A time-series clustering algorithm for analyzing the changes of mobility pattern caused by COVID-19

Abstract

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 issued restrictions on traveling, institutions cancel gatherings, and citizens socially distance themselves to limit the spread of the virus. This paper aims to develop a novel time-series clustering algorithm to analyze the changes in mobility patterns caused by the COVID-19. This work will produce broader impacts in many areas, such as helping local governments locate the medical facilities and improving the social distancing recommendations for infectious disease control.