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

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

doi:10.1177/00375497231184898

Cited by 11 publications

(3 citation statements)

References 110 publications

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“…In this context, toward the application of these tools outside the original purposes, these frameworks can be used to guide the process of prompting and fine-tuning LLMs and help their adaptation to new contexts. [34][35][36] Future work will explore this, implementing a comparison of which ABM frameworks produce better results when combined with LLMs, and under what conditions.…”

Section: Discussionmentioning

confidence: 99%

Modeler in a box: how can large language models aid in the simulation modeling process?

Frydenlund,

Martínez,

Padilla

et al. 2024

SIMULATION

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We examine the potential of prompting a large language model-based chatbot, ChatGPT, to generate functional simulation model code from a prose-based narrative. The simple narrative describes how the mode of transportation for elementary school students changed due to the COVID-19 pandemic and related factors, including a lack of available bus drivers, lack of mask enforcement on buses, and inclement weather. We document the process of providing this narrative to ChatGPT and further prompting the chatbot to generate model code to represent the narrative and to make it executable. We test ChatGPT’s ability to use prose descriptions of a phenomenon to generate simulation models using three paradigms: discrete event system, system dynamics, and agent-based modeling. Our findings reveal that ChatGPT could not produce concise or executable models, facing higher difficulty when asked to do so in programming languages it was less familiar with. This analysis underscores the strengths and limitations of the current state of this technology for modeling and simulation. Furthermore, we propose how future advancements in Large Language Models may aid the simulation modeling process, increasing transparency and participation in multidisciplinary team efforts.

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Section: Discussionmentioning

confidence: 99%

Modeler in a box: how can large language models aid in the simulation modeling process?

Frydenlund,

Martínez,

Padilla

et al. 2024

SIMULATION

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“…28 Although ABM models can capture a large amount of detail, which is typically not achievable by other simulation methods, their computational expense limits their scalability. 29,30 De Mooij et al 31 have recently developed an ABM modeling framework based on deliberative agents to circumvent this problem, successfully modeling a COVID-19 epidemic with millions of agents exhibiting complex behaviors such as habits, fatigue, and political preferences.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamical simulation of the Syrian refugee crisis: quantifying the driving factors of forced migration

Diedrichs,

Hill,

et al. 2023

SIMULATION

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With over 21 million refugees at the end of 2021, forced migration across national borders has become a global reality and a major international policy issue. This paper proposes a dynamical model based on the Syrian Refugee Crisis to quantify the factors that influence a refugee’s decision to migrate and the destination choice. The model simulates refugee migrations in space and time, from the moment they flee their country of origin until they are granted asylum in a host country or lose refugee status by attrition or repatriation. Migration is driven by comparative attractiveness scores based on differences in quality of life, political stability, societal violence, cultural familiarity, and distance between countries, while accounting for risk aversion and psychological inertia. By comparing simulation results to United Nations High Commissioner for Refugees (UNHCR) data, we determine weight parameters that quantify the relative importance of each attribute in inducing a migration flow. The model is a computationally efficient forecasting and ex-post analysis tool, providing insight into the dynamics of refugee flow and effect of immigration policies.

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“…Coelho and Codeco [23] used Bayesian inference to model vaccination behavior as a function of individual perception of vaccine safety [23]. Mooij et al [24] developed a large-scale agent-based epidemic model that uses mobility data to calibrate the behavior of agents. Similarly, Del Valle et al [22,25] used an agent-based model to showcase the impact of school closure and fear-based home isolation during a pandemic.…”

Section: Introductionmentioning

confidence: 99%

Mathematical assessment of the role of human behavior changes on SARS-CoV-2 transmission dynamics

Pant,

Safdar,

Santillana

et al. 2024

Preprint

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The COVID-19 pandemic has not only presented a major global public health and socio-economic crisis, but has also significantly impacted human behavior towards adherence (or lack thereof) to public health intervention and mitigation measures implemented in communities worldwide. The dynamic nature of the pandemic has prompted extensive changes in individual and collective behaviors towards the pandemic. This study is based on the use of mathematical modeling approaches to assess the extent to which SARS-CoV-2 transmission dynamics is impacted by population-level changes of human behavior due to factors such as (a) the severity of transmission (such as disease-induced mortality and level of symptomatic transmission), (b) fatigue due to the implementation of mitigation interventions measures (e.g., lockdowns) over a long (extended) period of time, (c) social peer-pressure, among others. A novel behavior-epidemiology model, which takes the form of a deterministic system of nonlinear differential equations, is developed and fitted using observed cumulative SARS-CoV-2 mortality data during the first wave in the United States. Rigorous analysis of the model shows that its disease-free equilibrium is locally-asymptotically stable whenever a certain epidemiological threshold, known as the control reproduction number (denoted by R_C) is less than one, and the disease persists (i.e., causes significant outbreak or outbreaks) if the threshold exceeds one. The model fits the observed data, as well as makes a more accurate prediction of the observed daily SARS-CoV-2 mortality during the first wave (March 2020 -June 2020), in comparison to the equivalent model which does not explicitly account for changes in human behavior. Of the various metrics for human behavior changes during the pandemic considered in this study, it is shown that behavior changes due to the level of SARS-CoV-2 mortality and symptomatic transmission were more influential (while behavioral changes due to the level of fatigue to interventions in the community was of marginal impact). It is shown that an increase in the proportion of exposed individuals who become asymptomatically-infectious at the end of the exposed period (represented by a parameter r) can lead to an increase (decrease) in the control reproduction number (R_C) if the effective contact rate of asymptomatic individuals is higher (lower) than that of symptomatic individuals. The study identifies two threshold values of the parameter r that maximize the cumulative and daily SARS-CoV-2 mortality, respectively, during the first wave. Furthermore, it is shown that, as the value of the proportion r increases from 0 to 1, the rate at which susceptible non-adherent individuals change their behavior to strictly adhere to public health interventions decreases. Hence, this study suggests that, as more newly-infected individuals become asymptomatically-infectious, the level of positive behavior change, as well as disease severity, hospitalizations and disease-induced mortality in the community can be expected to significantly decrease (while new cases may rise, particularly if asymptomatic individuals have higher contact rate, in comparison to symptomatic individuals).

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

Cited by 11 publications

References 110 publications

Modeler in a box: how can large language models aid in the simulation modeling process?

Modeler in a box: how can large language models aid in the simulation modeling process?

Dynamical simulation of the Syrian refugee crisis: quantifying the driving factors of forced migration

Mathematical assessment of the role of human behavior changes on SARS-CoV-2 transmission dynamics

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