University Operations During a Pandemic: A Flexible Decision Analysis Toolkit Article Swipe
Himanshu Kharkwal
,
Dakota Olson
,
Jiali Huang
,
Abhiraj Mohan
,
Ankur Mani
,
Jaideep Srivastava
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1145/3460125
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1145/3460125
Modeling infection spread during pandemics is not new, with models using past data to tune simulation parameters for predictions. These help in understanding of the healthcare burden posed by a pandemic and responding accordingly. However, the problem of how college/university campuses should function during a pandemic is new for the following reasons: (i) social contact in colleges are structured and can be engineered for chosen objectives; (ii) the last pandemic to cause such societal disruption was more than 100 years ago, when higher education was not a critical part of society; (iii) not much was known about causes of pandemics, and hence effective ways of safe operations were not known; and (iv) today with distance learning, remote operation of an academic institution is possible. As one of the first to address this problem, our approach is unique in presenting a flexible simulation system, containing a suite of model libraries, one for each major component. The system integrates agent-based modeling and the stochastic network approach, and models the interactions among individual entities (e.g., students, instructors, classrooms, residences) in great detail. For each decision to be made, the system can be used to predict the impact of various choices, and thus enables the administrator to make informed decisions. Although current approaches are good for infection modeling, they lack accuracy in social contact modeling. Our agent-based modeling approach, combined with ideas from Network Science, presents a novel approach to contact modeling. A detailed case study of the University of Minnesota’s Sunrise Plan is presented. For each decision made, its impact was assessed, and results were used to get a measure of confidence. We believe that this flexible tool can be a valuable asset for various kinds of organizations to assess their infection risks in pandemic-time operations, including middle and high schools, factories, warehouses, and small/medium-sized businesses.
Related Topics
Concepts
Pandemic
Suite
Plan (archaeology)
Computer science
Function (biology)
Operations research
Component (thermodynamics)
Management science
Risk analysis (engineering)
Engineering
Coronavirus disease 2019 (COVID-19)
Political science
Business
Law
Medicine
Geography
Archaeology
Infectious disease (medical specialty)
Physics
Evolutionary biology
Pathology
Biology
Disease
Thermodynamics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1145/3460125
- OA Status
- green
- Cited By
- 2
- References
- 54
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3094401266
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3094401266Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1145/3460125Digital Object Identifier
- Title
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University Operations During a Pandemic: A Flexible Decision Analysis ToolkitWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-09-08Full publication date if available
- Authors
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Himanshu Kharkwal, Dakota Olson, Jiali Huang, Abhiraj Mohan, Ankur Mani, Jaideep SrivastavaList of authors in order
- Landing page
-
https://doi.org/10.1145/3460125Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2010.10112Direct OA link when available
- Concepts
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Pandemic, Suite, Plan (archaeology), Computer science, Function (biology), Operations research, Component (thermodynamics), Management science, Risk analysis (engineering), Engineering, Coronavirus disease 2019 (COVID-19), Political science, Business, Law, Medicine, Geography, Archaeology, Infectious disease (medical specialty), Physics, Evolutionary biology, Pathology, Biology, Disease, ThermodynamicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2024: 1, 2022: 1Per-year citation counts (last 5 years)
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20Other works algorithmically related by OpenAlex
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