Covid cases highest in Hunts since pandemic began, new figures reveal

Coronavirus

Huntingdonshire Covid cases highest in county for festive period. - Credit: Archant

Huntingdonshire had the highest increase in Covid-19 cases in Cambridgeshire during the Christmas week – as new data estimates more than 50,000 people in the county may have had the virus without it being recorded. 

The stark rise in figures come as the new variant of Covid-19 - which is 50 to 70 per cent more transmissible - has spread throughout communities. 

The infection rate in Huntingdonshire for the week December 27 to January 3 was just above the national average, at 525 cases per 100,000 - up from 282.1 the week before. 

There were 934 cases in the district in that week, up by 436 (86.9 per cent). 

The seven-day rolling period to January 3 was the worst for the district in terms of case numbers since the pandemic began. 

This meant it was the highest week-on-week rise in any part of Cambridgeshire. 

Case numbers had doubled in all areas of Huntingdonshire - with Somersham, Riptons and Raveleys having one of the highest records in the county, with 100 cases, while St Neots Eynesbury had 99 and Brampton, the Stukeleys and the Alconburys had 66. 

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There have been seven coronavirus-related deaths in the district this year to date, with the latest two confirmed on Friday January 8.  

There had been 148 registered by December 25 2020.

New analysis by Edge Health also revealed that the true number of coronavirus infections in the county could stand at 65,992 – a difference of 51,040 in comparison to those officially recorded at 14,952. 

It would mean 10 per cent of the population had in fact been infected. 

The new modelling suggested that one in five people in England may have had the virus, equivalent to 12.4 million people and 22 per cent of the population, as of 3 January. 

The government’s test-and-trace programme had detected 2.4 million cases by the same date. 

The model estimates the number of cases in areas by comparing its number of deaths against an estimated infection fatality rate. 

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