Infection Density and Epidemic Size of COVID-19 in China outside the Hubei province.
Identifieur interne : 002465 ( Main/Corpus ); précédent : 002464; suivant : 002466Infection Density and Epidemic Size of COVID-19 in China outside the Hubei province.
Auteurs : Yukun Liu ; Jing Qin ; Yan Fan ; Yong Zhou ; Dean A. Follmann ; Chiung-Yu HuangSource :
- medRxiv : the preprint server for health sciences ; 2020.
Abstract
The novel coronavirus (COVID-19) has spread to almost all countries in the world, claiming more than 160,000 lives and sickening more than 2,400,000 people by April 21, 2020. There has been research showing that on average, each infected person spreads the infection to more than two persons. Therefore the majority of the population is at risk of infection if no intervention measures were undertaken. The true size of the COVID-19 epidemic remains unknown, as a significant proportion of infected individuals only exhibit mild symptoms or are even asymptomatic. A timely assessment of the evolving epidemic size is crucial for resource allocation and triage decisions. In this article, we modify the back-calculation algorithm to obtain a lower bound estimate of the number of COVID-19 infected persons in China outside the Hubei province. We estimate the infection density among infected and show that the drastic control measures enforced throughout China following the lockdown of Wuhan City effectively slowed down the spread of the disease in two weeks. Our findings from China are expected to provide guidelines and enlightenment for surveillance and control activities of COVID-19 in other countries around the world.
DOI: 10.1101/2020.04.23.20074708
PubMed: 32511453
PubMed Central: PMC7239081
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pubmed:32511453Le document en format XML
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<front><div type="abstract" xml:lang="en">The novel coronavirus (COVID-19) has spread to almost all countries in the world, claiming more than 160,000 lives and sickening more than 2,400,000 people by April 21, 2020. There has been research showing that on average, each infected person spreads the infection to more than two persons. Therefore the majority of the population is at risk of infection if no intervention measures were undertaken. The true size of the COVID-19 epidemic remains unknown, as a significant proportion of infected individuals only exhibit mild symptoms or are even asymptomatic. A timely assessment of the evolving epidemic size is crucial for resource allocation and triage decisions. In this article, we modify the back-calculation algorithm to obtain a lower bound estimate of the number of COVID-19 infected persons in China outside the Hubei province. We estimate the infection density among infected and show that the drastic control measures enforced throughout China following the lockdown of Wuhan City effectively slowed down the spread of the disease in two weeks. Our findings from China are expected to provide guidelines and enlightenment for surveillance and control activities of COVID-19 in other countries around the world.</div>
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<ArticleIdList><ArticleId IdType="pubmed">31995857</ArticleId>
</ArticleIdList>
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<Reference><Citation>Int J Infect Dis. 2020 Mar;92:214-217</Citation>
<ArticleIdList><ArticleId IdType="pubmed">32007643</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Sci Adv. 2020 Aug 14;6(33):eabc1202</Citation>
<ArticleIdList><ArticleId IdType="pubmed">32851189</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Lancet. 1986 Dec 6;2(8519):1320-2</Citation>
<ArticleIdList><ArticleId IdType="pubmed">2878184</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Philos Trans R Soc Lond B Biol Sci. 1997 Jul 29;352(1355):803-38</Citation>
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