-12-2021",

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language = "English",

volume = "3182",

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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 - https://www.scopus.com/pages/publications/85137105830

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 -

Using Agent-Based Simulation to Investigate Behavioral Interventions in a Pandemic Simulating Behavioral Interventions in a Pandemic

Abstract

Bibliographical note

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Keywords

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