REAL-TIME DATA ANALYTICS AND PREDICTION OF THE COVID-19 PANDEMIC (PERIOD TO APRIL 2ND, 2020)
Identifieur interne : 000495 ( Hal/Corpus ); précédent : 000494; suivant : 000496REAL-TIME DATA ANALYTICS AND PREDICTION OF THE COVID-19 PANDEMIC (PERIOD TO APRIL 2ND, 2020)
Auteurs : Chehbi Gamoura ChehbiSource :
Abstract
This brief paper is versioned 4 in a series of short papers that describe a set of descriptive and predictive analytics of the pandemic COVID-19 around the world. We exceptionally propose this new and uncommon way of publications because of the current emergency circumstances where Data are gathered and analyzed directly day by day. Because of the new behavior regarding the spread speed and the contagion features of this virus, we opted by comparative analytics based on demographic characteristics in localities and countries for prediction, without using historical data in epidemiology. The test proofs of our findings are done day by day with the real figures reported from the Data. To feed our models in algorithms, we refer to the reported cases from the Data of the World Health Organization (WHO). Because of the current circumstances of emergency, this paper is brief and will be succeeded with a series of versions until the end of the pandemic. The full paper will be published afterward with more details about the functions, the model, and the variables included in our algorithms.
Url:
Links to Exploration step
Hal:hal-02529549Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">REAL-TIME DATA ANALYTICS AND PREDICTION OF THE COVID-19 PANDEMIC (PERIOD TO APRIL 2ND, 2020)</title>
<author><name sortKey="Chehbi, Chehbi Gamoura" sort="Chehbi, Chehbi Gamoura" uniqKey="Chehbi C" first="Chehbi Gamoura" last="Chehbi">Chehbi Gamoura Chehbi</name>
<affiliation><hal:affiliation type="institution" xml:id="struct-325876" status="VALID"> <orgName>Ecole de Management de Strasbourg</orgName>
<orgName type="acronym">EM Strasbourg</orgName>
<date type="start">2007</date>
<desc> <address> <addrLine>EM Strasbourg Business School61 avenue de la Forêt-NoireF-67085 Strasbourg Cedex</addrLine>
<country key="FR"></country>
</address>
<ref type="url">http://www.em-strasbourg.eu/</ref>
</desc>
</hal:affiliation>
</affiliation>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">HAL</idno>
<idno type="RBID">Hal:hal-02529549</idno>
<idno type="halId">hal-02529549</idno>
<idno type="halUri">https://hal.archives-ouvertes.fr/hal-02529549</idno>
<idno type="url">https://hal.archives-ouvertes.fr/hal-02529549</idno>
<date when="2020-04-02">2020-04-02</date>
<idno type="wicri:Area/Hal/Corpus">000495</idno>
</publicationStmt>
<sourceDesc><biblStruct><analytic><title xml:lang="en">REAL-TIME DATA ANALYTICS AND PREDICTION OF THE COVID-19 PANDEMIC (PERIOD TO APRIL 2ND, 2020)</title>
<author><name sortKey="Chehbi, Chehbi Gamoura" sort="Chehbi, Chehbi Gamoura" uniqKey="Chehbi C" first="Chehbi Gamoura" last="Chehbi">Chehbi Gamoura Chehbi</name>
<affiliation><hal:affiliation type="institution" xml:id="struct-325876" status="VALID"> <orgName>Ecole de Management de Strasbourg</orgName>
<orgName type="acronym">EM Strasbourg</orgName>
<date type="start">2007</date>
<desc> <address> <addrLine>EM Strasbourg Business School61 avenue de la Forêt-NoireF-67085 Strasbourg Cedex</addrLine>
<country key="FR"></country>
</address>
<ref type="url">http://www.em-strasbourg.eu/</ref>
</desc>
</hal:affiliation>
</affiliation>
</author>
</analytic>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc><textClass></textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en"> <p>This brief paper is versioned 4 in a series of short papers that describe a set of descriptive and predictive analytics of the pandemic COVID-19 around the world. We exceptionally propose this new and uncommon way of publications because of the current emergency circumstances where Data are gathered and analyzed directly day by day. Because of the new behavior regarding the spread speed and the contagion features of this virus, we opted by comparative analytics based on demographic characteristics in localities and countries for prediction, without using historical data in epidemiology. The test proofs of our findings are done day by day with the real figures reported from the Data. To feed our models in algorithms, we refer to the reported cases from the Data of the World Health Organization (WHO). Because of the current circumstances of emergency, this paper is brief and will be succeeded with a series of versions until the end of the pandemic. The full paper will be published afterward with more details about the functions, the model, and the variables included in our algorithms.</p>
</div>
</front>
</TEI>
<hal api="V3"> <titleStmt> <title xml:lang="en">REAL-TIME DATA ANALYTICS AND PREDICTION OF THE COVID-19 PANDEMIC (PERIOD TO APRIL 2ND, 2020)</title>
<author role="aut"> <persName> <forename type="first">Chehbi Gamoura</forename>
<surname>CHEHBI</surname>
</persName>
<email type="md5">46673ea4f43732543b904372bf4da82f</email>
<email type="domain">gmail.com</email>
<ptr type="url" target="www.samiagamoura.com"></ptr>
<idno type="idhal" notation="string">samia-chehbi-gamoura</idno>
<idno type="idhal" notation="numeric">181023</idno>
<idno type="halauthorid">11703221</idno>
<idno type="ORCID">https://orcid.org/0000-0002-1239-0873</idno>
<idno type="ResearcherId">http://www.researcherid.com/rid/P-1446-2015</idno>
<affiliation ref="#struct-325876"></affiliation>
</author>
<editor role="depositor"> <persName> <forename>Samia</forename>
<surname>Chehbi Gamoura</surname>
</persName>
<email type="md5">46673ea4f43732543b904372bf4da82f</email>
<email type="domain">gmail.com</email>
</editor>
</titleStmt>
<editionStmt> <edition n="v1" type="current"> <date type="whenSubmitted">2020-04-02 13:12:28</date>
<date type="whenModified">2020-04-03 10:07:55</date>
<date type="whenReleased">2020-04-03 10:07:55</date>
<date type="whenProduced">2020-04-02</date>
<date type="whenEndEmbargoed">2020-04-02</date>
<ref type="file" target="https://hal.archives-ouvertes.fr/hal-02529549/document"> <date notBefore="2020-04-02"></date>
</ref>
<ref type="file" subtype="author" n="1" target="https://hal.archives-ouvertes.fr/hal-02529549/file/Paper_Samia_COVID-19_April-02_2020.pdf"> <date notBefore="2020-04-02"></date>
</ref>
</edition>
<respStmt> <resp>contributor</resp>
<name key="825337"> <persName> <forename>Samia</forename>
<surname>Chehbi Gamoura</surname>
</persName>
<email type="md5">46673ea4f43732543b904372bf4da82f</email>
<email type="domain">gmail.com</email>
</name>
</respStmt>
</editionStmt>
<publicationStmt> <distributor>CCSD</distributor>
<idno type="halId">hal-02529549</idno>
<idno type="halUri">https://hal.archives-ouvertes.fr/hal-02529549</idno>
<idno type="halBibtex">chehbi:hal-02529549</idno>
<idno type="halRefHtml">2020</idno>
<idno type="halRef">2020</idno>
</publicationStmt>
<seriesStmt></seriesStmt>
<notesStmt></notesStmt>
<sourceDesc> <biblStruct> <analytic> <title xml:lang="en">REAL-TIME DATA ANALYTICS AND PREDICTION OF THE COVID-19 PANDEMIC (PERIOD TO APRIL 2ND, 2020)</title>
<author role="aut"> <persName> <forename type="first">Chehbi Gamoura</forename>
<surname>CHEHBI</surname>
</persName>
<email type="md5">46673ea4f43732543b904372bf4da82f</email>
<email type="domain">gmail.com</email>
<ptr type="url" target="www.samiagamoura.com"></ptr>
<idno type="idhal" notation="string">samia-chehbi-gamoura</idno>
<idno type="idhal" notation="numeric">181023</idno>
<idno type="halauthorid">11703221</idno>
<idno type="ORCID">https://orcid.org/0000-0002-1239-0873</idno>
<idno type="ResearcherId">http://www.researcherid.com/rid/P-1446-2015</idno>
<affiliation ref="#struct-325876"></affiliation>
</author>
</analytic>
<monogr> <imprint></imprint>
</monogr>
</biblStruct>
</sourceDesc>
<profileDesc> <langUsage> <language ident="en">English</language>
</langUsage>
<textClass> <classCode scheme="halDomain" n="info.info-ai">Computer Science [cs]/Artificial Intelligence [cs.AI]</classCode>
<classCode scheme="halDomain" n="sdv.mhep.me">Life Sciences [q-bio]/Human health and pathology/Emerging diseases</classCode>
<classCode scheme="halTypology" n="UNDEFINED">Preprints, Working Papers, ...</classCode>
</textClass>
<abstract xml:lang="en"> <p>This brief paper is versioned 4 in a series of short papers that describe a set of descriptive and predictive analytics of the pandemic COVID-19 around the world. We exceptionally propose this new and uncommon way of publications because of the current emergency circumstances where Data are gathered and analyzed directly day by day. Because of the new behavior regarding the spread speed and the contagion features of this virus, we opted by comparative analytics based on demographic characteristics in localities and countries for prediction, without using historical data in epidemiology. The test proofs of our findings are done day by day with the real figures reported from the Data. To feed our models in algorithms, we refer to the reported cases from the Data of the World Health Organization (WHO). Because of the current circumstances of emergency, this paper is brief and will be succeeded with a series of versions until the end of the pandemic. The full paper will be published afterward with more details about the functions, the model, and the variables included in our algorithms.</p>
</abstract>
</profileDesc>
</hal>
</record>
Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Sante/explor/PandemieGrippaleV1/Data/Hal/Corpus
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000495 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Hal/Corpus/biblio.hfd -nk 000495 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien |wiki= Sante |area= PandemieGrippaleV1 |flux= Hal |étape= Corpus |type= RBID |clé= Hal:hal-02529549 |texte= REAL-TIME DATA ANALYTICS AND PREDICTION OF THE COVID-19 PANDEMIC (PERIOD TO APRIL 2ND, 2020) }}
![]() | This area was generated with Dilib version V0.6.34. | ![]() |