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Tracking COVID-19

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i-sense researchers have been analysing and modeling various forms of anonymised and aggregated data, including online search data and geospatial data, to help better understand the COVID-19 pandemic.  

Tracking COVID-19 using online search data

i-sense researchers from University College London, led by Dr Vasileios Lampos, in collaboration with Public Health England, Microsoft Research, and Harvard Medical School are looking at ways of tracking COVID-19 using online search data to better understand the true extent of community spread.

The model analyses a set of specific key search terms from Google search queries on a daily basis. It proposes unsupervised machine learning models for COVID-19 based on weighted symptom categories taken from the National Health Service (NHS) first few hundred (FF100) survey as well as an expanded version of that incorporating the symptom of ‘loss of smell’ and generic terms about COVID-19.

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Observing mobility using geospatial data  

i-sense researchers published a pre-print on arXiv, which draws on geospatial data highlighting local variation in mobility across the country, as well as case rate data, which shows potential hotspots of infection. Anonymised and aggregated local authority level mobility data, made available from project collaborators O2 (Telefonica UK), helps highlight the varying adherence to lockdown and the response to the easing of restrictions by understanding how people are moving to, from, or within an area.

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