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Understanding disease spread through data visualisation has concentrated ontrends and maps. Whilst these are helpful, they neglect importantmulti-dimensional interactions between characteristics of communities. Usingthe Topological Data Analysis Ball Mapper algorithm we construct an abstractrepresentation of NUTS3 level economic data, overlaying onto it the confirmedcases of Covid-19 in England. In so doing we may understand how the diseasespreads on different socio-economical dimensions. It is observed that someareas of the characteristic space have quickly raced to the highest levels ofinfection, while others close by in the characteristic space, do not show largeinfection growth. Likewise, we see patterns emerging in very different areasthat command more monitoring. A strong contribution for Topological DataAnalysis, and the Ball Mapper algorithm especially, in comprehending dynamicepidemic data is signposted.

Visualising the Evolution of English Covid-19 Cases with Topological Data Analysis Ball Mapper