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Understanding Unreported Cases in the COVID-19 Epidemic Outbreak in Wuhan, China, and the Importance of Major Public Health Interventions
We develop a mathematical model to provide epidemic predictions for the COVID-19 epidemic in Wuhan, China. We use reported case data up to 31 January 2020 from the Chinese Center for Disease Control and Prevention and the Wuhan Municipal Health Commission to parameterize the model. From the paramete...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7150940/ https://www.ncbi.nlm.nih.gov/pubmed/32182724 http://dx.doi.org/10.3390/biology9030050 |
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author | Liu, Zhihua Magal, Pierre Seydi, Ousmane Webb, Glenn |
author_facet | Liu, Zhihua Magal, Pierre Seydi, Ousmane Webb, Glenn |
author_sort | Liu, Zhihua |
collection | PubMed |
description | We develop a mathematical model to provide epidemic predictions for the COVID-19 epidemic in Wuhan, China. We use reported case data up to 31 January 2020 from the Chinese Center for Disease Control and Prevention and the Wuhan Municipal Health Commission to parameterize the model. From the parameterized model, we identify the number of unreported cases. We then use the model to project the epidemic forward with varying levels of public health interventions. The model predictions emphasize the importance of major public health interventions in controlling COVID-19 epidemics. |
format | Online Article Text |
id | pubmed-7150940 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-71509402020-04-20 Understanding Unreported Cases in the COVID-19 Epidemic Outbreak in Wuhan, China, and the Importance of Major Public Health Interventions Liu, Zhihua Magal, Pierre Seydi, Ousmane Webb, Glenn Biology (Basel) Article We develop a mathematical model to provide epidemic predictions for the COVID-19 epidemic in Wuhan, China. We use reported case data up to 31 January 2020 from the Chinese Center for Disease Control and Prevention and the Wuhan Municipal Health Commission to parameterize the model. From the parameterized model, we identify the number of unreported cases. We then use the model to project the epidemic forward with varying levels of public health interventions. The model predictions emphasize the importance of major public health interventions in controlling COVID-19 epidemics. MDPI 2020-03-08 /pmc/articles/PMC7150940/ /pubmed/32182724 http://dx.doi.org/10.3390/biology9030050 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Liu, Zhihua Magal, Pierre Seydi, Ousmane Webb, Glenn Understanding Unreported Cases in the COVID-19 Epidemic Outbreak in Wuhan, China, and the Importance of Major Public Health Interventions |
title | Understanding Unreported Cases in the COVID-19 Epidemic Outbreak in Wuhan, China, and the Importance of Major Public Health Interventions |
title_full | Understanding Unreported Cases in the COVID-19 Epidemic Outbreak in Wuhan, China, and the Importance of Major Public Health Interventions |
title_fullStr | Understanding Unreported Cases in the COVID-19 Epidemic Outbreak in Wuhan, China, and the Importance of Major Public Health Interventions |
title_full_unstemmed | Understanding Unreported Cases in the COVID-19 Epidemic Outbreak in Wuhan, China, and the Importance of Major Public Health Interventions |
title_short | Understanding Unreported Cases in the COVID-19 Epidemic Outbreak in Wuhan, China, and the Importance of Major Public Health Interventions |
title_sort | understanding unreported cases in the covid-19 epidemic outbreak in wuhan, china, and the importance of major public health interventions |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7150940/ https://www.ncbi.nlm.nih.gov/pubmed/32182724 http://dx.doi.org/10.3390/biology9030050 |
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