Evaluating reliability of tree-patterns in extreme-<i>K</i> categorical samples problems

Chou, Elizabeth P.; Hsieh, Yin-Chen; Enriquez, Sabrina; Hsieh, Fushing

doi:10.1080/00949655.2021.1951266

Cited by 1 publication

(1 citation statement)

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“…The quantification of this visible similarity can be further evaluated using the tree geometry of the HC-tree. This evaluation is performed using a binary coding scheme proposed and developed in [ 16 ].…”

Section: Growth and Decline Similarity Detailedmentioning

confidence: 99%

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

Chen,

Chou,

Chen

et al. 2024

PLoS ONE

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We investigate the dynamic characteristics of Covid-19 daily infection rates in Taiwan during its initial surge period, focusing on 79 districts within the seven largest cities. By employing computational techniques, we extract 18 features from each district-specific curve, transforming unstructured data into structured data. Our analysis reveals distinct patterns of asymmetric growth and decline among the curves. Utilizing theoretical information measurements such as conditional entropy and mutual information, we identify major factors of order-1 and order-2 that influence the peak value and curvature at the peak of the curves, crucial features characterizing the infection rates. Additionally, we examine the impact of geographic and socioeconomic factors on the curves by encoding each of the 79 districts with two binary characteristics: North-vs-South and Urban-vs-Suburban. Furthermore, leveraging this data-driven understanding at the district level, we explore the fine-scale behavioral effects on disease spread by examining the similarity among 96 age-group-specific curves within urban districts of Taipei and suburban districts of New Taipei City, which collectively represent a substantial portion of the nation’s population. Our findings highlight the implicit influence of human behaviors related to living, traveling, and working on the dynamics of Covid-19 transmission in Taiwan.

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Section: Growth and Decline Similarity Detailedmentioning

confidence: 99%