Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Applied Mathematics | en_US |
| dc.contributor.advisor | He, Daihai (AMA) | en_US |
| dc.creator | Chen, Boqiang | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/13292 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Modelling the spread of SARS-CoV-2 and seasonal influenza | en_US |
| dcterms.abstract | This thesis is dedicated to studying the transmission dynamics of COVID-19 in several countries, as well as the unique patterns observed in seasonal influenza during the COVID-19 period. The COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a significant number of infections and fatalities since its initial outbreak in late 2019. As a result, researchers from various disciplines have been actively investigating this global health crisis, employing diverse mathematical models to comprehend the mechanisms of pandemic transmission and explore strategies for mitigating its impact. | en_US |
| dcterms.abstract | Chapter 1 serves as an introduction, providing a comprehensive background and introducing the fundamental concepts relevant to the thesis. Following this, Chapter 2 focuses on the transmission dynamics of COVID-19 in specific countries. The ongoing COVID-19 pandemic has witnessed multiple waves of infection in six countries: India, Indonesia, Nepal, Malaysia, Bangladesh, and Myanmar. Despite the severity of the pandemic in these countries, there has been a limited amount of research conducted to understand the dynamics of the disease. Therefore, the primary objective of Chapter 2 is to assess the impact of the Omicron variant in these six countries and estimate crucial epidemiological parameters such as the infection fatality rate (IFR) and the reproduction number (R0). To achieve this, a Susceptible-Vaccinated-Exposed-Infectious-Hospitalized-Death-Recovered (SVEIHDR) model with a time-varying transmission rate β(t) is proposed. The model takes into account the specific characteristics of the Omicron variant, such as its level of immune evasion and intrinsic transmissibility advantage. Through comprehensive simulations, the model successfully captures the multiple waves of the COVID-19 pandemic in each country and aligns well with reported cases. | en_US |
| dcterms.abstract | In Chapter 3, we shift the focus to examining the unusual patterns observed in seasonal influenza in China and the United States during the COVID-19 period. The implementation of nonpharmaceutical interventions (NPIs) to mitigate the spread of COVID-19 has inadvertently influenced the transmission dynamics of other respiratory infectious diseases, including seasonal influenza. The significant decline in seasonal influenza cases resulting from these interventions raises concerns about the waning population immunity and the potential resurgence of seasonal influenza when restrictions are lifted in the post-pandemic era. By analyzing weekly laboratory confirmations of influenza cases from FluNet, we investigate the resurgence patterns of seasonal influenza in China and the United States. Remarkably, our analysis reveals that the impact of NPIs, particularly travel restrictions, has resulted in influenza virus A dominance in the United States, whereas influenza virus B dominates in China. | en_US |
| dcterms.abstract | Furthermore, Chapter 4 delves deeper into the investigation of influenza transmission dynamics during the COVID-19 period. We utilize a Bayesian mechanistic model and integrate FluNet-derived cases and deaths data using two data preprocessing techniques. This enables us to estimate the time-varying effective reproduction number (Rt) of seasonal influenza, both pre- and post-implementation of non-pharmaceutical interventions aimed at controlling the COVID-19 pandemic. Additionally, we explore the impact of NPIs implemented to combat the COVID-19 pandemic on seasonal influenza activity. By incorporating the effects of these interventions, our Bayesian mechanistic model provides valuable insights into the dynamics of seasonal influenza during the COVID-19 period, ultimately contributing to a better understanding of the intricate interplay between the COVID-19 and seasonal influenza. | en_US |
| dcterms.abstract | In the final chapter, Chapter 5, we present a comprehensive conclusion of this thesis. Additionally, we introduce future work ideas that can build upon the findings and insights obtained in this study. | en_US |
| dcterms.extent | xxii, 113 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2024 | en_US |
| dcterms.educationalLevel | Ph.D. | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.LCSH | Epidemiology -- Mathematical models | en_US |
| dcterms.LCSH | COVID-19 (Disease) -- Mathematical models | en_US |
| dcterms.LCSH | Influenza -- Transmission -- Mathematical models | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | open access | en_US |
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