Predicting COVID-19 Incidence Through Human Interactions

• DOI: 10.5281/zenodo.5532706
• Publication Date: 2021-09-27
• Authors: Behzad Vahedi; Morteza Karimzadeh; Hamidreza Zoraghein
• Keywords: 2019-20 coronavirus outbreak; Betacoronavirus

This repository contains data (features) necessary to run STXGB model. STXGB is a spatiotemporal autoregressive model that predicts county-level new cases of COVID-19 in the coterminous US in 1- to 4-week prediction horizons using spatiotemporal lags of infection rates, human interactions, human mobility, and socioeconomic composition of counties as predictive features.