International USR Summit 2022: Education and Action for a Sustainable Future
![20221116](/scri/-/media/department/scri/events/2022/20221116.png?bc=ffffff&h=540&w=1000&hash=860BF567EF34FC9C17BD5692A847F99F)
-
Date
17 Nov 2022
-
Organiser
Otto Poon Charitable Foundation Smart Cities Research Institute (SCRI)
-
Time
14:30 - 17:30
-
Venue
Online
Speaker
Prof. John Shi
Summary
PolyU and the University Social Responsibility Network (USRN) are co-organising the International USR Summit 2022 on the theme of “Education and Action for a Sustainable Future” from 16 to 18 November 2022. This biennial flagship event of the USRN, a global alliance comprising 20 universities sharing the same mission in pursuing university social responsibility, steers global discussions and advocates for the promotion of social responsibility in higher education.
PAIR will arrange a session featuring Chair Professors of PolyU and Director of SCRI will present an interdisciplinary research project that promote social responsibility and sustainability.
Speaker: Prof. John Shi, Director of SCRI; Chair Professor of Geographical Information Science and Remote Sensing
Title: Spatiotemporal Prediction of COVID-19 Onset Risk to Support Epidemic Prevention and Control
Keynote Speaker
![Prof. John Shi](/scri/-/media/department/scri/events/2022/prof-wenzhong-shi.png?as=1&sc=0.5&hash=0ED94FF8D81C2CD6EB0C9DD19C8F6A47)
Prof. John Shi
Director of SCRI; Chair Professor of Geographical Information Science and Remote Sensing
Predicting the spatiotemporal risk of COVID-19 is the key to formulating place-specific precise control measures. Most methods for predicting COVID-19 risk are based on confirmed/positive cases. However, there is a spatially variant delay of a few days from the symptom onset, when the cases’ infectiousness reaches the peak, to the case is tested positive. The prediction of symptom onset risk thus has a higher potential to support more timely anti-epidemic measures. This presentation introduces the applications of the lately developed extended Weight Kernel Density Estimation (E-WKDE) model for short-term COVID-19 spatiotemporal onset risk prediction. By flexibly incorporating the COVID-19 cases data, virus transmissibility, and mobility data in different granularities, the models have been used to predict the COVID-19 onset risk and evaluate the anti-epidemic measures in various countries, regions, and cities. Studies with the models (e.g., on the early Omicron break in South Africa) found that more strict travel restriction in the epidemic centre and looser epidemic control elsewhere can usually more effectively control the epidemic risk than nation- or region-wide medium control measures, which provides evidence for precise and strict control. The predictions have also been applied to decision-supports, such as identifying the epidemic risk levels in different secondary schools in Hong Kong, and informing the logistic companies in the Guangdong-Hong Kong-Macao Greater Bay Area the epidemic risk of their common destinations.