Learning trends of COVID-19 using semi-supervised clustering

TL;DR

This paper employs a semi-supervised mixture model to analyze COVID-19 case and death trends across US states and European countries, revealing insights into mitigation strategies and potential future scenarios.

Contribution

It introduces a semi-supervised clustering method with positive constraints to jointly analyze COVID-19 case and death trends using Gaussian regression models.

Findings

Clusters reflect different mitigation strategies

Identifies current relative standings of regions

Suggests possible future trends if strategies continue

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