Mathematical models and deep learning for predicting the number of individuals reported to be infected with SARS-CoV-2.
Identifieur interne : 000B29 ( Main/Exploration ); précédent : 000B28; suivant : 000B30Mathematical models and deep learning for predicting the number of individuals reported to be infected with SARS-CoV-2.
Auteurs : A S Fokas [Royaume-Uni, Grèce, États-Unis] ; N. Dikaios [Grèce, Royaume-Uni] ; G A Kastis [Grèce]Source :
- Journal of the Royal Society, Interface [ 1742-5662 ] ; 2020.
Descripteurs français
- KwdFr :
- Apprentissage profond (MeSH), Betacoronavirus (MeSH), Europe (épidémiologie), Humains (MeSH), Infections à coronavirus (prévention et contrôle), Infections à coronavirus (transmission), Infections à coronavirus (épidémiologie), Modèles biologiques (MeSH), Pandémies (prévention et contrôle), Pneumopathie virale (prévention et contrôle), Pneumopathie virale (transmission), Pneumopathie virale (épidémiologie).
- MESH :
- prévention et contrôle : Infections à coronavirus, Pandémies, Pneumopathie virale.
- épidémiologie : Europe, Infections à coronavirus, Pneumopathie virale.
- Apprentissage profond, Betacoronavirus, Humains, Modèles biologiques.
English descriptors
- KwdEn :
- Betacoronavirus (MeSH), Coronavirus Infections (epidemiology), Coronavirus Infections (prevention & control), Coronavirus Infections (transmission), Deep Learning (MeSH), Europe (epidemiology), Humans (MeSH), Models, Biological (MeSH), Pandemics (prevention & control), Pneumonia, Viral (epidemiology), Pneumonia, Viral (prevention & control), Pneumonia, Viral (transmission).
- MESH :
- geographic , epidemiology : Europe.
- epidemiology : Coronavirus Infections, Pneumonia, Viral.
- prevention & control : Coronavirus Infections, Pandemics, Pneumonia, Viral.
- transmission : Coronavirus Infections, Pneumonia, Viral.
- Betacoronavirus, Deep Learning, Humans, Models, Biological.
Abstract
We introduce a novel methodology for predicting the time evolution of the number of individuals in a given country reported to be infected with SARS-CoV-2. This methodology, which is based on the synergy of explicit mathematical formulae and deep learning networks, yields algorithms whose input is only the existing data in the given country of the accumulative number of individuals who are reported to be infected. The analytical formulae involve several constant parameters that were determined from the available data using an error-minimizing algorithm. The same data were also used for the training of a bidirectional long short-term memory network. We applied the above methodology to the epidemics in Italy, Spain, France, Germany, USA and Sweden. The significance of these results for evaluating the impact of easing the lockdown measures is discussed.
DOI: 10.1098/rsif.2020.0494
PubMed: 32752997
PubMed Central: PMC7482569
Affiliations:
- Grèce, Royaume-Uni, États-Unis
- Angleterre, Angleterre de l'Est, Attique (région), Californie
- Athènes, Cambridge, Los Angeles
- Université de Californie du Sud, Université de Cambridge
Links toward previous steps (curation, corpus...)
Le document en format XML
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This methodology, which is based on the synergy of explicit mathematical formulae and deep learning networks, yields algorithms whose input is only the existing data in the given country of the accumulative number of individuals who are reported to be infected. The analytical formulae involve several constant parameters that were determined from the available data using an error-minimizing algorithm. The same data were also used for the training of a bidirectional long short-term memory network. We applied the above methodology to the epidemics in Italy, Spain, France, Germany, USA and Sweden. The significance of these results for evaluating the impact of easing the lockdown measures is discussed.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">32752997</PMID><DateCompleted><Year>2020</Year><Month>08</Month><Day>17</Day></DateCompleted><DateRevised><Year>2020</Year><Month>09</Month><Day>19</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1742-5662</ISSN><JournalIssue CitedMedium="Internet"><Volume>17</Volume><Issue>169</Issue><PubDate><Year>2020</Year><Month>08</Month></PubDate></JournalIssue><Title>Journal of the Royal Society, Interface</Title><ISOAbbreviation>J R Soc Interface</ISOAbbreviation></Journal><ArticleTitle>Mathematical models and deep learning for predicting the number of individuals reported to be infected with SARS-CoV-2.</ArticleTitle><Pagination><MedlinePgn>20200494</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1098/rsif.2020.0494</ELocationID><Abstract><AbstractText>We introduce a novel methodology for predicting the time evolution of the number of individuals in a given country reported to be infected with SARS-CoV-2. This methodology, which is based on the synergy of explicit mathematical formulae and deep learning networks, yields algorithms whose input is only the existing data in the given country of the accumulative number of individuals who are reported to be infected. The analytical formulae involve several constant parameters that were determined from the available data using an error-minimizing algorithm. The same data were also used for the training of a bidirectional long short-term memory network. We applied the above methodology to the epidemics in Italy, Spain, France, Germany, USA and Sweden. 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Dikaios</name></country><country name="Grèce"><region name="Attique (région)"><name sortKey="Fokas, A S" sort="Fokas, A S" uniqKey="Fokas A" first="A S" last="Fokas">A S Fokas</name></region><name sortKey="Dikaios, N" sort="Dikaios, N" uniqKey="Dikaios N" first="N" last="Dikaios">N. Dikaios</name><name sortKey="Kastis, G A" sort="Kastis, G A" uniqKey="Kastis G" first="G A" last="Kastis">G A Kastis</name></country><country name="États-Unis"><region name="Californie"><name sortKey="Fokas, A S" sort="Fokas, A S" uniqKey="Fokas A" first="A S" last="Fokas">A S Fokas</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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