Predictive Modeling of County-level Contributing Factors to COVID-19 Propagation in the United States
Abstract: This work presents a methodology to systematically examine and quantify the relationship between predictors and the COVID-19 cases at county level. The main objective is to extract predictors that characterize different aspects of a county from publicly available data and investigate the relevance between the predictors and the COVID-19 cases so that we can predict the propagation of COVID-19 in the near future. For this purpose, we extract predictors in social economy, health, demography and mobility and use Pearson correlation coefficient and lasso regularization model to find non-redundant predictors that are highly correlated to COVID-19 cases. With these predictors, we formulate a fixed-effect regression model at county level to quantify the relationship. The results clearly show that the significant predictors selected and the model can capture the relationship well.
Advisor Information: Hui Yang, Ph.D. PI & Site Director: NSF Center for Health Organization Transformation Professor: Industrial and Manufacturing Engineering Affiliate Faculty: Bioengineering, Institute of Cyberscience, CIMP-3D Office: Leonhard 221, Tel: 814-865-7397 The Pennsylvania State University, University Park, PA 16802-1401 Email: email@example.com
|Work Title||Predictive Modeling of County-level Contributing Factors to COVID-19 Propagation in the United States|
|License||CC0 1.0 (Public Domain Dedication)|
|Work Type||Research Paper|
|Deposited||March 22, 2021|
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