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Predict and Prevent COVID-19: a data driven innovation project

15 months
Approved budget:
Professor Colin Simpson
Professor Michael Baker
Professor Alexei Drummond
Professor Nigel French
Professor David Murdoch
Dr Binh Nguyen
Professor Winston Seah
Mr Andrew Sporle
Dr Mehnaz Adnan
Dr David Welch
Health issue:
Infectious disease
Proposal type:
Lay summary
The study of how infectious diseases like COVID-19 spread and how well public health interventions and therapies work is suboptimal. The main data such methods use come from reports about where people with disease are located, when they first became sick, and how many required hospitalisation. Increasingly, viral genetic samples are collected which can help to estimate how fast the virus is spreading and reveal who infected whom. We aim to create technical solutions that will address the challenges with existing methods. Using cutting-edge techniques including machine learning and improved phylodynamics, we will develop methods to combine modern sources of detailed data. We will create new approaches to use genomic data to understand the spread of this disease through the population and incorporate new data in near real-time. We will use detailed human movement and location data to independently model the structure of the population.