Respiratory infectious diseases (RID) have significant health and social impacts globally, and the speed of transmission and the scale of outbreaks pose significant challenges to public health systems and social stability. As the two major respiratory infectious diseases, influenza and SARS-CoV-2 are both highly contagious and clinically complex. Influenza causes large-scale outbreaks every year, posing a serious threat, especially to vulnerable populations. SARS-CoV-2 has spread rapidly across the world since its emergence in late 2019, leading to the COVID-19 pandemic and triggering public health crises worldwide. Therefore, this PhD thesis focuses on a number of important aspects of influenza and SARS-CoV-2, aiming to gain an in-depth understanding of their transmission status in the community, and prevalence in different subgroups of populations, and to construct reliable forecasting frameworks to support pandemic control and public health decision-making.


The thesis systematically analysed the transmission dynamics of SARS-CoV-2 in England through community surveillance, revealing its epidemiological trends and the prevalence of SARS-CoV-2 infection in different populations with respect to their social, economic and living characteristics. The results of the risk factor analysis demonstrated health inequality existed among ethnic groups, household size, and occupational activity, providing a scientific basis for targeted prevention and control and resource allocation. Furthermore, an efficient forecasting framework based on advanced data analysis and machine learning techniques was developed, which can accurately predict the trends of influenza and SARS-CoV-2 transmission, potentially offering a more practical tool for decision-making by the government and health departments than other currently available frameworks. These research results are expected to provide useful references for global epidemic prevention and control, as well as valuable experience for in-depth research and practice in the fields of public health and data science.