Unraveling Natural and Vaccine-Elicited Immunity to Lassa Fever (UNVEIL)

Lassa fever (LF) is a significant public health issue, with hundreds of thousands of suspected cases annually and a substantial fatality rate. Despite the urgent need for countermeasures, LF lacks licensed vaccines partially due to the challenge of determining what constitutes effective immunity. This proposal aims to address this gap by identifying reliable indicators of LF protection (CoP) to guide vaccine development. Using advanced modeling and machine learning techniques, our interdisciplinary team will integrate data from animal models, vaccine studies, and clinical data from LF-endemic areas (Nigeria, Sierra Leone) to predict vaccine efficacy in humans. The goal is to create a robust CoP model and validated assays that streamline vaccine development, reducing costs and time to licensure. This approach can be adapted for evaluating other vaccines and addressing other emerging infectious diseases with epidemic potential, contributing to global health improvements. Modernising the vaccine pipeline with predictive modeling may also catalyse alternative licensure avenues.