About Us

Who are we?

We are a team of economists, geographers, engineers and mathematicians from the University if Oxford, united by our view that mobile phone data may help to understand, predict and control the course of Covid-19.


Epidemic spreading is mediated by human contacts and it is thus critical to understand, in real time, how governmental measures translate into change of behaviour in the UK. For this purpose, we have implemented a set of metrics in order to capture mobility patterns from mobile data, that can be used by policy makers to inform their decisions and by modellers to calibrate the epidemic models.

Which data?

The population movement, POI and flow data that we use is provided by Cuebiq, which is a location intelligence and measurement platform. Through its Data for Good programme, Cuebiq provides access to aggregated and privacy-enhanced mobility data for academic research and humanitarian initiatives. This first-party data is collected via anonymised users who have opted-in to provide access to their location data anonymously, through a GDPR-compliant framework. The underlying anonymised data is collected via smartphone applications from users who have opted-in with complete anonymity regarding their personal identity and personal details. At the device level, iOS and Android operating systems combine various location data sources, including GPS, wifi, beacons and network. These data sources provide geographical coordinates across a range of accuracy. Location accuracy is determined on a device-by-device basis and is therefore variable in nature. In terms of sampling frequency, data is aggregated over five minute windows.

Can you share the data?

The original data is confidential but all the statistical indicators used to prepare figures on this site can be downloaded. In addition, we are open to recommendations, so do not hesitate to send us suggestions of alternative metrics, which may be more appropriate from your modelling perspective, and we will be happy to look into it if relevant.

Is my privacy protected?

Yes. Beyond simple anonymisation, in order to preserve privacy the data provider aggregates sensitive locations such as home and work areas to the Geohash 6 level.

Project Team

Project Co-Directors

Matthias Qian

Department of Economics, University of Oxford

Adam Saunders

SKOPE, Department of Education, University of Oxford

Project Contributors

Daniel Pesch

Saïd Business School, University of Oxford

Steven Reece

Department of Engineering Science, University of Oxford

Bill Wildi

Tony Blair Institute for Global Change

Won Do Lee

Transport Studies Unit, School of Geography and the Environment, University of Oxford

Xiaowen Dong

Department of Engineering Science, University of Oxford

Renaud Lambiotte

Mathematical Institute, University of Oxford

Lucas Kruitwagen

School of Geography and the Environment, University of Oxford

Please send enquires to contact@ox-ai.com

Oxford Covid 19 Response

University of Oxford
Wellington Square
United Kingdom