Disruption in the Chinese E-Commerce During COVID-19: A Case Study of the Beidian Platform

The recent outbreak of the novel coronavirus (COVID-19) has infected millions of citizens worldwide and claimed many lives. This pandemic has changed human life in many ways, and the research community is putting efforts to understand these impacts in various sectors. This paper focuses on its impact on the Chinese market by analyzing the e-commerce behavioral changes seen at an online shopping platform before and during the pandemic. We present how the epidemic affected consumer behaviors via time series analysis. We find that disruptions in the supply of essential goods needed during a disaster can be leveraged for epidemic forecasting, and further design a hierarchical attention learning model to predict the newly confirmed cases. Experiment results demonstrate that our predictions outperform existing baselines, which also extends to the long-term and province-level forecasts. We believe models like ours contribute to better preparing for future epidemics by gaining extra time to launch preventive steps.

[1]  G. Eysenbach,et al.  Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak , 2010, PloS one.

[2]  José Neves,et al.  Evolution of neural networks for classification and regression , 2007, Neurocomputing.

[3]  Matthias Doepke,et al.  The Impact of Covid-19 on Gender Equality , 2020 .

[4]  Chia-Hung Yeh,et al.  Trend Prediction of Influenza and the Associated Pneumonia in Taiwan Using Machine Learning , 2019, 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS).

[5]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[6]  A. Sumner,et al.  Estimates of the impact of COVID-19 on global poverty , 2020 .

[7]  Duen-Ren Liu,et al.  A Hybrid Neural Network Model for Sales Forecasting Based on ARIMA and Search Popularity of Article Titles , 2016, Comput. Intell. Neurosci..

[8]  Jing Li,et al.  Early Prediction of the 2019 Novel Coronavirus Outbreak in the Mainland China Based on Simple Mathematical Model , 2020, IEEE Access.

[9]  Chris Hans.  Elastic Net Regression Modeling With the Orthant Normal Prior , 2011 .

[10]  Tao Wang,et al.  When Your Friends Become Sellers: An Empirical Study of Social Commerce Site Beidian , 2019, ICWSM.

[11]  B. Graham,et al.  Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation , 2020, Science.

[12]  Jon Cohen,et al.  Scientists are racing to model the next moves of a coronavirus that's still hard to predict , 2020 .

[13]  Xiang Li,et al.  Predicting the outbreak of the hand-foot-mouth diseases in China using recurrent neural network , 2019, 2019 IEEE International Conference on Healthcare Informatics (ICHI).

[14]  Hongda Jiang,et al.  Visual Data Analysis and Simulation Prediction for COVID-19 , 2020, International Journal of Educational Excellence.

[15]  D. Brockmann,et al.  Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China , 2020, Science.

[16]  S C Schoenbaum,et al.  Economic Impact of Influenza: The Individual's Perspective , 1987, The American journal of medicine.

[17]  Madhav V. Marathe,et al.  Forecasting a Moving Target: Ensemble Models for ILI Case Count Predictions , 2014, SDM.

[18]  Rainer Duttmann,et al.  Spatio-Temporal Prediction of the Epidemic Spread of Dangerous Pathogens Using Machine Learning Methods , 2020, ISPRS Int. J. Geo Inf..

[19]  N. Christakis,et al.  Social Network Sensors for Early Detection of Contagious Outbreaks , 2010, PloS one.

[20]  Haifeng Wang,et al.  Understanding the Impact of the COVID-19 Pandemic on Transportation-related Behaviors with Human Mobility Data , 2020, KDD.

[21]  Hung-Hao Chang,et al.  Covid-19 and the Demand for Online Food Shopping Services: Empirical Evidence from Taiwan , 2020 .

[22]  Ruifu Yang,et al.  An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China , 2020, Science.

[23]  J. Contreras,et al.  ARIMA models to predict next-day electricity prices , 2002 .

[24]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[25]  G. Herrler,et al.  SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor , 2020, Cell.

[26]  W. O. Kermack,et al.  A contribution to the mathematical theory of epidemics , 1927 .

[27]  Xin Yan,et al.  Linear Regression Analysis: Theory and Computing , 2009 .

[28]  Terence C. Mills,et al.  Time series techniques for economists , 1990 .

[29]  Jeremy Ginsberg,et al.  Detecting influenza epidemics using search engine query data , 2009, Nature.

[30]  Chen-hua Shen,et al.  Analysis of detrended time-lagged cross-correlation between two nonstationary time series , 2015 .

[31]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[32]  Fengli Xu,et al.  Understanding the Role of Intermediaries in Online Social E-commerce , 2020, Proc. ACM Hum. Comput. Interact..

[33]  Warren Cornwall,et al.  Social scientists scramble to study pandemic, in real time , 2020 .

[34]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[35]  Aron Culotta,et al.  Towards detecting influenza epidemics by analyzing Twitter messages , 2010, SOMA '10.

[36]  Nicholas Bloom,et al.  The Unprecedented Stock Market Impact of Covid-19 , 2020 .

[37]  M. Meltzer,et al.  The economic impact of pandemic influenza in the United States: priorities for intervention. , 1999, Emerging infectious diseases.

[38]  Marvin H. J. Gruber.  Improving Efficiency by Shrinkage: The James--Stein and Ridge Regression Estimators , 1998 .