Exploration of Potential Factors Affecting the Spread of COVID-19 using Spatiotemporal Analysis

Abstract

The coronavirus (COVID-19) has caused a huge negative influence on the global. The virus continues to spread and has sickened more than 90,201,652 people until January 2021 and caused 1,937,091 deaths in the world. So far, social distancing was given as an effective way to control the coronavirus. Governments issue restrictions on traveling, institutions cancel gatherings, and citizens socially distance themselves to limit the spread of the virus. The thesis's main focus is to explore the spatiotemporal patterns under the COVID-19 pandemic. Additionally, we conducted both spatiotemporal modeling analysis and spatiotemporal clustering analysis in Texas to identify some potential variables which are associated with the increase of confirmed cases. My thesis will help local governments locate the medical facilities and improve the social distancing recommendations regarding the COVID-19 outbreak. For instance, governments can use our results to locate risk areas and enforce guidelines to limit interaction in those areas and provide additional medical facilities.