Analyzing the impact of modeling choices and assumptions in compartmental epidemiological models

Nutaro, James J.; Pullum, Laura L.; Ramanathan, Arvind; Ozmen, Ozgur

doi:10.1177/0037549716640877

Title: Analyzing the impact of modeling choices and assumptions in compartmental epidemiological models

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Abstract

In this study, computational models have become increasingly used as part of modeling, predicting, and understanding how infectious diseases spread within large populations. These models can be broadly classified into differential equation-based models (EBM) and agent-based models (ABM). Both types of models are central in aiding public health officials design intervention strategies in case of large epidemic outbreaks. We examine these models in the context of illuminating their hidden assumptions and the impact these may have on the model outcomes. Very few ABM/EBMs are evaluated for their suitability to address a particular public health concern, and drawing relevant conclusions about their suitability requires reliable and relevant information regarding the different modeling strategies and associated assumptions. Hence, there is a need to determine how the different modeling strategies, choices of various parameters, and the resolution of information for EBMs and ABMs affect outcomes, including predictions of disease spread. In this study, we present a quantitative analysis of how the selection of model types (i.e., EBM vs. ABM), the underlying assumptions that are enforced by model types to model the disease propagation process, and the choice of time advance (continuous vs. discrete) affect the overall outcomes of modeling disease spread. Our studymore »« less

Authors:

Nutaro, James J. ^[1]; Pullum, Laura L. ^[1]; Ramanathan, Arvind ^[1]; Ozmen, Ozgur ^[1]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Publication Date:: Sun May 01 00:00:00 EDT 2016

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1250405

Grant/Contract Number:: AC05-00OR22725

Resource Type:: Accepted Manuscript

Journal Name:: Simulation

Additional Journal Information:: Journal Volume: 92; Journal Issue: 5; Journal ID: ISSN 0037-5497

Publisher:: SAGE

Country of Publication:: United States

Language:: English

Subject:: 60 APPLIED LIFE SCIENCES; susceptible-infected-recovered; SIR; epidemiology; agent-based; event-based; equation-based models

Citation Formats


                    Nutaro, James J., Pullum, Laura L., Ramanathan, Arvind, and Ozmen, Ozgur. Analyzing the impact of modeling choices and assumptions in compartmental epidemiological models.  United States: N. p., 2016. 
Web.  doi:10.1177/0037549716640877.

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                    Nutaro, James J., Pullum, Laura L., Ramanathan, Arvind, & Ozmen, Ozgur. Analyzing the impact of modeling choices and assumptions in compartmental epidemiological models.  United States.  https://doi.org/10.1177/0037549716640877

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                    Nutaro, James J., Pullum, Laura L., Ramanathan, Arvind, and Ozmen, Ozgur. Sun .  
"Analyzing the impact of modeling choices and assumptions in compartmental epidemiological models".  United States.  https://doi.org/10.1177/0037549716640877.  https://www.osti.gov/servlets/purl/1250405.

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@article{osti_1250405,

  title        = {Analyzing the impact of modeling choices and assumptions in compartmental epidemiological models},

  author       = {Nutaro, James J. and Pullum, Laura L. and Ramanathan, Arvind and Ozmen, Ozgur},

  abstractNote = {In this study, computational models have become increasingly used as part of modeling, predicting, and understanding how infectious diseases spread within large populations. These models can be broadly classified into differential equation-based models (EBM) and agent-based models (ABM). Both types of models are central in aiding public health officials design intervention strategies in case of large epidemic outbreaks. We examine these models in the context of illuminating their hidden assumptions and the impact these may have on the model outcomes. Very few ABM/EBMs are evaluated for their suitability to address a particular public health concern, and drawing relevant conclusions about their suitability requires reliable and relevant information regarding the different modeling strategies and associated assumptions. Hence, there is a need to determine how the different modeling strategies, choices of various parameters, and the resolution of information for EBMs and ABMs affect outcomes, including predictions of disease spread. In this study, we present a quantitative analysis of how the selection of model types (i.e., EBM vs. ABM), the underlying assumptions that are enforced by model types to model the disease propagation process, and the choice of time advance (continuous vs. discrete) affect the overall outcomes of modeling disease spread. Our study reveals that the magnitude and velocity of the simulated epidemic depends critically on the selection of modeling principles, various assumptions of disease process, and the choice of time advance.},

  doi          = {10.1177/0037549716640877},

  journal      = {Simulation},

  number       = 5,

  volume       = 92,

  place        = {United States},

  year         = {Sun May 01 00:00:00 EDT 2016},

  month        = {Sun May 01 00:00:00 EDT 2016}

}

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