Lin Chen

PhD candidate

Hong Kong University of Science and Technology

Miss Lin Chen received her bachelor’s degree from the Department of Electronics and Information Engineering, Tongji University, where she was honored with the prestigious Nation Scholarship for three consecutive years from 2017 to 2019, alongside receiving the Shanghai Outstanding Graduation Award in 2020. Having been granted by the esteemed Hong Kong Ph.D. Fellowship Scheme (HKPFS), she joined Prof. Pan Hui’s group in the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST) in the same year. Currently, in her third year of the Ph.D. journey, she has had the privilege of being a visiting researcher at two esteemed institutions: (1) the University of Chicago, where she worked closely with Prof. James Evans, Director of Knowledge Lab, and (2) Tsinghua University, where she collaborated with Prof. Yong Li, Director of Future Intelligence Lab. Focusing her research at the intersection of data science, urban science, and computational social science, her work primarily entails (1) utilizing advanced data analysis and machine learning techniques to comprehend urban/social phenomena including human mobility patterns, epidemic spreading, and research innovation, and (2) innovating novel machine learning techniques to better capture the intricacies of urban/social systems.

Strategic COVID-19 vaccine distribution can simultaneously elevate social utility and equity (published in Nature Human Behaviour)

Balancing social utility and equity in distributing limited vaccines is a critical policy concern for protecting against the prolonged COVID-19 pandemic and future health emergencies. What is the nature of the trade-off between maximizing collective welfare and minimizing disparities between more and less privileged communities? To evaluate vaccination strategies, we propose an epidemic model that explicitly accounts for both demographic and mobility differences among communities and their associations with heterogeneous COVID-19 risks, then calibrate it with large-scale data. Using this model, we find that social utility and equity can be simultaneously improved when vaccine access is prioritized for the most disadvantaged communities, which holds even when such communities manifest considerable vaccine reluctance. Nevertheless, equity among distinct demographic features may conflict; for example, low-income neighbourhoods might have fewer elder citizens. We design two behaviour-and-demography-aware indices, community risk and societal risk, which capture the risks communities face and those they impose on society from not being vaccinated, to inform the design of comprehensive vaccine distribution strategies. Our study provides a framework for uniting utility and equity-based considerations in vaccine distribution and sheds light on how to balance multiple ethical values in complex settings for epidemic control.