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

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Title: Analyzing the impact of modeling choices and assumptions in compartmental epidemiological models.
Authors: Özmen, Özgür1, Nutaro, James J.2, Pullum, Laura L.2 pullumll@ornl.gov, Ramanathan, Arvind1
Source: Simulation. May2016, Vol. 92 Issue 5, p459-472. 14p.
Subjects: Epidemiological models, Epidemiology methodology, Reproducible research, Simulation methods & models, Couplings (Gearing)
Abstract: Computational disease spread models can be broadly classified into differential equation-based models (EBMs) and agent-based models (ABMs). We examine these models in the context of illuminating their hidden assumptions and the impact these may have on the model outcomes. Drawing relevant conclusions about the usability of a model requires reliable information regarding its modeling strategy and its associated assumptions. Hence, we aim to provide clear guidelines on the development of these models and delineate important modeling choices that cause the differences between the model outputs. In this study, we present a quantitative analysis of how the choice of model trajectories and temporal resolution (continuous versus discrete-event models), coupling between agents (instantaneous versus delayed interactions), and progress of patients from one stage of the disease to the next 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. In order to inform public health officials and improve reproducibility, these initial decisions of modelers should be carefully considered and recorded when building and documenting an ABM. [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.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: Analyzing the impact of modeling choices and assumptions in compartmental epidemiological models.
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  Data: <searchLink fieldCode="JN" term="%22Simulation%22">Simulation</searchLink>. May2016, Vol. 92 Issue 5, p459-472. 14p.
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  Data: <searchLink fieldCode="DE" term="%22Epidemiological+models%22">Epidemiological models</searchLink><br /><searchLink fieldCode="DE" term="%22Epidemiology+methodology%22">Epidemiology methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Reproducible+research%22">Reproducible research</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink><br /><searchLink fieldCode="DE" term="%22Couplings+%28Gearing%29%22">Couplings (Gearing)</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Computational disease spread models can be broadly classified into differential equation-based models (EBMs) and agent-based models (ABMs). We examine these models in the context of illuminating their hidden assumptions and the impact these may have on the model outcomes. Drawing relevant conclusions about the usability of a model requires reliable information regarding its modeling strategy and its associated assumptions. Hence, we aim to provide clear guidelines on the development of these models and delineate important modeling choices that cause the differences between the model outputs. In this study, we present a quantitative analysis of how the choice of model trajectories and temporal resolution (continuous versus discrete-event models), coupling between agents (instantaneous versus delayed interactions), and progress of patients from one stage of the disease to the next 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. In order to inform public health officials and improve reproducibility, these initial decisions of modelers should be carefully considered and recorded when building and documenting an ABM. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  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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        Value: 10.1177/0037549716640877
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      – Code: eng
        Text: English
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        PageCount: 14
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      – SubjectFull: Epidemiological models
        Type: general
      – SubjectFull: Epidemiology methodology
        Type: general
      – SubjectFull: Reproducible research
        Type: general
      – SubjectFull: Simulation methods & models
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      – SubjectFull: Couplings (Gearing)
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      – TitleFull: Analyzing the impact of modeling choices and assumptions in compartmental epidemiological models.
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              M: 05
              Text: May2016
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              Y: 2016
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