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Modeling COVID-19 epidemic in Heilongjiang province, China
The Coronavirus Disease 2019 (COVID-19) surges worldwide. However, massive imported patients especially into Heilongjiang Province in China recently have been an alert for local COVID-19 outbreak. We collected data from January 23 to March 25 from Heilongjiang province and trained an ordinary differ...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256610/ https://www.ncbi.nlm.nih.gov/pubmed/32834579 http://dx.doi.org/10.1016/j.chaos.2020.109949 |
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author | Sun, Tingzhe Wang, Yan |
author_facet | Sun, Tingzhe Wang, Yan |
author_sort | Sun, Tingzhe |
collection | PubMed |
description | The Coronavirus Disease 2019 (COVID-19) surges worldwide. However, massive imported patients especially into Heilongjiang Province in China recently have been an alert for local COVID-19 outbreak. We collected data from January 23 to March 25 from Heilongjiang province and trained an ordinary differential equation model to fit the epidemic data. We extended the simulation using this trained model to characterize the effect of an imported ‘escaper’. We showed that an imported ‘escaper’ was responsible for the newly confirmed COVID-19 infections from Apr 9 to Apr 19 in Heilongjiang province. Stochastic simulations further showed that significantly increased local contacts among imported ‘escaper’, its epidemiologically associated cases and susceptible populations greatly contributed to the local outbreak of COVID-19. Meanwhile, we further found that the reported number of asymptomatic patients was markedly lower than model predictions implying a large asymptomatic pool which was not identified. We further forecasted the effect of implementing strong interventions immediately to impede COVID-19 outbreak for Heilongjiang province. Implementation of stronger interventions to lower mutual contacts could accelerate the complete recovery from coronavirus infections in Heilongjiang province. Collectively, our model has characterized the epidemic of COVID-19 in Heilongjiang province and implied that strongly controlled measured should be taken for infected and asymptomatic patients to minimize total infections. |
format | Online Article Text |
id | pubmed-7256610 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-72566102020-05-29 Modeling COVID-19 epidemic in Heilongjiang province, China Sun, Tingzhe Wang, Yan Chaos Solitons Fractals Article The Coronavirus Disease 2019 (COVID-19) surges worldwide. However, massive imported patients especially into Heilongjiang Province in China recently have been an alert for local COVID-19 outbreak. We collected data from January 23 to March 25 from Heilongjiang province and trained an ordinary differential equation model to fit the epidemic data. We extended the simulation using this trained model to characterize the effect of an imported ‘escaper’. We showed that an imported ‘escaper’ was responsible for the newly confirmed COVID-19 infections from Apr 9 to Apr 19 in Heilongjiang province. Stochastic simulations further showed that significantly increased local contacts among imported ‘escaper’, its epidemiologically associated cases and susceptible populations greatly contributed to the local outbreak of COVID-19. Meanwhile, we further found that the reported number of asymptomatic patients was markedly lower than model predictions implying a large asymptomatic pool which was not identified. We further forecasted the effect of implementing strong interventions immediately to impede COVID-19 outbreak for Heilongjiang province. Implementation of stronger interventions to lower mutual contacts could accelerate the complete recovery from coronavirus infections in Heilongjiang province. Collectively, our model has characterized the epidemic of COVID-19 in Heilongjiang province and implied that strongly controlled measured should be taken for infected and asymptomatic patients to minimize total infections. Elsevier Ltd. 2020-09 2020-05-29 /pmc/articles/PMC7256610/ /pubmed/32834579 http://dx.doi.org/10.1016/j.chaos.2020.109949 Text en © 2020 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 Sun, Tingzhe Wang, Yan Modeling COVID-19 epidemic in Heilongjiang province, China |
title | Modeling COVID-19 epidemic in Heilongjiang province, China |
title_full | Modeling COVID-19 epidemic in Heilongjiang province, China |
title_fullStr | Modeling COVID-19 epidemic in Heilongjiang province, China |
title_full_unstemmed | Modeling COVID-19 epidemic in Heilongjiang province, China |
title_short | Modeling COVID-19 epidemic in Heilongjiang province, China |
title_sort | modeling covid-19 epidemic in heilongjiang province, china |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256610/ https://www.ncbi.nlm.nih.gov/pubmed/32834579 http://dx.doi.org/10.1016/j.chaos.2020.109949 |
work_keys_str_mv | AT suntingzhe modelingcovid19epidemicinheilongjiangprovincechina AT wangyan modelingcovid19epidemicinheilongjiangprovincechina |