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Exploring the impact of under-reported cases on the COVID-19 spatiotemporal distributions using healthcare workers infection data
A timely understanding of the spatiotemporal pattern and development trend of COVID-19 is critical for timely prevention and control. However, the under-reporting of casesis widespread in fields associated with public health. It is also possible to draw biased inferences and formulate inappropriate...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8761553/ https://www.ncbi.nlm.nih.gov/pubmed/35068649 http://dx.doi.org/10.1016/j.cities.2022.103593 |
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author | Wang, Peixiao Hu, Tao Liu, Hongqiang Zhu, Xinyan |
author_facet | Wang, Peixiao Hu, Tao Liu, Hongqiang Zhu, Xinyan |
author_sort | Wang, Peixiao |
collection | PubMed |
description | A timely understanding of the spatiotemporal pattern and development trend of COVID-19 is critical for timely prevention and control. However, the under-reporting of casesis widespread in fields associated with public health. It is also possible to draw biased inferences and formulate inappropriate prevention and control policies if the phenomenon of under-reporting is not taken into account. Therefore, in this paper, a novel framework was proposed to explore the impact of under-reporting on COVID-19 spatiotemporal distributions, and empirical analysis was carried out using infection data of healthcare workers in Wuhan and Hubei (excluding Wuhan). The results show that (1) the lognormal distribution was the most suitable to describe the evolution of epidemic with time; (2) the estimated peak infection time of the reported cases lagged the peak infection time of the healthcare worker cases, and the estimated infection time interval of the reported cases was smaller than that of the healthcare worker cases. (3) The impact of under-reporting cases on the early stages of the pandemic was greater than that on its later stages, and the impact on the early onset area was greater than that on the late onset area. (4) Although the number of reported cases was lower than the actual number of cases, a high spatial correlation existed between the cumulatively reported cases and healthcare worker cases. The proposed framework of this study is highly extensible, and relevant researchers can use data sources from other counties to carry out similar research. |
format | Online Article Text |
id | pubmed-8761553 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-87615532022-01-18 Exploring the impact of under-reported cases on the COVID-19 spatiotemporal distributions using healthcare workers infection data Wang, Peixiao Hu, Tao Liu, Hongqiang Zhu, Xinyan Cities Article A timely understanding of the spatiotemporal pattern and development trend of COVID-19 is critical for timely prevention and control. However, the under-reporting of casesis widespread in fields associated with public health. It is also possible to draw biased inferences and formulate inappropriate prevention and control policies if the phenomenon of under-reporting is not taken into account. Therefore, in this paper, a novel framework was proposed to explore the impact of under-reporting on COVID-19 spatiotemporal distributions, and empirical analysis was carried out using infection data of healthcare workers in Wuhan and Hubei (excluding Wuhan). The results show that (1) the lognormal distribution was the most suitable to describe the evolution of epidemic with time; (2) the estimated peak infection time of the reported cases lagged the peak infection time of the healthcare worker cases, and the estimated infection time interval of the reported cases was smaller than that of the healthcare worker cases. (3) The impact of under-reporting cases on the early stages of the pandemic was greater than that on its later stages, and the impact on the early onset area was greater than that on the late onset area. (4) Although the number of reported cases was lower than the actual number of cases, a high spatial correlation existed between the cumulatively reported cases and healthcare worker cases. The proposed framework of this study is highly extensible, and relevant researchers can use data sources from other counties to carry out similar research. Elsevier Ltd. 2022-04 2022-01-17 /pmc/articles/PMC8761553/ /pubmed/35068649 http://dx.doi.org/10.1016/j.cities.2022.103593 Text en © 2022 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Wang, Peixiao Hu, Tao Liu, Hongqiang Zhu, Xinyan Exploring the impact of under-reported cases on the COVID-19 spatiotemporal distributions using healthcare workers infection data |
title | Exploring the impact of under-reported cases on the COVID-19 spatiotemporal distributions using healthcare workers infection data |
title_full | Exploring the impact of under-reported cases on the COVID-19 spatiotemporal distributions using healthcare workers infection data |
title_fullStr | Exploring the impact of under-reported cases on the COVID-19 spatiotemporal distributions using healthcare workers infection data |
title_full_unstemmed | Exploring the impact of under-reported cases on the COVID-19 spatiotemporal distributions using healthcare workers infection data |
title_short | Exploring the impact of under-reported cases on the COVID-19 spatiotemporal distributions using healthcare workers infection data |
title_sort | exploring the impact of under-reported cases on the covid-19 spatiotemporal distributions using healthcare workers infection data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8761553/ https://www.ncbi.nlm.nih.gov/pubmed/35068649 http://dx.doi.org/10.1016/j.cities.2022.103593 |
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