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使用带有拐点的 IPcase 指数和相应的病例编号来识别 COVID-19 在中国造成的影响：一项观察性研究。

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.

机构信息

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.

出版信息

Int J Environ Res Public Health. 2021 Feb 18;18(4):1994. doi: 10.3390/ijerph18041994.

DOI:10.3390/ijerph18041994
PMID:33670825
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7923186/
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, < 0.001) among areas in China. The IRT model with the AAC is recommended to determine the IP days during the COVID-19 pandemic.

摘要

2019 年冠状病毒病（COVID-19）发生于武汉，并迅速在全球蔓延。评估 COVID-19 的影响是当务之急。拐点（IP）和相应的累计感染人数（CNIC）是两个应该共同考虑的观点，以区分与 COVID-19 作斗争的影响（SACOVID）。CNIC 数据于 2020 年 11 月 23 日从 GitHub 网站下载。提出了项目反应理论模型（IRT），为中国的每个省/直辖市/地区绘制了钟形曲线。ipcase-index 通过将 IP 天数乘以相应的 CNIC 来确定。IRT 模型被参数化，IP 天数通过绝对优势系数（AAC）确定。使用森林图比较 SACOVID 的差异。在观察性研究中，COVID-19 最严重的三个地区是香港、上海和湖北，其 ipcase 指数分别为 1744、723 和 698，而 IP 天数最多的三个地区是云南、四川和天津，分别为 5、51 和 119。中国各地区之间的 IP 天数差异显著（χ2=5065666，df=32，<0.001）。建议使用 AAC 的 IRT 模型来确定 COVID-19 大流行期间的 IP 天数。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40f1/7923186/cfcb8a1d4b22/ijerph-18-01994-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40f1/7923186/f122c3720ebd/ijerph-18-01994-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40f1/7923186/51cca61429e8/ijerph-18-01994-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40f1/7923186/42112555c89b/ijerph-18-01994-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40f1/7923186/2e5a34db8386/ijerph-18-01994-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40f1/7923186/d76add81a44e/ijerph-18-01994-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40f1/7923186/c1db5de0f76c/ijerph-18-01994-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40f1/7923186/dc861caee5c3/ijerph-18-01994-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40f1/7923186/cfcb8a1d4b22/ijerph-18-01994-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40f1/7923186/f122c3720ebd/ijerph-18-01994-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40f1/7923186/51cca61429e8/ijerph-18-01994-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40f1/7923186/42112555c89b/ijerph-18-01994-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40f1/7923186/2e5a34db8386/ijerph-18-01994-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40f1/7923186/d76add81a44e/ijerph-18-01994-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40f1/7923186/c1db5de0f76c/ijerph-18-01994-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40f1/7923186/dc861caee5c3/ijerph-18-01994-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40f1/7923186/cfcb8a1d4b22/ijerph-18-01994-g008.jpg

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[4]
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[7]
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[8]
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本文引用的文献

[1]
Seroprevalence and asymptomatic carrier status of SARS-CoV-2 in Wuhan City and other places of China.

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Nonlinear Dyn. 2020

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