Identifying High-Risk Workspaces during COVID-19 using Machine Learning
The COVID-19 pandemic has wreaked havoc worldwide, on both public health and the worldwide economy. While necessary, quarantine and social distancing requirements have left many companies unable to reopen their offices in a safe manner. We present a model capable of identifying workspaces at high ri...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
LibraryPress@UF
2021-04-01
|
| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| Subjects: | |
| Online Access: | https://journals.flvc.org/FLAIRS/article/view/128484 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849762035714228224 |
|---|---|
| author | Lex Drennan Matthew Chesser Jorge Lozano Erin Carrier |
| author_facet | Lex Drennan Matthew Chesser Jorge Lozano Erin Carrier |
| author_sort | Lex Drennan |
| collection | DOAJ |
| description | The COVID-19 pandemic has wreaked havoc worldwide, on both public health and the worldwide economy. While necessary, quarantine and social distancing requirements have left many companies unable to reopen their offices in a safe manner. We present a model capable of identifying workspaces at high risk for COVID-19 disease transmission and illustrate how existing techniques for quantifying uncertainty in machine learning can be applied to assess the reliability of these predictions. This model is developed using a dataset created by leveraging historical sales data and detailed product information, and it is in the process of being utilized to identify customers to whom to reach out to facilitate the retrofitting of workspaces to support a safe return to the office. |
| format | Article |
| id | doaj-art-970a3e5e457644e8bb77ceb1eefd6b5e |
| institution | DOAJ |
| issn | 2334-0754 2334-0762 |
| language | English |
| publishDate | 2021-04-01 |
| publisher | LibraryPress@UF |
| record_format | Article |
| series | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| spelling | doaj-art-970a3e5e457644e8bb77ceb1eefd6b5e2025-08-20T03:05:50ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622021-04-013410.32473/flairs.v34i1.12848462878Identifying High-Risk Workspaces during COVID-19 using Machine LearningLex Drennan0Matthew Chesser1Jorge Lozano2Erin Carrier3SteelcaseUniversity of MichiganSteelcaseGrand Valley State UniversityThe COVID-19 pandemic has wreaked havoc worldwide, on both public health and the worldwide economy. While necessary, quarantine and social distancing requirements have left many companies unable to reopen their offices in a safe manner. We present a model capable of identifying workspaces at high risk for COVID-19 disease transmission and illustrate how existing techniques for quantifying uncertainty in machine learning can be applied to assess the reliability of these predictions. This model is developed using a dataset created by leveraging historical sales data and detailed product information, and it is in the process of being utilized to identify customers to whom to reach out to facilitate the retrofitting of workspaces to support a safe return to the office.https://journals.flvc.org/FLAIRS/article/view/128484machine learningcovid-19risk detection |
| spellingShingle | Lex Drennan Matthew Chesser Jorge Lozano Erin Carrier Identifying High-Risk Workspaces during COVID-19 using Machine Learning Proceedings of the International Florida Artificial Intelligence Research Society Conference machine learning covid-19 risk detection |
| title | Identifying High-Risk Workspaces during COVID-19 using Machine Learning |
| title_full | Identifying High-Risk Workspaces during COVID-19 using Machine Learning |
| title_fullStr | Identifying High-Risk Workspaces during COVID-19 using Machine Learning |
| title_full_unstemmed | Identifying High-Risk Workspaces during COVID-19 using Machine Learning |
| title_short | Identifying High-Risk Workspaces during COVID-19 using Machine Learning |
| title_sort | identifying high risk workspaces during covid 19 using machine learning |
| topic | machine learning covid-19 risk detection |
| url | https://journals.flvc.org/FLAIRS/article/view/128484 |
| work_keys_str_mv | AT lexdrennan identifyinghighriskworkspacesduringcovid19usingmachinelearning AT matthewchesser identifyinghighriskworkspacesduringcovid19usingmachinelearning AT jorgelozano identifyinghighriskworkspacesduringcovid19usingmachinelearning AT erincarrier identifyinghighriskworkspacesduringcovid19usingmachinelearning |