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- W4286976663 abstract "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. The obtained results help identify the current relative standing of individual states and show a possible future if they continue with the chosen mitigation technique" @default.
- W4286976663 created "2022-07-25" @default.
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- W4286976663 date "2021-09-14" @default.
- W4286976663 modified "2023-10-17" @default.
- W4286976663 title "Learning trends of COVID-19 using semi-supervised clustering" @default.
- W4286976663 doi "https://doi.org/10.48550/arxiv.2109.06955" @default.
- W4286976663 hasPublicationYear "2021" @default.
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