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.
Key publications
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Reemergence of Cosmopolitan Genotype Dengue Virus Serotype 2, Southern Vietnam.
Journal article
Tran VT. et al, (2023), Emerging infectious diseases, 29, 2180 - 2182
Recent publications
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The overlapping global distribution of dengue, chikungunya, Zika and yellow fever.
Journal article
Lim A. et al, (2025), Nature communications, 16
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Improving mobility data for infectious disease research.
Journal article
Kostandova N. et al, (2025), Nat Hum Behav
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Artificial intelligence for modelling infectious disease epidemics
Journal article
Kraemer MUG. et al, (2025), Nature, 638, 623 - 635
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Disruption of seasonal influenza circulation and evolution during the 2009 H1N1 and COVID-19 pandemics in Southeastern Asia.
Journal article
Chen Z. et al, (2025), Nature communications, 16
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Toward optimal disease surveillance with graph-based active learning.
Journal article
Tsui JL-H. et al, (2024), Proceedings of the National Academy of Sciences of the United States of America, 121