Dr Moritz Kraemer

Research Interests

I am an Associate Professor at the Department of Biology, Pandemic Sciences Institute, and AI and Machine Learning Fellow at Reuben College. My lab members and I take novel, collaborative and interdisciplinary approaches to investigating the dynamics and evolution of infectious diseases, focussing on epidemiologically relevant questions to control them.

We approach these problems by developing novel methods that combine techniques from mathematical modelling, statistical inference (including AI and Machine Learning), and phylodynamics. Our work incorporates datasets capturing variation in human behaviour (e.g., migration), climate, and economics at granular scales to address questions about the drivers and dynamics of infectious diseases relevant for public health decision making. We are increasingly interested in questions related to optimal deployment of resources for disease surveillance, coordination to reduce risk of international spread of high impact pathogens, and we can leverage techniques from causal inference in epidemiology. We are working on climate sensitive diseases such as dengue, chikungunya, Yellow fever and Zika, and outbreaks and pandemics such as SARS-CoV-2, mpox, and Ebola.

Currently I co-lead the Oxford Martin School's Programme in Pandemic Genomics at the University of Oxford and am the co-founder of Global.health, a data science platform for open-access data and disease outbreak analytics. Our group members have diverse expertise in network science, public health, phylogenetics, mathematical/statistical modelling (including machine learning), and software engineering. Our group’s work is funded by the Wellcome Trust, Google.org & AI, The Rockefeller Foundation, Nation Institutes of Health (NIH), Medical Research Foundation, and European Union among others. Previous to my appointment at Oxford I was a fellow at Harvard Medical School, the University of California Berkeley, and Berlin Institute of Advanced Studies (Wissenschaftskolleg). I hold a DPhil in Statistical Epidemiology from the University of Oxford.

Group Members