Before revision:  We develop a temporal complex network to quantify US county level spread dynamics of Covid-19. We conduct the analysis by using the following sequence of methods: Spearman's rank correlation, Granger causality, Random Forest (RF) model, and EGARCH (1,1) model .
After revision:  We develop a temporal complex network to quantify US county level spread dynamics of COVID-19. The objective is to study the effects of the local spread dynamics, COVID-19 cases and death, and Google search activities on the US stock market. We use both conventional econometric and Machine Learning (ML) models .