Carbon Policy

Trajectory Tracking of COVID-19 Epidemic Risk Using Self-organizing Feature Map

¹ Institute of Urban Safety and Environmental Science, Beijing Academy of Science and Technology, P.R. China
² Institutes of Science and Development, Chinese Academy of Sciences, Beijing, P.R. China
³ University of Chinese Academy of Sciences, Beijing, P.R. China

^* To whom correspondence may be addressed. Email: anchen@casisd.cn

Abstract

The ongoing COVID-19 has become a worldwide pandemic with increasing confirmed cases and deaths across the globe. By July 2022, the number of cumulative confirmed cases reported to the World Health Organization (WHO) has risen to 550 million, with more than 6 million deaths in total. The analysis of its epidemic risk remains the focus of attention all over the world for a long time. The Self-organizing feature map (SOM), a vector quantization method, offers a data mapping approach to tracking the response of time series data on a well-trained map. This study aims at a trajectory tracking of COVID-19 epidemic risk in 237 countries measured by the number of new confirmed cases and deaths per day for over one year. A hybrid clustering method uses SOM and K-means to generate a risk map and then displays the trajectory of daily risk on the map. The experimental results demonstrate the promising functionality of SOM for trajectory tracking and give experts insights into the dynamic changes of COVID-19 risk.