Transmission dynamics and immunity of influenza and SARS-CoV-2

Abstract

The persistent global circulation of seasonal influenza viruses, combined with the unprecedented emergence of SARS-CoV-2, underscores the urgent and ongoing need for sophisticated real-time surveillance systems that capture infectious disease transmission dynamics and population immunity landscapes. A deeper understanding of epidemiological characteristics, transmission mechanisms, and host immune responses elicited by these respiratory pathogens is essential for implementing and optimizing, evidence-based public health interventions. This PhD thesis establishes a comprehensive, multi-faceted framework that advances the monitoring and assessment of respiratory pathogen transmission and immunity, specifically addressing critical gaps in real-time epidemic surveillance and the evaluation of durable immune protection over extended timescales. First, this thesis refined the real-time estimation and application of the effective reproductive number (Rₜ). Detailed analysis of COVID-19 in Hong Kong (2020-2022) and Macau (2022) demonstrated Rₜ's indispensable utility in capturing intrinsic, variant-specific (e.g., Delta, Omicron) transmission dynamics and the immediate impact of interventions across successive waves. Extending the same framework to thirteen years (2010–2023) of Hong Kong influenza data systematically quantified the impact of public health and social measures (PHSMs) on transmission. The sharp resurgence of influenza in early 2023 following PHSM relaxation provided direct empirical evidence of their effect. By comparing influenza Rₜ across pre-pandemic, intervention, and post-relaxation phases, this work offers a generalizable methodology for assessing the broader utility of non-pharmaceutical interventions (NPIs) in controlling respiratory pathogens. In addition, this thesis established a new framework to nowcast and forecast the case number and estimation of Rₜ. Second, this thesis characterized the kinetics of individual antibody waning after influenza vaccination. Although hemagglutination inhibition (HAI) antibody titers are established correlates of protection against infection, their longitudinal kinetics post-vaccination remain incompletely defined. Using piecewise log-linear mixed-effects models applied to serial blood samples from a pediatric influenza vaccine randomized controlled trial (RCT), a biphasic waning pattern was identified and quantified in HAI antibodies against influenza A(H3N2) and B/Victoria lineage viruses, featuring an initial rapid decline followed by slower decay. These findings clarify the durability of vaccine-induced immunity. Third, to bridge the gap between individual HAI titers and population-level susceptibility, this thesis integrated serological data from four cohort studies spanning 19 influenza epidemics/seasons. Four population immunity estimators derived from HAI titer distributions were then constructed and evaluated: geometric mean titer (GMT), the proportion of non-naïve individuals, the proportion of the population immune, and the relative reduction in reproductive number. All four estimators demonstrated strong predictive validity for the upcoming season's predominant subtype and correlated strongly with observed cumulative incidence. Complementary simulation studies elucidated key epidemiological (e.g., pathogen transmissibility) and immunological (e.g., antibody waning rate) factors modulating these correlations. Collectively, this thesis delivers significant advances in real-time monitoring of respiratory pathogen transmission and systematic tracking of population immunity. By synthesizing methodologies across epidemiology, statistical modelling, and immunology, it provides a cohesive framework and actionable tools. These contributions give public health authorities a data-driven basis for timing vaccination campaigns, calibrating non-pharmaceutical measures, and monitoring population immunity—thereby strengthening preparedness for future respiratory threats while minimizing unnecessary societal and healthcare disruption.