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dc.contributor.authorLim, Jessica
dc.contributor.authorLim, Shin Yee
dc.contributor.authorKraker, Jessica J.
dc.descriptionColor poster with text, charts, and graphs.en_US
dc.description.abstractIn summer 2020, the project started with the faculty mentor created a dashboard to visualize and summarize information about local COVID data. Skills developed include learning a new programing package dplyr in R and hosting code on GitHub, which were then applied in preparatory work such as building new data frames and calculations. Thus, we will discuss a predictive time series model with lagged counts for future outcomes (such as hospitalizations), built on age-grouped case-counts to account for the disparities in outcomes observed for different ages in the COVID pandemic.en_US
dc.description.sponsorshipUniversity of Wisconsin--Eau Claire Office of Research and Sponsored Programsen_US
dc.relation.ispartofseriesUSGZE AS589;
dc.subjectCOVID-19 (Disease)en_US
dc.subjectDashboards (Management information systems)en_US
dc.subjectData scienceen_US
dc.subjectDepartment of Mathematicsen_US
dc.titleTracking COVID Locally and Adaptivelyen_US

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    Posters of collaborative student/faculty research presented at CERCA

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