Supporting data for PhD Thesis "Evaluating public health measures and transmission dynamics for SARS-CoV-2 and other respiratory viruses"

Supporting files and datasets brief description.

Submitted by Benjamin R. Young for the degree of Doctor of Philosophy at The University of Hong Kong in February 2025

These files supports the research presented in chapters 4 through 8 of the PhD thesis, which evaluates the effects of specific public health interventions in Hong Kong. Using COVID-19 line lists and publicly available mass testing mandates, the research analyses the frequency of residential clusters and building-wide compulsory testing. It examines the role of severe disease data in estimating SARS-CoV-2 transmission dynamics and calibrates a compartmental model to simulate a large Omicron epidemic in Hong Kong. The calibrated model is used to conduct counterfactual simulations assessing the impact of vaccinations and prolonged school closures during the epidemic. Additionally, scenario simulations evaluate the impact of annual school holidays and pandemic-era NPIs on the transmission of seasonal endemic respiratory viruses.

This dataset is intended for researchers in epidemiology and public health. It includes .rds files corresponding to specific figures, such as vaccination coverage (e.g., Fig2_vacrates.rds), vaccine effectiveness (Fig4_samp_VE_ests.rds), and NPI impacts (Fig6_IntCombDiff.rds). Files like Fig7_NPI_shrt_ageAR.rds explore age-specific attack rates and long-term prevalence trends. While some datasets are omitted due to data-sharing restrictions, a detailed data dictionary is provided. All datasets are modified to be anonymised, and only presented as aggregate data counts where applicable. The data is reproducible with the accompanying R scripts.