| Author: | Peng, Ningyezi |
| Title: | Modelling vulnerability and resilience of urban complex systems in response to recurring COVID-19 waves |
| Advisors: | Liu, Xintao (LSGI) |
| Degree: | Ph.D. |
| Year: | 2025 |
| Subject: | Population geography City planning -- Health aspects Communicable diseases Epidemics -- Prevention Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Land Surveying and Geo-Informatics |
| Pages: | xii, 132 pages : color illustrations |
| Language: | English |
| Abstract: | Superspreading events (SSEs) underscore the uneven spreading patterns of COVID-19 across individuals and places. These heterogenous spread dynamics may stem from human mobility, yet the underlying mechanisms are still not fully understood. While existing research has predominantly emphasized the significance of local mobility intensity, it has often overlooked other critical aspects, such as the spatial structure of human mobility. Therefore, this thesis aims to investigate how the spatial structure of human mobility influences on local spread dynamics. Specifically, within urban areas, human mobility patterns follow a widely found power-law scaling distribution, referred to as the urban scaling structure afterwards. Our first objective is to explore the impact of the urban scaling structure on local spread dynamics. The scaling property of the urban scaling structure indicates that cities are urban complex systems composed of hierarchically ordered subsystems. After exploring how COVID-19 spreads within urban complex systems, the subsequent objective is to examine how coronavirus variants with different characteristics interact with urban complex systems, influencing local spread dynamics and ultimately contributing to heterogeneous vulnerability patterns. Finally, this thesis aims to compare different scenarios of new variant invasions and their influence on urban resilience. To achieve these goals, this thesis employs a spatially-explicit agent-based model that incorporates the urban scaling structure to simulate fine-grained human mobility patterns and individual-to-individual spread processes. The simulation results fit reasonably well with empirical data from the fifth and the sixth waves of the Omicron variants at various spatial scales in Hong Kong. The validated model is firstly used to explore the impact of urban scaling structure on local spread dynamics. The results reveal a positive association between the scaling index and local spread risks among places, as well as the likelihood for local visitors to become superspreaders. The scaling index represents a place’s importance within the urban scaling structure. The findings implies that the urban scaling structure may offer the first-mover advantage to a minority of places and their local visitors to infect earlier and thus infect more. Further simulations on hospital stress reveal large variations among local hospitals and over time concerning Emergency Department services and hospital beds. Secondly, the model is employed to examine local vulnerability patterns considering different variant characteristics. Different variants may lead to differing degrees of individual heterogeneity in infectiousness. Simulations show that while different degrees of individual heterogeneity alone exert small effects on local SSE risks, it amplifies the effects of the urban scaling structure on local SSE risks. The findings imply that individual characteristics may not play as decisive a role in SSEs as expected. Instead, places could play a dominant role by constraining individuals’ ability to fully realize their spread potential. Additionally, a counterfactual simulation of the lockdown scenario demonstrated that implementing lockdown measures, despite the significant cost, would not yield substantial long-term benefits and could potentially exacerbate spatial inequalities. Thirdly, the model is utilized to compare new variant invasion scenarios involving the introduction of new variant into origin places with high or low scaling indices, which signify places’ importance within the urban scaling structure. Our analysis reveals differences in initial places of invasion have path-dependent effects on urban resilience. While high scenarios exhibit a greater chance of successfully initiating new waves, low scenarios surprisingly show more explosive early spread. This study brings important insights into local spread dynamics of COVID-19 and similar diseases. Firstly, it highlights the crucial role of urban scaling structure in shaping local spread risks and local SSEs. Secondly, it demonstrates how variant invasion contexts interact with urban complex systems, leading to diverse vulnerability and resilience outcomes. These findings could inform policymaking at finer spatial scales and over relatively longer temporal scales. The research framework presented here holds potential for broader applications in wider spatial contexts (e.g., Great Bay Areas) and various disaster contexts. |
| Rights: | All rights reserved |
| Access: | open access |
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