Disruption in Chinese E-Commerce During COVID-19 - Yuan, Yuan; Guan, Muzhi; Zhou, Zhilun; Kim, Sundong; Cha, Meeyoung; Jin, Depeng; Li, Yong (Frontiers in Computer Science, 2021)
The recent outbreak of the novel coronavirus (COVID-19) has infected millions of citizens worldwide and claimed many lives. This paper examines the impact of COVID-19 on Chinese e-commerce by analyzing behavioral changes observed on a large online shopping platform. We first conduct a time series analysis to identify product categories that faced the most extensive disruptions. The time-lagged analysis shows that behavioral patterns of shopping actions are highly responsive to the development of the epidemic. Based on these findings, we present a consumer demand prediction method by encompassing the epidemic statistics and behavioral features of COVID-19 related products. Experimental results demonstrate that our predictions outperform existing baselines and further extend to the long-term and province-level forecasts. Finally, we discuss how our market analysis and prediction can help better prepare for future pandemics by gaining extra time to launch preventive measures.