PhD Studentship: Multivariate Statistical Methods for the Analysis of Serological Data Development and Application

Serological surveillance provides crucial insights into population-level immunity and exposure patterns to infectious diseases. The measurement of antibody responses presents rich multivariate data structures that can reveal complex patterns of pathogen exposure, immunological interactions, and population immunity dynamics. Multiplex immunoassays, which enable simultaneous measurement of antibody responses to multiple pathogens from a single blood sample, represent a particularly valuable technology for diagnosing and monitoring neglected tropical diseases, emerging infections, and vaccine-preventable diseases in resource-limited settings. However, the statistical challenges they present exemplify broader methodological gaps in serological data analysis that this PhD project will aim to address. Current statistical approaches for analyzing such data rely heavily on methods that treat each antibody measurement independently and understanding how to rigorously model the correlation across multiple antibodies remains an open problem.

This PhD project will develop novel multivariate statistical methods for the analysis of serological data. The student will be encouraged to build upon recently developed latent variable modeling methodology (Giorgi & Wallin, 2025; arxiv.org/abs/2512.14504) and will explore extensions of this framework to address diverse challenges in serological surveillance. Possible directions of model development include extending laten variable models for the joint analysis of multiple antibody responses, capturing dependencies that reflect shared underlying immunological processes while accounting for complex age-dependent patterns and population heterogeneity. Multiplex immunoassay data will serve as a major case study throughout the research, though the methods developed will have wider applicability to other omics applications.

The project will be conducted in collaboration with leading research groups: Prof. Chris Drakeley at the London School of Hygiene & Tropical Medicine will provide expertise in tropical disease immunology and access to diverse serological surveillance datasets; Prof. Jonas Wallin at Lund University will contribute to computational methodology and theoretical foundations, as well as model development; and public health experts from The Task Force for Global Health in Atlanta will ensure methods address practical surveillance needs. All methodological developments will be implemented in a comprehensive, publicly available R package with extensive documentation to maximize accessibility for epidemiologists and public health practitioners.

While the foundational methodology is established, this PhD offers flexibility for the student to pursue different research directions depending on their interests and strengths. The student is expected to have a strong background in statistics and a strong motivation to work in public health, developing novel biostatistical methods for the analysis of infectious disease data.

NOTE: For administrative purposes, interested applicants will initially be asked to apply for a PhD in Medicine as part of the selection process. Depending on the candidate’s background and interests, the PhD registration and final degree title may instead be changed to a PhD in Statistics. This can be discussed and agreed with the main supervisor, Prof. Emanuele Giorgi, following selection.

Funding notes:

The studentship provides financial support over three years (October 2026 to September 2029). This includes a stipend totalling £66,027, paid at £21,383 in Year 1, £22,003 in Year 2, and £22,641 in Year 3. Tuition fees are fully covered for the same period, amounting to £15,906 (£5,151 in Year 1, £5,301 in Year 2, and £5,454 in Year 3). In addition, £10,000 is allocated for research consumables, comprising £2,500 per year for three years, plus an additional £2,500 to support open access publication costs.

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