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[VIRTUAL] Luncheon Fireside Chat, Tracking COVID-19 Spread with Big Data

Tuesday, May 12 2020 at 12:30 - 2:00 pm EDT

From Yale Club of Hong Kong We hope you and your families are safe and well. We are pleased to host a virtual event with Dr. Jayson Jia (Yale College ’08), Associate Professor of Marketing at HKU. Dr. Jia and his co-authors have recently 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 (https://www.nature.com/articles/s41586-020-2284-y/metrics). Dr. Jia and his co-authors 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. Combining the population flow data with the number and location of COVID-19 confirmed cases up to 19 February 2020 in China, Dr Jia’s team 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. Remarks: This is a private event, and participants are requested to respect the confidentiality of the views and opinions expressed. For enquiry, please email rawen.huang@aya.yale.edu When: Tuesday, May 20, 2020 | 12:30pm – 2:00pm HKT Register: https://www.yaleclubhk.org/event_details.aspx?id=610