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

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Názov: A framework for modeling human behavior in large-scale agent-based epidemic simulations.
Autori: de Mooij, Jan, Bhattacharya, Parantapa, Dell'Anna, Davide, Dastani, Mehdi, Logan, Brian, Swarup, Samarth
Zdroj: Simulation; Dec2023, Vol. 99 Issue 12, p1183-1211, 29p
Predmety: HUMAN behavior models, COVID-19 pandemic, COMPUTATIONAL neuroscience, INFECTIOUS disease transmission, SIMPLE machines, COMPUTATIONAL complexity
Geografický termín: VIRGINIA
Abstrakt: 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 toward 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. [ABSTRACT FROM AUTHOR]
Copyright of Simulation is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A framework for modeling human behavior in large-scale agent-based epidemic simulations.
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  Data: <searchLink fieldCode="AR" term="%22de+Mooij%2C+Jan%22">de Mooij, Jan</searchLink><br /><searchLink fieldCode="AR" term="%22Bhattacharya%2C+Parantapa%22">Bhattacharya, Parantapa</searchLink><br /><searchLink fieldCode="AR" term="%22Dell'Anna%2C+Davide%22">Dell'Anna, Davide</searchLink><br /><searchLink fieldCode="AR" term="%22Dastani%2C+Mehdi%22">Dastani, Mehdi</searchLink><br /><searchLink fieldCode="AR" term="%22Logan%2C+Brian%22">Logan, Brian</searchLink><br /><searchLink fieldCode="AR" term="%22Swarup%2C+Samarth%22">Swarup, Samarth</searchLink>
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  Data: Simulation; Dec2023, Vol. 99 Issue 12, p1183-1211, 29p
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– Name: Abstract
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  Data: 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 toward 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. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Simulation is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Dec2023
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