  Understanding disease spread through data visualisation has concentrated on
trends and maps. Whilst these are helpful, they neglect important
multi-dimensional interactions between characteristics of communities. Using
the Topological Data Analysis Ball Mapper algorithm we construct an abstract
representation of NUTS3 level economic data, overlaying onto it the confirmed
cases of Covid-19 in England. In so doing we may understand how the disease
spreads on different socio-economical dimensions. It is observed that some
areas of the characteristic space have quickly raced to the highest levels of
infection, while others close by in the characteristic space, do not show large
infection growth. Likewise, we see patterns emerging in very different areas
that command more monitoring. A strong contribution for Topological Data
Analysis, and the Ball Mapper algorithm especially, in comprehending dynamic
epidemic data is signposted.
