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Activity Number:
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444
- Recent Advances in Statistical Methodology for Big Data
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Type:
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Contributed
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Date/Time:
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Thursday, August 12, 2021 : 4:00 PM to 5:50 PM
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Sponsor:
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IMS
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Abstract #318406
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Title:
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Learning Trends of COVID-19 Using Semi-Supervised Clustering
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Author(s):
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Semhar Michael*
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Companies:
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South Dakota State University
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Keywords:
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semi-supervised clustering;
mixture model;
covid-19 data
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Abstract:
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A finite mixture model is used to learn trends from the currently available data on coronavirus (COVID-19). Data on the number of confirmed COVID-19 related cases and deaths for European countries and the United States (US) are explored. A semi-supervised clustering approach with positive equivalence constraints is used to incorporate country and state information into the model. The analysis of trends in the rates of cases and deaths is carried out jointly using a mixture of multivariate Gaussian non-linear regression models with a mean trend specified using a generalized logistic function. The optimal number of clusters is chosen using the Bayesian information criterion. The resulting clusters provide insight into different mitigation strategies adopted by US states and European countries.
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Authors who are presenting talks have a * after their name.
