Exploration of potential factors affecting the spread of COVID-19 using time series clustering

Authors: Ziyi Zhang*, Department of Electrical and Computer Engineering, Texas A&M University, Nick Duffield, Department of Electrical and Computer Engineering, Texas A&M University, Diya Li, Department of Geography, Texas A&M University, Zhe Zhang, Department of Geography, Texas A&M University
Topics: Agricultural Geography
Keywords: Time Series Clustering, COVID-19, Mobility
Session Type: Virtual Paper
Day: 4/10/2021
Start / End Time: 4:40 PM / 5:55 PM
Room: Virtual 9
Presentation File: No File Uploaded


The coronavirus (COVID-19) has spread to more than 135 countries and continues to spread. The virus sickened more than 90,201,652 people until January 2021 and caused 1,937,091 deaths in the world. So far, social distancing plays a vital role in controlling the coronavirus. Governments issue restrictions on traveling, institutions cancel gatherings, and citizens socially distance themselves to limit the spread of the virus.
The paper's main focus is to explore changes in people's mobility patterns under the COVID-19 pandemic. Additionally, we conducted a spatial correlation analysis to identify other potential factors that can cause the spread of COVID-19 diseases. The project 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 models' results to locate risk areas and enforce guidelines to limit interaction in those areas and provide additional medical facilities.

Abstract Information

This abstract is already part of a session. View the session here.

To access contact information login