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<p>The urban system is believed to play a role in transmission of human respiratory disease such as COVID-19 in densely populated cities. Hong Kong experienced five significant local infection waves of COVID-19 during 2020–2022, caused by different SARS-CoV-2 variants. Viral genomic data capture the footprint of transmission history, offering insights that are invisible to incidence data, such as resolving fine-scale transmission chains, distinguishing between multiple independent introductions and local superspreading events, and revealing cryptic transmission links. Building on this, our study aimed to investigate the impact of urban demography and connectivity on SARS-CoV-2 transmission by integrating genomic surveillance and urban mobility data into Bayesian phylodynamics analysis to examine the spatiotemporal spread of SARS-CoV-2 across epidemic waves three to five in Hong Kong. Using 1,725 viral genomes linked to their patients’ residential addresses, we reconstructed variant-specific dispersal patterns and quantified the impact of human demographic and behavioral factors on inter-district transmission. Results revealed that these three outbreak waves initially concentrated in high-density districts, Wong Tai Sin, Yau Tsim Mong, and Kwai Tsing, respectively. Population density and Mass Transit Railway (MTR) usage consistently predicted transmission rates among districts. The early seeding districts acted as epicenters and main exporters of regional spread. These findings demonstrated that synthesizing pathogen genomic, spatial, and urban mobility data can effectively identify transmission hotspots and optimize targeted interventions. This inter-disciplinary approach provides a multi-data-source framework for monitoring pathogen spread in cities, improving outbreak response in high-density urban environments.</p>

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Geographical dispersion and transmission of multiple SARS-CoV-2 variants in Hong Kong

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月6日

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  • Zoe Yi Song,
  • Jinjin Zhang,
  • Marcus H. H. Shum,
  • Haogao Gu,
  • Wenzhong Shi,
  • Gilman K. H. Siu,
  • Leo L. M. Poon,
  • Joseph T. Wu,
  • Kathy S. M. Leung,
  • Tommy T. Y. Lam

摘要

The urban system is believed to play a role in transmission of human respiratory disease such as COVID-19 in densely populated cities. Hong Kong experienced five significant local infection waves of COVID-19 during 2020–2022, caused by different SARS-CoV-2 variants. Viral genomic data capture the footprint of transmission history, offering insights that are invisible to incidence data, such as resolving fine-scale transmission chains, distinguishing between multiple independent introductions and local superspreading events, and revealing cryptic transmission links. Building on this, our study aimed to investigate the impact of urban demography and connectivity on SARS-CoV-2 transmission by integrating genomic surveillance and urban mobility data into Bayesian phylodynamics analysis to examine the spatiotemporal spread of SARS-CoV-2 across epidemic waves three to five in Hong Kong. Using 1,725 viral genomes linked to their patients’ residential addresses, we reconstructed variant-specific dispersal patterns and quantified the impact of human demographic and behavioral factors on inter-district transmission. Results revealed that these three outbreak waves initially concentrated in high-density districts, Wong Tai Sin, Yau Tsim Mong, and Kwai Tsing, respectively. Population density and Mass Transit Railway (MTR) usage consistently predicted transmission rates among districts. The early seeding districts acted as epicenters and main exporters of regional spread. These findings demonstrated that synthesizing pathogen genomic, spatial, and urban mobility data can effectively identify transmission hotspots and optimize targeted interventions. This inter-disciplinary approach provides a multi-data-source framework for monitoring pathogen spread in cities, improving outbreak response in high-density urban environments.