PandemieGrippaleV1, 2020, Analysis, bibRecord, 000148

Chaos theory applied to the outbreak of COVID-19: an ancillary approach to decision making in pandemic context

Identifieur interne : 000148 ( 2020/Analysis ); précédent : 000147; suivant : 000149

Chaos theory applied to the outbreak of COVID-19: an ancillary approach to decision making in pandemic context

Auteurs : M. Peyre [France] ; Y. Zhang [France] ; M. Huc [France] ; F. Roger [France] ; Y. Kerr [France]

Source :

Epidemiology and Infection [ 0950-2688 ] ; 2020.

RBID : Hal:hal-02733183

English descriptors

mix :
- Coronavirus, epidemics, infectious disease control, mathematical modelling, pandemic.

Abstract

While predicting the course of an epidemic is difficult, predicting the course of a pandemic from an emerging virus is even more so. The validity of most predictive models relies on numerous parameters, involving biological and social characteristics often unknown or highly uncertain. Data of the COVID-19 epidemics in China, Japan, South Korea and Italy were used to build up deterministic models without strong assumptions. These models were then applied to other countries to identify the closest scenarios in order to foresee their coming behaviour. The models enabled to predict situations that were confirmed little by little, proving that these tools can be efficient and useful for decision making in a quickly evolving operational context..

Url:

https://hal.inrae.fr/hal-02733183

DOI: 10.1017/S0950268820000990

Affiliations:

France

Links toward previous steps (curation, corpus...)

to stream Hal, to step Corpus: 000094
to stream Hal, to step Curation: 000094
to stream Hal, to step Checkpoint: 000152
to stream Main, to step Merge: 000147
to stream Main, to step Curation: 000148
to stream Main, to step Exploration: 000148
to stream 2020, to step Extraction: 000148

Links to Exploration step

Hal:hal-02733183

Le document en format XML

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<idno type="DOI">10.1017/S0950268820000990</idno>
<series><title level="j">Epidemiology and Infection</title>
<idno type="ISSN">0950-2688</idno>
<imprint><date type="datePub">2020</date>
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<profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>Coronavirus</term>
<term>epidemics</term>
<term>infectious disease control</term>
<term>mathematical modelling</term>
<term>pandemic</term>
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<front><div type="abstract" xml:lang="en">              <p>While predicting the course of an epidemic is difficult, predicting the course of a pandemic from an emerging virus is even more so. The validity of most predictive models relies on numerous parameters, involving biological and social characteristics often unknown or highly uncertain. Data of the COVID-19 epidemics in China, Japan, South Korea and Italy were used to build up deterministic models without strong assumptions. These models were then applied to other countries to identify the closest scenarios in order to foresee their coming behaviour. The models enabled to predict situations that were confirmed little by little, proving that these tools can be efficient and useful for decision making in a quickly evolving operational context..</p>
            </div>
</front>
</TEI>
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<name sortKey="Roger, F" sort="Roger, F" uniqKey="Roger F" first="F." last="Roger">F. Roger</name>
<name sortKey="Zhang, Y" sort="Zhang, Y" uniqKey="Zhang Y" first="Y." last="Zhang">Y. Zhang</name>
</country>
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</record>

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   |texte=   Chaos theory applied to the outbreak of COVID-19: an ancillary approach to decision making in pandemic context
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Chaos theory applied to the outbreak of COVID-19: an ancillary approach to decision making in pandemic context

Chaos theory applied to the outbreak of COVID-19: an ancillary approach to decision making in pandemic context

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