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  1. Article ; Online: Using the IPcase Index with Inflection Points and the Corresponding Case Numbers to Identify the Impact Hit by COVID-19 in China: An Observation Study.

    Wang, Lin-Yen / Chien, Tsair-Wei / Chou, Willy

    International journal of environmental research and public health

    2021  Volume 18, Issue 4

    Abstract: ... compared using a forest plot. In the observation study, the top three regions hit severely by COVID-19 were ... Assessing the impact of COVID-19 is the first and foremost concern. The inflection point (IP) and ... to draw the ogive curve for every province/metropolitan city/area in China. The ipcase-index was ...

    Abstract Coronavirus disease 2019 (COVID-19) occurred in Wuhan and rapidly spread around the world. Assessing the impact of COVID-19 is the first and foremost concern. The inflection point (IP) and the corresponding cumulative number of infected cases (CNICs) are the two viewpoints that should be jointly considered to differentiate the impact of struggling to fight against COVID-19 (SACOVID). The CNIC data were downloaded from the GitHub website on 23 November 2020. The item response theory model (IRT) was proposed to draw the ogive curve for every province/metropolitan city/area in China. The ipcase-index was determined by multiplying the IP days with the corresponding CNICs. The IRT model was parameterized, and the IP days were determined using the absolute advantage coefficient (AAC). The difference in SACOVID was compared using a forest plot. In the observation study, the top three regions hit severely by COVID-19 were Hong Kong, Shanghai, and Hubei, with IPcase indices of 1744, 723, and 698, respectively, and the top three areas with the most aberrant patterns were Yunnan, Sichuan, and Tianjin, with IP days of 5, 51, and 119, respectively. The difference in IP days was determined (χ2 = 5065666, df = 32,
    MeSH term(s) COVID-19/epidemiology ; China/epidemiology ; Cities ; Hong Kong ; Humans ; Pandemics
    Language English
    Publishing date 2021-02-18
    Publishing country Switzerland
    Document type Journal Article ; Observational Study
    ZDB-ID 2175195-X
    ISSN 1660-4601 ; 1661-7827
    ISSN (online) 1660-4601
    ISSN 1661-7827
    DOI 10.3390/ijerph18041994
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Using the IPcase Index with Inflection Points and the Corresponding Case Numbers to Identify the Impact Hit by COVID-19 in China

    Lin-Yen Wang / Tsair-Wei Chien / Willy Chou

    International Journal of Environmental Research and Public Health, Vol 18, Iss 1994, p

    An Observation Study

    2021  Volume 1994

    Abstract: ... compared using a forest plot. In the observation study, the top three regions hit severely by COVID-19 were ... Assessing the impact of COVID-19 is the first and foremost concern. The inflection point (IP) and ... to draw the ogive curve for every province/metropolitan city/area in China. The ipcase-index was ...

    Abstract Coronavirus disease 2019 (COVID-19) occurred in Wuhan and rapidly spread around the world. Assessing the impact of COVID-19 is the first and foremost concern. The inflection point (IP) and the corresponding cumulative number of infected cases (CNICs) are the two viewpoints that should be jointly considered to differentiate the impact of struggling to fight against COVID-19 (SACOVID). The CNIC data were downloaded from the GitHub website on 23 November 2020. The item response theory model (IRT) was proposed to draw the ogive curve for every province/metropolitan city/area in China. The ipcase-index was determined by multiplying the IP days with the corresponding CNICs. The IRT model was parameterized, and the IP days were determined using the absolute advantage coefficient (AAC). The difference in SACOVID was compared using a forest plot. In the observation study, the top three regions hit severely by COVID-19 were Hong Kong, Shanghai, and Hubei, with IPcase indices of 1744, 723, and 698, respectively, and the top three areas with the most aberrant patterns were Yunnan, Sichuan, and Tianjin, with IP days of 5, 51, and 119, respectively. The difference in IP days was determined (χ2 = 5065666, df = 32, p < 0.001) among areas in China. The IRT model with the AAC is recommended to determine the IP days during the COVID-19 pandemic.
    Keywords item response theory ; ogive curve ; absolute advantage coefficient ; infection point ; COVID-19 ; forest plot ; Medicine ; R
    Subject code 910
    Language English
    Publishing date 2021-02-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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