The Interplay of Demographic Variables and Social Distancing Scores in Deep Prediction of U.S. COVID-19 Cases

DOI10.1080/01621459.2021.1901717zbMath1464.92252arXiv2101.02113OpenAlexW3121468932MaRDI QIDQ4999122

Hamza Chiheb, Francesca Tang, Yang Feng, Jianqing Fan

Publication date: 6 July 2021

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2101.02113

zbMATH Keywords

neural networklearningcommunity detectionspectral clusteringblock modelCOVID-19stochastic machine

Mathematics Subject Classification ID

Epidemiology (92D30) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Artificial neural networks and deep learning (68T07)

Related Items (1)

Hierarchical Neyman-Pearson Classification for Prioritizing Severe Disease Categories in COVID-19 Patient Data

Uses Software

• COVID-Net

Cites Work

• Fast community detection by SCORE
• Spectral clustering and the high-dimensional stochastic blockmodel
• Local quasi-likelihood with a parametric guide
• COVID-Net
• Predicting the cumulative number of cases for the COVID-19 epidemic in China from early data
• Analysis and forecast of COVID-19 spreading in China, Italy and France
• Entrywise eigenvector analysis of random matrices with low expected rank
• Consistency of spectral clustering in stochastic block models
• BOULWARE STATE IN EXACTLY SOLVABLE MODELS OF 2D DILATON GRAVITY
• Community Detection and Stochastic Block Models
• Confidence Distributions and a Unifying Framework for Meta-Analysis
• Learning representations by back-propagating errors

This page was built for publication: The Interplay of Demographic Variables and Social Distancing Scores in Deep Prediction of U.S. COVID-19 Cases