Empirical networks for localized COVID-19 interventions using WiFi infrastructure at university campuses
Infectious diseases, like COVID-19, pose serious challenges to university campuses, which typically adopt closure as a non-pharmaceutical intervention to control spread and ensure a gradual return to normalcy. Intervention policies, such as remote instruction (RI) where large classes are offered onl...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-05-01
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| Series: | Frontiers in Digital Health |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fdgth.2023.1060828/full |
| _version_ | 1797826271449710592 |
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| author | Vedant Das Swain Jiajia Xie Jiajia Xie Maanit Madan Sonia Sargolzaei James Cai Munmun De Choudhury Gregory D. Abowd Gregory D. Abowd Lauren N. Steimle B. Aditya Prakash |
| author_facet | Vedant Das Swain Jiajia Xie Jiajia Xie Maanit Madan Sonia Sargolzaei James Cai Munmun De Choudhury Gregory D. Abowd Gregory D. Abowd Lauren N. Steimle B. Aditya Prakash |
| author_sort | Vedant Das Swain |
| collection | DOAJ |
| description | Infectious diseases, like COVID-19, pose serious challenges to university campuses, which typically adopt closure as a non-pharmaceutical intervention to control spread and ensure a gradual return to normalcy. Intervention policies, such as remote instruction (RI) where large classes are offered online, reduce potential contact but also have broad side-effects on campus by hampering the local economy, students’ learning outcomes, and community wellbeing. In this paper, we demonstrate that university policymakers can mitigate these tradeoffs by leveraging anonymized data from their WiFi infrastructure to learn community mobility—a methodology we refer to as WiFi mobility models (WiMob). This approach enables policymakers to explore more granular policies like localized closures (LC). WiMob can construct contact networks that capture behavior in various spaces, highlighting new potential transmission pathways and temporal variation in contact behavior. Additionally, WiMob enables us to design LC policies that close super-spreader locations on campus. By simulating disease spread with contact networks from WiMob, we find that LC maintains the same reduction in cumulative infections as RI while showing greater reduction in peak infections and internal transmission. Moreover, LC reduces campus burden by closing fewer locations, forcing fewer students into completely online schedules, and requiring no additional isolation. WiMob can empower universities to conceive and assess a variety of closure policies to prevent future outbreaks. |
| first_indexed | 2024-03-13T11:06:17Z |
| format | Article |
| id | doaj.art-3d530c62fc95406aa5c2fd8bd25ffd65 |
| institution | Directory Open Access Journal |
| issn | 2673-253X |
| language | English |
| last_indexed | 2024-03-13T11:06:17Z |
| publishDate | 2023-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Digital Health |
| spelling | doaj.art-3d530c62fc95406aa5c2fd8bd25ffd652023-05-16T05:39:14ZengFrontiers Media S.A.Frontiers in Digital Health2673-253X2023-05-01510.3389/fdgth.2023.10608281060828Empirical networks for localized COVID-19 interventions using WiFi infrastructure at university campusesVedant Das Swain0Jiajia Xie1Jiajia Xie2Maanit Madan3Sonia Sargolzaei4James Cai5Munmun De Choudhury6Gregory D. Abowd7Gregory D. Abowd8Lauren N. Steimle9B. Aditya Prakash10College of Computing, Georgia Institute of Technology, Atlanta, GA, United StatesCollege of Computing, Georgia Institute of Technology, Atlanta, GA, United StatesH. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, United StatesCollege of Computing, Georgia Institute of Technology, Atlanta, GA, United StatesCollege of Computing, Georgia Institute of Technology, Atlanta, GA, United StatesDepartment of Computer Science, Brown University, Providence, RI, United StatesCollege of Computing, Georgia Institute of Technology, Atlanta, GA, United StatesCollege of Computing, Georgia Institute of Technology, Atlanta, GA, United StatesCollege of Engineering, Northeastern University, Boston, MA, United StatesH. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, United StatesCollege of Computing, Georgia Institute of Technology, Atlanta, GA, United StatesInfectious diseases, like COVID-19, pose serious challenges to university campuses, which typically adopt closure as a non-pharmaceutical intervention to control spread and ensure a gradual return to normalcy. Intervention policies, such as remote instruction (RI) where large classes are offered online, reduce potential contact but also have broad side-effects on campus by hampering the local economy, students’ learning outcomes, and community wellbeing. In this paper, we demonstrate that university policymakers can mitigate these tradeoffs by leveraging anonymized data from their WiFi infrastructure to learn community mobility—a methodology we refer to as WiFi mobility models (WiMob). This approach enables policymakers to explore more granular policies like localized closures (LC). WiMob can construct contact networks that capture behavior in various spaces, highlighting new potential transmission pathways and temporal variation in contact behavior. Additionally, WiMob enables us to design LC policies that close super-spreader locations on campus. By simulating disease spread with contact networks from WiMob, we find that LC maintains the same reduction in cumulative infections as RI while showing greater reduction in peak infections and internal transmission. Moreover, LC reduces campus burden by closing fewer locations, forcing fewer students into completely online schedules, and requiring no additional isolation. WiMob can empower universities to conceive and assess a variety of closure policies to prevent future outbreaks.https://www.frontiersin.org/articles/10.3389/fdgth.2023.1060828/fullCOVID-19mobilitymodelingpolicynon-pharmaceutical interventionWiFi |
| spellingShingle | Vedant Das Swain Jiajia Xie Jiajia Xie Maanit Madan Sonia Sargolzaei James Cai Munmun De Choudhury Gregory D. Abowd Gregory D. Abowd Lauren N. Steimle B. Aditya Prakash Empirical networks for localized COVID-19 interventions using WiFi infrastructure at university campuses Frontiers in Digital Health COVID-19 mobility modeling policy non-pharmaceutical intervention WiFi |
| title | Empirical networks for localized COVID-19 interventions using WiFi infrastructure at university campuses |
| title_full | Empirical networks for localized COVID-19 interventions using WiFi infrastructure at university campuses |
| title_fullStr | Empirical networks for localized COVID-19 interventions using WiFi infrastructure at university campuses |
| title_full_unstemmed | Empirical networks for localized COVID-19 interventions using WiFi infrastructure at university campuses |
| title_short | Empirical networks for localized COVID-19 interventions using WiFi infrastructure at university campuses |
| title_sort | empirical networks for localized covid 19 interventions using wifi infrastructure at university campuses |
| topic | COVID-19 mobility modeling policy non-pharmaceutical intervention WiFi |
| url | https://www.frontiersin.org/articles/10.3389/fdgth.2023.1060828/full |
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