Moritz Kraemer
Associate Professor of Computational and Genomic Epidemiology
Moritz's research addresses questions related to the spatial spread of infectious diseases. Specifically he is concerned with the impact of human behaviour on pathogen dynamics and how novel insights can be best translated into effective and sustainable policies to reduce the burden from infectious pathogens. Our groups research melds techniques from statistics, epidemiology, software engineering, genomics, ecology, and network science.
Moritz finished his DPhil in 2017, was a NIH research fellow at Harvard Medical School, and is now a Branco Weiss Research Fellow in the Department of Biology and Reuben College at the University of Oxford and a Lead Researcher on the Oxford Martin Programme on Pandemic Genomics. He is also the co-founder of Global health (https://www.global.health/), an interdisciplinary programme to advance data science and software engineering capacity in health research and policy.
I lead a group of postdocs, software developers, research assistants and DPhil students and we are recruiting postdocs at regular intervals and can supervise DPhil students from programmes at the University of Oxford. Please don't hesitate to get in touch if you are interested in joining the group or collaborating with us.
Recent publications
Multimodal Data Approaches for Examining the 2024-2025 Highly Pathogenic Avian Influenza Outbreak in the United States: Descriptive Study.
Journal article
Sopko J. et al, (2026), JMIR Public Health Surveill, 12
Federated analysis of incubation period distributions using individual-level observed data and heterogeneous summary statistics
Preprint
Morgenstern C. et al, (2026)
Large-scale genomic surveillance reveals immunosuppression drives mutation dynamics in persistent SARS-CoV-2 infections.
Journal article
Khurana MP. et al, (2026), Nature communications
Unified framework for the ingestion of early epidemic data for downstream data analytics
Preprint
Kamau E. et al, (2026)
Unified framework for the ingestion of early epidemic data for downstream data analytics
Journal article
Kamau E. et al, (2026), Wellcome Open Research, 10, 524 - 524