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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

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  • Lin-Yen Wang

    (Department of Pediatrics, Chi-Mei Medical Center, Tainan 700, Taiwan
    Department of Childhood Education and Nursery, Chia Nan University of Pharmacy and Science, Tainan 700, Taiwan
    School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 800, Taiwan)

  • Tsair-Wei Chien

    (Department of Medical Research, Chi-Mei Medical Center, Tainan 700, Taiwan)

  • Willy Chou

    (Department of Physical Medicine and Rehabilitation, Chi Mei Hospital Chiali, Tainan 700, Taiwan)

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.

Suggested Citation

  • Lin-Yen Wang & Tsair-Wei Chien & Willy Chou, 2021. "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," IJERPH, MDPI, vol. 18(4), pages 1-16, February.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:4:p:1994-:d:501692
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    2. Kyent-Yon Yie & Tsair-Wei Chien & Yu-Tsen Yeh & Willy Chou & Shih-Bin Su, 2021. "Using Social Network Analysis to Identify Spatiotemporal Spread Patterns of COVID-19 around the World: Online Dashboard Development," IJERPH, MDPI, vol. 18(5), pages 1-15, March.
    3. Shin-Yueh Liu & Tsair-Wei Chien & Ting-Ya Yang & Yu-Tsen Yeh & Willy Chou & Julie Chi Chow, 2021. "A Bibliometric Analysis on Dengue Outbreaks in Tropical and Sub-Tropical Climates Worldwide Since 1950," IJERPH, MDPI, vol. 18(6), pages 1-15, March.

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