Purple & UST DataLabs Deliver Geographical Links Between Public WiFi and the Rates in Covid Cases

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In February, Purple and UST DataLabs worked together to see if there was a correlation between social behavior and the rates of C19 cases in Manchester, York, and London.

Through the combination of an enriched dataset created by UST, detailing the movement of the UK workforce during the pandemic, and Purple data collected from over 36,000 WiFi hotspots in the UK, we were able to view a large dataset and evaluate trends in social behaviour in areas of the country with significantly different covid restrictions.

In the following graphs from December 2020, we can see a clear correlation between the increases and decreases in covid cases which are being affected around 2 weeks after the corresponding WiFi hits number.

In the 3 weeks leading up to Christmas London was under Tier 2 restrictions which allowed the hospitality industry and non-essential businesses to operate. From the 1st of December to the 16th we can see that the number of devices pinging off of WiFi hotspots is ever increasing indicating more people were out in public places. The rate of infection creeps up in correlation with the number of people out in public.

Decision makers could have used this data to identify that the increase of people in public places leading up to Christmas was having an impact on the rates of infection and as a result could have taken action earlier as opposed until waiting to the 16th December to implement Tier 3 restrictions in London.  

During this time however Manchester was under Tier 3 restrictions with lower levels of public places open for business, in particular the hospitality industry was closed. As you can see from the graph, WiFi hits and consequently the number of people in public places was much lower and as a result the correlation between WiFi hits and the rate of infections isn’t as great, indicating that tier 3 restrictions and the reduction in the number of people out in public was having a positive impact. 

In conclusion, analyzing data such as WiFi hits could have led to the UK government identifying that the increase in the number of people in public places in the lead up to Christmas was having a significant impact on the rate of infection in London and as a result a decision to toughen restrictions could have been made earlier. In comparison, the data for Manchester shows that these tougher restrictions would have had a positive impact.

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