Title:
Determining Infectious Disease Positivity Rate Over Interaction Rate Through Analysis of Collocation Data
Determining Infectious Disease Positivity Rate Over Interaction Rate Through Analysis of Collocation Data
| dc.contributor.advisor | Abowd, Gregory Dominic | |
| dc.contributor.author | Wang, Yiyang | |
| dc.contributor.committeeMember | Ploetz, Thomas | |
| dc.contributor.department | Computer Science | |
| dc.date.accessioned | 2022-05-27T14:37:12Z | |
| dc.date.available | 2022-05-27T14:37:12Z | |
| dc.date.created | 2022-05 | |
| dc.date.issued | 2022-05 | |
| dc.date.submitted | May 2022 | |
| dc.date.updated | 2022-05-27T14:37:13Z | |
| dc.description.abstract | Knowing and understanding the flow of infectious disease within a community is very important, as it can effectively aid community administrations in planning their actions to control the situations. In this study, I investigated the correlation between collocation rate and COVID positivity rate within each building pair using Wifi data and school daily COVID case reports. I used collocation bipartite graphs to generate occupancy features, such as occupancy count and occupancy duration, and then input these features into our correlation model. I then used the COVID positivity rate as the output of the correlation model. Using the model, I am able to find a weak linear correlation between the collocation rate and the COVID positivity rate. The correlation is stronger in places with more students in and out on a daily basis, such as libraries and student centers. Using this insight, the school administration could advise students who visited these places to get tested when there was a COVID outbreak in these areas. A similar approach could also be adopted to investigate the correlation between collocation rate and infectious positivity rate on other types of infectious diseases in the future. | |
| dc.description.degree | Undergraduate | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/1853/66702 | |
| dc.language.iso | en_US | |
| dc.publisher | Georgia Institute of Technology | |
| dc.subject | human-centered computing | |
| dc.subject | machine learning | |
| dc.subject | contact tracing | |
| dc.title | Determining Infectious Disease Positivity Rate Over Interaction Rate Through Analysis of Collocation Data | |
| dc.type | Text | |
| dc.type.genre | Undergraduate Thesis | |
| dspace.entity.type | Publication | |
| local.contributor.corporatename | College of Computing | |
| local.contributor.corporatename | School of Computer Science | |
| local.contributor.corporatename | Undergraduate Research Opportunities Program | |
| local.relation.ispartofseries | Undergraduate Research Option Theses | |
| relation.isOrgUnitOfPublication | c8892b3c-8db6-4b7b-a33a-1b67f7db2021 | |
| relation.isOrgUnitOfPublication | 6b42174a-e0e1-40e3-a581-47bed0470a1e | |
| relation.isOrgUnitOfPublication | 0db885f5-939b-4de1-807b-f2ec73714200 | |
| relation.isSeriesOfPublication | e1a827bd-cf25-4b83-ba24-70848b7036ac | |
| thesis.degree.level | Undergraduate |