Multi-Scale Genomic Surveillance and Disease Modeling of COVID-19

Abstract

The COVID-19 pandemic has fundamentally reshaped the landscape of public health, exposing critical vulnerabilities in disease surveillance, response systems, and the social structure of urban environments. This study presents a comprehensive, three-year, multi-country analysis of SARS-CoV-2 genomes derived from wastewater, spanning March 2020 to May 2023. In parallel, the study explores the evolutionary trajectory and public health implications of SARS-CoV-2 variants in Hong Kong from January 2020 to December 2023. Through high-throughput sequence data and advanced bioinformatics, the research identifies key lineages and mutations of circulating variants, with a particular focus on those affecting viral entry and transmission dynamics. Molecular simulations further elucidate the functional significance of these mutations, providing mechanistic insights into how changes in the spike protein may alter interactions with the human ACE2 receptor and influence transmission. Recognizing that the pandemic’s impact is shaped not only by viral evolution but also by social and environmental factors, this study integrates multiple data streams, including genomic, clinical, demographic, and socioeconomic data, to quantify the risk determinants of infectious disease spread. Special attention is given to the unique context of Hong Kong’s public housing estates, where household-level factors and socioeconomic disparities have contributed to health inequities and shaped transmission patterns. The ultimate goal of this study is the development of a robust predictive model that integrates these diverse data streams to forecast hotspots, assess the effectiveness of control measures, and inform targeted public health strategies. By bridging molecular virology, epidemiology, and social science, this study not only advances our understanding of SARS-CoV-2 but also provides actionable insights for mitigating health disparities and strengthening pandemic preparedness in urban settings.

Date of Award	2025
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology
Supervisor	King Lun YEUNG (Supervisor)