About
Sam Moore serves as a Lecturer in Biostatistics at Lancaster Medical School, Lancaster University, and is an active member of the Data Science Institute. His academic work centers on biostatistical applications in infectious disease dynamics with direct policy implications for public health agencies.
Moore's research specializes in computational epidemic modeling for vaccine-preventable diseases, particularly COVID-19 and measles. He develops sophisticated statistical frameworks that integrate socio-economic variables to evaluate intervention effectiveness, with emphasis on vaccination strategies and transmission dynamics. His methodological approach combines compartmental modeling with real-world health data analysis to generate actionable insights for policymakers.
His 2024 publication in PLoS Computational Biology established evidence that prioritizing older populations for COVID-19 boosters maximizes public health outcomes across diverse socio-economic contexts, reflecting his consistent focus on equitable intervention design.
Moore currently leads multiple research initiatives including a Data Science Institute project assessing measles resurgence due to declining vaccination rates (2024-2026), evaluation of school testing effectiveness during the pandemic (2024), and contributed to WHO SAGE modeling on variant-adapted vaccines (2023). These projects demonstrate sustained funding and policy relevance.
He is an integral member of the CHICAS research group where he applies advanced computational statistics to health informatics challenges, particularly in infectious disease surveillance and intervention modeling.