Insights from serological surveillance in infectious disease epidemiology

Traditional infectious disease surveillance methods rely on testing of symptomatic individuals. However, such methods are often poor indicators of the true burden of infection in a population due to varying rates of subclinical infections, non-specific symptoms, and heterogeneity in health seeking behaviour. Serological surveillance, which tests for immune markers such as antibodies generated in response to an infection, provides invaluable information relating to the underlying burden and dynamics of infectious diseases. In this thesis I developed and applied analytical methods to maximize inferences from IgG antibody data and investigate epidemiological questions relating to the transmission and immune dynamics of human pathogens. Using SARS-CoV-2 seroprevalence studies and age-specific COVID-19 death data, I refined estimates of SARS-CoV-2 infection fatality ratios and inferred infected population proportions in multiple countries. Using longitudinal dengue antibody data from Thailand I reconstructed the dynamics of maternal dengue antibodies and found that antibody-dependent enhancement mechanisms were best able to explain observed age patterns of infant dengue hospitalizations. I developed an analytical framework for the analysis of multi-pathogen serological data to disentangle the antibody responses of related, cross-reacting pathogens. I conducted simulation testing of model performance and applied this framework to serological data for ten arboviruses in Bangladesh. I inferred the levels of between-virus antibody cross-reactivity and reconstructed the spatiotemporal transmission dynamics of each present virus, revealing heterogeneous burdens even at small spatial scales. Overall, serological surveillance represents an important opportunity to fill the gaps left by traditional disease surveillance methods, allowing an increased understanding of pathogen transmission dynamics, rates of pathogen severity, antigenic landscapes, and immune correlates or mechanisms of disease risk and/or protection. The work of this thesis demonstrates how modern analytical methodologies can maximize the epidemiological inferences made from serological data and inform relevant public health questions.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2024

Author dc:creator

• O'Driscoll, Megan

Advisor dc:contributor.advisor

• Salje, Henrik

• epidemiology
• infectious diseases
• mathematical modelling

Licence

• https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/59d1e721-6211-4511-83bb-2e11afc0dc55/download
• https://creativecommons.org/licenses/by/4.0/

Language dc:language

eng

Author Identifier

0000-0002-7972-5703

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/365538