A retrospective assessment of non-pharmaceutical interventions and risk heterogeneities during the SARS-CoV-2 pandemic in europe
File(s)
Author(s)
Morgenstern, Christian
Type
Thesis
Abstract
Outbreaks of novel pathogens, such as SARS-CoV-2, pose considerable challenges for epidemiological modelling. Assessing the e!ectiveness of interventions and quantifying risk disparities across population subgroups in the context of complex, large-scale data are the two main focus areas of this thesis. The COVID-19 pandemic highlighted significant gaps in our understanding of the interplay between disease transmission and economic activity, as well as how public health measures influence these two outcomes. This thesis aims to add to a better understanding of the e!ects of non-pharmaceutical interventions (NPIs), vaccination and socioeconomic disparities on pandemic outcomes, with application to European data.
I make several contributions in this thesis. In Chapter 2, I construct a mixed-e!ects vector autoregressive model to examine the dynamic relationships between disease transmission, excess mortality, economic activity, and mobility across Europe. This model accounts for country-level heterogeneity in NPI stringency, vaccination uptake, and the dominant variant, providing insights into the e!ectiveness of interventions and the impact of country-specific characteristics. In Chapter 3, I investigate socioeconomic and ethnic disparities in COVID-19 outcomes in England, leveraging individual-level healthcare, vaccination, and demographic data. Using survival models, I quantify the risk heterogeneity of deprivation and ethnicity for cases, hospitalisations, and deaths, while also assessing how vaccination and public health measures mitigate these disparities. Chapter 4 provides an extension of an existing transmission model for Denmark and reviews the construction of transmission networks based on genomic sequencing and nation-scale social network data. Finally, in Chapter 5, I utilise these transmission networks to evaluate NPIs based on individual-level data and settings, such as schools and households, using social network data.
The findings underscore the importance of designing equitable policies and integrating real-time data for future outbreak preparedness. The methodologies developed here provide a foundation for more robust, data-driven decision-making in public health crises.
I make several contributions in this thesis. In Chapter 2, I construct a mixed-e!ects vector autoregressive model to examine the dynamic relationships between disease transmission, excess mortality, economic activity, and mobility across Europe. This model accounts for country-level heterogeneity in NPI stringency, vaccination uptake, and the dominant variant, providing insights into the e!ectiveness of interventions and the impact of country-specific characteristics. In Chapter 3, I investigate socioeconomic and ethnic disparities in COVID-19 outcomes in England, leveraging individual-level healthcare, vaccination, and demographic data. Using survival models, I quantify the risk heterogeneity of deprivation and ethnicity for cases, hospitalisations, and deaths, while also assessing how vaccination and public health measures mitigate these disparities. Chapter 4 provides an extension of an existing transmission model for Denmark and reviews the construction of transmission networks based on genomic sequencing and nation-scale social network data. Finally, in Chapter 5, I utilise these transmission networks to evaluate NPIs based on individual-level data and settings, such as schools and households, using social network data.
The findings underscore the importance of designing equitable policies and integrating real-time data for future outbreak preparedness. The methodologies developed here provide a foundation for more robust, data-driven decision-making in public health crises.
Version
Open Access
Date Issued
2025-09-26
Date Awarded
2025-12-01
Copyright Statement
Attribution-NonCommercial 4.0 International Licence (CC BY-NC)
License URL
Advisor
Ferguson, Neil
Bhatt, Samir
Sponsor
Schmidt Sciences (Firm)
Grant Number
G-22-63345
Publisher Department
School of Public Health
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)