Lessons learned from the 2019-nCoV epidemic on prevention of future infectious diseases.
Identifieur interne : 000569 ( PubMed/Checkpoint ); précédent : 000568; suivant : 000570Lessons learned from the 2019-nCoV epidemic on prevention of future infectious diseases.
Auteurs : Xingchen Pan [République populaire de Chine] ; David M. Ojcius [États-Unis] ; Tianyue Gao [Canada] ; Zhongsheng Li [République populaire de Chine] ; Chunhua Pan [République populaire de Chine] ; Chungen Pan [République populaire de Chine]Source :
- Microbes and infection [ 1769-714X ] ; 2020.
Descripteurs français
- KwdFr :
- MESH :
- virologie : Chiroptera, Zoonoses.
- épidémiologie : Infections à coronavirus, Pneumopathie virale.
- Animaux, Chine, Communication, Gouvernement, Humains, Infections à coronavirus, Pneumopathie virale, Santé publique, Voyage, Épidémies.
- Wicri :
- geographic : République populaire de Chine.
English descriptors
- KwdEn :
- Animals, Betacoronavirus (pathogenicity), China, Chiroptera (virology), Communication, Coronavirus Infections (epidemiology), Coronavirus Infections (prevention & control), Epidemics (prevention & control), Government, Humans, Pneumonia, Viral (epidemiology), Pneumonia, Viral (prevention & control), Public Health (methods), Travel, Zoonoses (virology).
- MESH :
- geographic : China.
- epidemiology : Coronavirus Infections, Pneumonia, Viral.
- methods : Public Health.
- pathogenicity : Betacoronavirus.
- prevention & control : Coronavirus Infections, Epidemics, Pneumonia, Viral.
- virology : Chiroptera, Zoonoses.
- Animals, Communication, Government, Humans, Travel.
Abstract
Only a month after the outbreak of pneumonia caused by 2019-nCoV, more than forty-thousand people were infected. This put enormous pressure on the Chinese government, medical healthcare provider, and the general public, but also made the international community deeply nervous. On the 25th day after the outbreak, the Chinese government implemented strict traffic restrictions on the area where the 2019-nCoV had originated-Hubei province, whose capital city is Wuhan. Ten days later, the rate of increase of cases in Hubei showed a significant difference (p = 0.0001) compared with the total rate of increase in other provinces of China. These preliminary data suggest the effectiveness of a traffic restriction policy for this pandemic thus far. At the same time, solid financial support and improved research ability, along with network communication technology, also greatly facilitated the application of epidemic prevention measures. These measures were motivated by the need to provide effective treatment of patients, and involved consultation with three major groups in policy formulation-public health experts, the government, and the general public. It was also aided by media and information technology, as well as international cooperation. This experience will provide China and other countries with valuable lessons for quickly coordinating and coping with future public health emergencies.
DOI: 10.1016/j.micinf.2020.02.004
PubMed: 32088333
Affiliations:
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pubmed:32088333Le document en format XML
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<front><div type="abstract" xml:lang="en">Only a month after the outbreak of pneumonia caused by 2019-nCoV, more than forty-thousand people were infected. This put enormous pressure on the Chinese government, medical healthcare provider, and the general public, but also made the international community deeply nervous. On the 25th day after the outbreak, the Chinese government implemented strict traffic restrictions on the area where the 2019-nCoV had originated-Hubei province, whose capital city is Wuhan. Ten days later, the rate of increase of cases in Hubei showed a significant difference (p = 0.0001) compared with the total rate of increase in other provinces of China. These preliminary data suggest the effectiveness of a traffic restriction policy for this pandemic thus far. At the same time, solid financial support and improved research ability, along with network communication technology, also greatly facilitated the application of epidemic prevention measures. These measures were motivated by the need to provide effective treatment of patients, and involved consultation with three major groups in policy formulation-public health experts, the government, and the general public. It was also aided by media and information technology, as well as international cooperation. This experience will provide China and other countries with valuable lessons for quickly coordinating and coping with future public health emergencies.</div>
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<Abstract><AbstractText>Only a month after the outbreak of pneumonia caused by 2019-nCoV, more than forty-thousand people were infected. This put enormous pressure on the Chinese government, medical healthcare provider, and the general public, but also made the international community deeply nervous. On the 25th day after the outbreak, the Chinese government implemented strict traffic restrictions on the area where the 2019-nCoV had originated-Hubei province, whose capital city is Wuhan. Ten days later, the rate of increase of cases in Hubei showed a significant difference (p = 0.0001) compared with the total rate of increase in other provinces of China. These preliminary data suggest the effectiveness of a traffic restriction policy for this pandemic thus far. At the same time, solid financial support and improved research ability, along with network communication technology, also greatly facilitated the application of epidemic prevention measures. These measures were motivated by the need to provide effective treatment of patients, and involved consultation with three major groups in policy formulation-public health experts, the government, and the general public. It was also aided by media and information technology, as well as international cooperation. This experience will provide China and other countries with valuable lessons for quickly coordinating and coping with future public health emergencies.</AbstractText>
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<tree><country name="République populaire de Chine"><noRegion><name sortKey="Pan, Xingchen" sort="Pan, Xingchen" uniqKey="Pan X" first="Xingchen" last="Pan">Xingchen Pan</name>
</noRegion>
<name sortKey="Li, Zhongsheng" sort="Li, Zhongsheng" uniqKey="Li Z" first="Zhongsheng" last="Li">Zhongsheng Li</name>
<name sortKey="Pan, Chungen" sort="Pan, Chungen" uniqKey="Pan C" first="Chungen" last="Pan">Chungen Pan</name>
<name sortKey="Pan, Chunhua" sort="Pan, Chunhua" uniqKey="Pan C" first="Chunhua" last="Pan">Chunhua Pan</name>
</country>
<country name="États-Unis"><region name="Californie"><name sortKey="Ojcius, David M" sort="Ojcius, David M" uniqKey="Ojcius D" first="David M" last="Ojcius">David M. Ojcius</name>
</region>
</country>
<country name="Canada"><noRegion><name sortKey="Gao, Tianyue" sort="Gao, Tianyue" uniqKey="Gao T" first="Tianyue" last="Gao">Tianyue Gao</name>
</noRegion>
</country>
</tree>
</affiliations>
</record>
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