Developing the next generation of epidemiological analytics using individual- level virologic and serologic data

SARS-CoV-2 has revealed serious inadequacies in how we track infectious disease spread. Despite progress in quantifying individual-level biomarkers at scale, inferring transmission dynamics to inform public health decisions still largely depends on counting cases. Recently, I showed that individual-level viral load measurements from a single cross-sectional sample of RT-qPCR data can accurately estimate an epidemic's trajectory, overcoming many limitations of case count-based surveillance. Here, I will build a new generation of outbreak analytic tools, leveraging individual-level immunological and pathogen titres to robustly estimate transmission dynamics. First, I will integrate virologic and serologic data from the UK's SARS-CoV-2 surveillance studies to create a new modelling framework coupling within-host viral and antibody kinetics with population-level dynamics. Using this framework, I will evaluate prioritization of different surveillance strategies across pandemic phases. Second, I will develop new epidemiological inference methods harnessing biological kinetics, validated using UK SARS-CoV-2 data, and evaluated for use in resource-limited settings. Finally, I will integrate viral load data with phylodynamics to improve the rapid characterization of emerging viral variants. Overall, this research will advance how we use individual-based information for infectious disease surveillance, establishing the study of viral load dynamics, or viroepidemiology, as a key tool alongside seroepidemiology and phylodynamics.