Impact of insufficient detection in COVID-19 outbreaks

The COVID-19 (novel coronavirus disease 2019) pandemic has tremendously impacted global health and economics. Early detection of COVID-19 infections is important for patient treatment and for controlling the epidemic. However, many countries/regions suffer from a shortage of nucleic acid testing (NA...

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Autores principales:	Yue Deng, Siming Xing, Meixia Zhu, Jinzhi Lei
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	covid-19 nucleic acid testing detection probability epidemic scale Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/59f88786036145a1b463a1d57bd7af57
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spelling	oai:doaj.org-article:59f88786036145a1b463a1d57bd7af572021-11-29T06:36:34ZImpact of insufficient detection in COVID-19 outbreaks10.3934/mbe.20214761551-0018https://doaj.org/article/59f88786036145a1b463a1d57bd7af572021-11-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021476?viewType=HTMLhttps://doaj.org/toc/1551-0018The COVID-19 (novel coronavirus disease 2019) pandemic has tremendously impacted global health and economics. Early detection of COVID-19 infections is important for patient treatment and for controlling the epidemic. However, many countries/regions suffer from a shortage of nucleic acid testing (NAT) due to either resource limitations or epidemic control measures. The exact number of infective cases is mostly unknown in counties/regions with insufficient NAT, which has been a major issue in predicting and controlling the epidemic. In this paper, we propose a mathematical model to quantitatively identify the influences of insufficient detection on the COVID-19 epidemic. We extend the classical SEIR (susceptible-exposed-infections-recovered) model to include random detections which are described by Poisson processes. We apply the model to the epidemic in Guam, Texas, the Virgin Islands, and Wyoming in the United States and determine the detection probabilities by fitting model simulations with the reported number of infected, recovered, and dead cases. We further study the effects of varying the detection probabilities and show that low level-detection probabilities significantly affect the epidemic; increasing the detection probability of asymptomatic infections can effectively reduce the the scale of the epidemic. This study suggests that early detection is important for the control of the COVID-19 epidemic.Yue Deng Siming XingMeixia ZhuJinzhi Lei AIMS Pressarticlecovid-19nucleic acid testingdetection probabilityepidemic scaleBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 6, Pp 9727-9742 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	covid-19 nucleic acid testing detection probability epidemic scale Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	covid-19 nucleic acid testing detection probability epidemic scale Biotechnology TP248.13-248.65 Mathematics QA1-939 Yue Deng Siming Xing Meixia Zhu Jinzhi Lei Impact of insufficient detection in COVID-19 outbreaks
description	The COVID-19 (novel coronavirus disease 2019) pandemic has tremendously impacted global health and economics. Early detection of COVID-19 infections is important for patient treatment and for controlling the epidemic. However, many countries/regions suffer from a shortage of nucleic acid testing (NAT) due to either resource limitations or epidemic control measures. The exact number of infective cases is mostly unknown in counties/regions with insufficient NAT, which has been a major issue in predicting and controlling the epidemic. In this paper, we propose a mathematical model to quantitatively identify the influences of insufficient detection on the COVID-19 epidemic. We extend the classical SEIR (susceptible-exposed-infections-recovered) model to include random detections which are described by Poisson processes. We apply the model to the epidemic in Guam, Texas, the Virgin Islands, and Wyoming in the United States and determine the detection probabilities by fitting model simulations with the reported number of infected, recovered, and dead cases. We further study the effects of varying the detection probabilities and show that low level-detection probabilities significantly affect the epidemic; increasing the detection probability of asymptomatic infections can effectively reduce the the scale of the epidemic. This study suggests that early detection is important for the control of the COVID-19 epidemic.
format	article
author	Yue Deng Siming Xing Meixia Zhu Jinzhi Lei
author_facet	Yue Deng Siming Xing Meixia Zhu Jinzhi Lei
author_sort	Yue Deng
title	Impact of insufficient detection in COVID-19 outbreaks
title_short	Impact of insufficient detection in COVID-19 outbreaks
title_full	Impact of insufficient detection in COVID-19 outbreaks
title_fullStr	Impact of insufficient detection in COVID-19 outbreaks
title_full_unstemmed	Impact of insufficient detection in COVID-19 outbreaks
title_sort	impact of insufficient detection in covid-19 outbreaks
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/59f88786036145a1b463a1d57bd7af57
work_keys_str_mv	AT yuedeng impactofinsufficientdetectionincovid19outbreaks AT simingxing impactofinsufficientdetectionincovid19outbreaks AT meixiazhu impactofinsufficientdetectionincovid19outbreaks AT jinzhilei impactofinsufficientdetectionincovid19outbreaks
_version_	1718407524718215168

Impact of insufficient detection in COVID-19 outbreaks

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