Role of spatial and sociodemographic variation on SARS-CoV-2 transmission and public health interventions in England

Thesis

This thesis investigates the dynamics of SARS-CoV-2 transmission in England from October 2020 to March 2022, focusing on how mobility patterns, sociodemographic factors, and public health interventions influenced the spread of the virus. The primary motivation for this research is to provide insights that can inform more equitable and effective public health responses during pandemics, particularly in the context of emerging infectious diseases.

Using Bayesian hierarchical models, I examined spatial differences in virus transmission, and the impact of human mobility on the spread of the Alpha and Delta variants. I developed novel methodologies for understanding testing behaviors and biases across different sociodemographic groups. I also introduced Bayesian nonparametric methods, to assess the spatial and sociodemographic drivers of the dynamic clustering in transmission patterns throughout England.

Key findings include the important role of human mobility in the early spread of variants of concern. Analysis of the national COVID-19 testing program identified disparities in testing use among different sociodemographic groups and delays in reporting positive results among ethnic minorities. Additionally, mobility between areas and spatial proximity influenced synchronous transmission patterns throughout the study period.

In conclusion, this thesis contributes to the broader understanding of how infectious diseases like SARS-CoV-2 spread within a population and underscores the importance of equitable public health interventions. The methodologies developed here can be applied to future pandemics, helping to ensure that public health responses are both effective and inclusive. The findings emphasize the critical need for real time data and interdisciplinary approaches in addressing public health challenges.

Authors: Bajaj, S

• DOI: 10.5287/ora-nbg1qb7nq
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: Bayesian inference; inequity; statistical models; COVID-19; SARS-CoV-2; sociodemographics
• Subjects: Statistics