Title:

Reconstructing kinetics of antibodies against dengue virus to triage individuals with high-risk for severe disease

Summary:

Each year, over 100 million dengue virus (DENV) infections happen world-wide leading to overstrained healthcare facilities in settings with high transmission. While most infections are subclinical or self-resolving, intensive monitoring and care is needed to prevent mortality in severe cases. To date, the most recognized risk for severe disease is pre-existing immunity from prior exposures. While the relatively rapid rise of anti-DENV IgG compared to IgM has been used for multiple decades to differentiate secondary DENV infections (high-risk) from primary, discriminatory power of this approach remains elusive. This owes to the lack of quantitative characterization of the anti-DENV IgG and IgM kinetics, especially in the early phase of illness when triage is most needed.

In this project, you will develop mathematical models to quantitatively characterize anti-DENV IgG, IgM kinetics during acute infections, develop approaches to best discriminate secondary from primary infections, and assess the utility of your developed approach in triaging severe infections for care. Through this project, you will learn to work with latent class models, develop likelihood functions to fit models to the data, assess performance of models to predict unseen data, and produce high quality figures and writings for scientific publications.

Required knowledge:

• Working knowledge of R-programming language and a basic understanding of probabilities is essential to the advancement of this project.

Supervisors:

Day-to-day supervisor: Angkana Huang (HAT) (ah2223@cam.ac.uk)

Co-supervisor: Henrik Salje (hs743@cam.ac.uk)

Department of Genetics