Abstract
Emerging and reemerging infectious diseases have caused substantial morbidity and mortality worldwide. Mathematical and computational models offer powerful tools for investigating the transmission dynamics of infectious diseases, forecasting potential epidemic outcomes, and informing public health policies. The fundamental elements of these models are patterns of human mobility and structures of physical and social contact between individuals, which can be naturally represented as networks. Although epidemic processes on networks have been widely discussed in previous studies, there remains an incomplete understanding of the role of realistic intervention settings, modulated by various biological, socioeconomic, and behavioural factors, in the transmission and control of infectious diseases.In this thesis, we proposed a set of theoretical and real-world network epidemic models to address the above problems. By modelling heterogeneous inter-individual and inter-population interactions, we conducted four studies to investigate the impact of three broad types of control measures (i.e., non-pharmaceutical interventions, vaccination, and antiviral medicine interventions) on epidemic dynamics under realistic intervention settings. Leveraging data from multiple sources, we comprehensively investigated the transmission dynamics of infectious disease across different scales, and explored the complex interplay among epidemics, risk perception, cross-immunity between diseases, vaccine equity, and human movement behaviours.
First, motivated by the importance of individual differences in the adoption of non-pharmaceutical interventions in response to infectious disease outbreaks, we proposed a heterogeneous disease-behavior-information transmission model, in which people's risk of getting infected is influenced by information diffusion, behaviour change, and disease transmission. We investigated how heterogeneous risk perception among individuals influences the prevalence of protective behaviour and the epidemic outbreak. We found that disease awareness plays a central role in preventing the disease outbreak, and a reasonable fraction of overreacting nodes are needed to effectively control the epidemic. The media and opinion leaders should not understate the transmissibility and severity of diseases to ensure that people become aware of the disease and adopt self-protection to protect themselves and the whole population.
Second, vaccines are effective tools for preventing epidemics like COVID-19, diphtheria, and influenza. Vaccines help the human body's natural defence systems to develop antibodies to pathogens. Generally, antibodies to one pathogen do not provide protection against another pathogen. However, there are often multiple diseases with cross-immunity competing for vaccination resources. To identify the optimal vaccination program that controls competitive diseases with cross-immunity at the minimum cost, we developed an optimization framework based on a two-layer disease transmission network. We identified three scenarios of the optimal vaccination program and derived a criterion to specify the optimal program based on the costs for different vaccines. The proposed optimization framework provides clues to design effective and efficient vaccination programs and serves as a general scheme for the allocation of public health products.
Third, despite broad agreement on the negative consequences of vaccine inequity, we have seen a growing gap in vaccine coverage between high-income countries (HICs) and low- and middle-income countries (LMICs) during the COVID-19 pandemic. To explore the short-term and long-term consequences of vaccine (in)equity in the face of evolving COVID-19 strains, we proposed a data-driven multistrain metapopulation model, accounting for viral mutations and global human mobility. We investigated the impact of viral evolution on epidemic dynamics in HICs and LMICs under (in)equitable global vaccine allocation strategies, and evaluated the effects of different vaccine donation strategies to explore a practical pathway to global vaccine equity. Our results show that vaccine inequity provides only limited and short-term benefits to HICs. Equitable vaccine allocation strategies, in contrast, substantially curb the spread of new strains. For HICs, making immediate and generous vaccine donations to LMICs is a practical pathway to protect everyone.
Fourth, pre-exposure prophylaxis (PrEP) is an effective medicine for preventing HIV. However, little has been done to examine the impact of PrEP interventions on containing the geographical spread of HIV among the men who have sex with men (MSM) population. To capture the impacts of PrEP interventions and investigate the geographical spread patterns of a new HIV genotype, we proposed a metapopulation model based on a movement probabilistic network, which infers the movement patterns among MSM between 21 prefecture-level cities in Guangdong, China. Results show that PrEP initiation exponentially delays the occurrence of the virus for the rest of the cities transmitted from the initial outbreak city; hubs on the movement network are at a higher risk of ‘earliest’ exposure to the new HIV genotype; most cities acquire the virus directly from the initial outbreak city, while others acquire the virus from cities that are not initial outbreak locations and have relatively high betweenness centralities.
| Date of Award | 27 Jun 2023 |
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| Original language | English |
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| Supervisor | Qingpeng ZHANG (Supervisor) |