Main Menu

CUHK-Shenzhen Prof. Jia Jianmin’s Study Accurately Tracks COVID-19 Spread with Big Data

  • 2020.04.30
  • News
A research co-conducted by Prof. Jia Jianmin, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), developed a new method to accurately track the spread of COVID-19 using population flow data.

A research co-conducted by Prof. Jia Jianmin, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), developed a new method to accurately track the spread of COVID-19 using population flow data, and establishing a new risk assessment model to identify high-risk locales of COVID-19 at an early stage, which serves as a valuable toolkit to public health experts and policy makers in implementing infectious disease control during new outbreaks. The study findings have been published in the journal Nature on April 29.

 

 

The researchers of this team used nation-wide data provided by a major national carrier in China to track population movement out of Wuhan between 1 January and 24 January 2020, a period covering the annual Chunyun mass migration before the Chinese Lunar New Year to a lockdown of the city to contain the virus. The movement of over 11 million people travelling through Wuhan to 296 prefectures in 31 provinces and regions in China were tracked.

 

 

Differing from usual epidemiological models that rely on historical data or assumptions, the team used real-time data about actual movements focusing on aggregate population flow rather than individual tracking. The data include any mobile phone user who had spent at least 2 hours in Wuhan during the study period. Locations were detected once users had their phones on. As only aggregate data was used and no individual data was used, there was no threat to consumer privacy.

 

Combining the population flow data with the number and location of COVID-19 confirmed cases up to 19 February 2020 in China, the study showed that the relative quantity of human movement from the disease epicentre, in this case, Wuhan, directly predicted the relative frequency and geographic distribution of the number of COVID-19 cases across China. The researchers found that their model can explain 96% of the distribution and intensity of the spread of COVID-19 across China statistically.

 

The research team then used this empirical relationship to build a new risk detection toolkit. Leveraging on the population flow data, the researchers created an "expected growth pattern" based on the number of people arriving from the risk source, i.e. the disease epicentre. The team thereby developed a new risk model by contrasting expected growth of cases against the actual number of confirmed cases for each city in China, the difference being the "community transmission risk."

 

The approach is advantageous because it requires no assumptions or knowledge of how or why the virus spreads, is robust to data reporting inaccuracies, and only requires knowledge of relative distribution of human movement. It can be used by policy makers in any nation with available data to make rapid and accurate risk assessments and to plan allocation of limited resources ahead of ongoing disease outbreaks.

 

The approach is advantageous because it requires no assumptions or knowledge of how or why the virus spreads, is robust to data reporting inaccuracies, and only requires knowledge of relative distribution of human movement. It can be used by policy makers in any nation with available data to make rapid and accurate risk assessments and to plan allocation of limited resources ahead of ongoing disease outbreaks.

 

Dr. Jayson Jia, Associate Professor of Marketing of HKU, is the lead author of this study. The co-authors are Jianmin Jia, Presidential Chair Professor at the Chinese University of Hong Kong, Shenzhen (corresponding author); Nicholas A. Christakis, Sterling Professor of Social and Natural Science at Yale; Xin Lu, the National University of Defense Technology in Changsha, China, and the Karolinska Institutet in Stockholm, Sweden; Yun Yuan, Southwest Jiaotong University; Ge Xu, Hunan University of Technology and Business.

 

Introdution

Prof. Jia Jianmin Presidential Chair Professor of CUHK-Shenzhen

Prof. Jianmin Jia (Jamie) is an Adjunct Professor in Department of Marketing at The Chinese University of Hong Kong (CUHK) Business School and the presidential chair professor at CUHK-Shenzhen. He was a Professor and Chairman in Department of Marketing at CUHK, Chang Jiang Chair Professor appointed by the Ministry of Education, China, and Dean of School of Economics and Management, Southwest Jiaotong University, China. Prof. Jia received his PhD from the McCombs School of Business at the University of Texas at Austin in 1995. He was a visiting scholar at Carnegie Mellon University and Duke University for three years.

 

Prof. Jia was the prize winner of the 1994 Decision Analysis Student Paper Competition sponsored by the Decision Analysis Society of INFORMS (US), and his dissertation about “Measures of risk and risk-value theory” received Honorable Mention Award from the University of Texas at Austin. Prof. Jia serves as a member of the National MBA Education Supervisory Committee of China, and a member of the Expert Consultation Committee of the Management Sciences Department of the Natural Science Foundation of China.

 

His research and teaching interests include big data marketing, social networks, consumer choice, and decision making. He was an Associate Editor of Operations Research, Academic Trustee of Marketing Science Institute (US), and Vice Chairman of the Academic Committee of China Marketing Association. Prof. Jia published in Management Science, Marketing Science, Psychological Science, Journal of Consumer Research, Operations Research and other leading international and Chinese journals.