We are a group of scientists based at the Big Data Institute at Oxford University with expertise in infectious disease epidemiology, medicine, virology, immunology, mathematical modeling, phylogenetics, behavioral economics, and ethics. We have redirected our usual research on stopping virus spread to address the new pathogen SARS-CoV-2. Our research is informing the design and technology behind the contact tracing app developed by the NHS. 

We started by asking whether, from a mathematical point of view, it is possible to stop the epidemic. We concluded that the epidemic can be stopped if contact tracing is sufficiently fast, efficient, and scalable. We suggested that the best way to achieve this is by using a mobile app. Read our paper published in Science or explore the results on our interactive dashboard below.

We have also developed an agent-based epidemic simulation to enable epidemiologists, app designers, and policymakers to compare a variety of algorithms for digital contact tracing. Our model is open-source and accessible here. Read our report detailing the simulation findings or this blog post for a brief overview.

We have recently examined the effectiveness of the centralized and decentralized data architectures for app-based contact tracing in suppressing the COVID-19 epidemic. We compare these two approaches from an epidemiological point of view, keeping in mind that this intervention aims to maintain control of the epidemic as part of Test, Track and Trace, whilst maximizing freedom of movement and maintaining privacy. We also outline five key epidemiological and public health requirements which COVID-19 contact tracing apps should satisfy here. Read our full report with a comprehensive overview or our blog post for a concise summary.

SOURCE:  https://045.medsci.ox.ac.uk/