English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We study Covid-19 spreading dynamics underlying 84 curves of daily Covid-19infection rates pertaining to 84 districts belonging to the largest sevencities in Taiwan during her pristine surge period. Our computationaldevelopments begin with selecting and extracting 18 features from each smootheddistrict-specific curve. This step of computing effort allows unstructured datato be converted into structured data, with which we then demonstrate asymmetricgrowth and decline dynamics among all involved curves. Specifically, based onTheoretical Information measurements of conditional entropy and mutualinformation, we compute major factors of order-1 and order-2 that revealsignificant effects on affecting the curves' peak value and curvature at peak,which are two essential features characterizing all the curves. Further, weinvestigate and demonstrate major factors determining the geographic andsocial-economic induced behavioral effects by encoding each of these 84districts with two binary characteristics: North-vs-South andUnban-vs-suburban. Furthermore, based on this data-driven knowledge on thedistrict scale, we go on to study fine-scale behavioral effects on infectiousdisease spreading through similarity among 96 age-group-specific curves ofdaily infection rate within 12 urban districts of Taipei and 12 suburbandistricts of New Taipei City, which counts for almost one-quarter of the islandnation's total population. We conclude that human living, traveling, andworking behaviors do implicitly affect the spreading dynamics of Covid-19across Taiwan profoundly.

Unraveling implicit human behavioral effects on dynamic characteristics of Covid-19 daily infection rates in Taiwan