  We study Covid-19 spreading dynamics underlying 84 curves of daily Covid-19
infection rates pertaining to 84 districts belonging to the largest seven
cities in Taiwan during her pristine surge period. Our computational
developments begin with selecting and extracting 18 features from each smoothed
district-specific curve. This step of computing effort allows unstructured data
to be converted into structured data, with which we then demonstrate asymmetric
growth and decline dynamics among all involved curves. Specifically, based on
Theoretical Information measurements of conditional entropy and mutual
information, we compute major factors of order-1 and order-2 that reveal
significant effects on affecting the curves' peak value and curvature at peak,
which are two essential features characterizing all the curves. Further, we
investigate and demonstrate major factors determining the geographic and
social-economic induced behavioral effects by encoding each of these 84
districts with two binary characteristics: North-vs-South and
Unban-vs-suburban. Furthermore, based on this data-driven knowledge on the
district scale, we go on to study fine-scale behavioral effects on infectious
disease spreading through similarity among 96 age-group-specific curves of
daily infection rate within 12 urban districts of Taipei and 12 suburban
districts of New Taipei City, which counts for almost one-quarter of the island
nation's total population. We conclude that human living, traveling, and
working behaviors do implicitly affect the spreading dynamics of Covid-19
across Taiwan profoundly.
