Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries.
Identifieur interne : 000F82 ( Main/Exploration ); précédent : 000F81; suivant : 000F83Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries.
Auteurs : Rohit Salgotra [Inde] ; Mostafa Gandomi [Iran] ; Amir H. Gandomi [Australie]Source :
- Chaos, solitons, and fractals [ 0960-0779 ] ; 2020.
Abstract
COVID-19 or SARS-Cov-2, affecting 6 million people and more than 300,000 deaths, the global pandemic has engulfed more than 90% countries of the world. The virus started from a single organism and is escalating at a rate of 3% to 5% daily and seems to be a never ending process. Understanding the basic dynamics and presenting new predictions models for evaluating the potential effect of the virus is highly crucial. In present work, an evolutionary data analytics method called as Genetic programming (GP) is used to mathematically model the potential effect of coronavirus in 15 most affected countries of the world. Two datasets namely confirmed cases (CC) and death cases (DC) were taken into consideration to estimate, how transmission varied in these countries between January 2020 and May 2020. Further, a percentage rise in the number of daily cases is also shown till 8 June 2020 and it is expected that Brazil will have the maximum rise in CC and USA have the most DC. Also, prediction of number of new CC and DC cases for every one million people in each of these countries is presented. The proposed model predicted that the transmission of COVID-19 in China is declining since late March 2020; in Singapore, France, Italy, Germany and Spain the curve has stagnated; in case of Canada, South Africa, Iran and Turkey the number of cases are rising slowly; whereas for USA, UK, Brazil, Russia and Mexico the rate of increase is very high and control measures need to be taken to stop the chains of transmission. Apart from that, the proposed prediction models are simple mathematical equations and future predictions can be drawn from these general equations. From the experimental results and statistical validation, it can be said that the proposed models use simple linkage functions and provide highly reliable results for time series prediction of COVID-19 in these countries.
DOI: 10.1016/j.chaos.2020.110118
PubMed: 32834632
PubMed Central: PMC7367045
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries.</title><author><name sortKey="Salgotra, Rohit" sort="Salgotra, Rohit" uniqKey="Salgotra R" first="Rohit" last="Salgotra">Rohit Salgotra</name><affiliation wicri:level="1"><nlm:affiliation>Dept. of ECE, Thapar Institute of Engineering & Technology, Patiala, India.</nlm:affiliation><country xml:lang="fr">Inde</country><wicri:regionArea>Dept. of ECE, Thapar Institute of Engineering & Technology, Patiala</wicri:regionArea><wicri:noRegion>Patiala</wicri:noRegion></affiliation></author><author><name sortKey="Gandomi, Mostafa" sort="Gandomi, Mostafa" uniqKey="Gandomi M" first="Mostafa" last="Gandomi">Mostafa Gandomi</name><affiliation wicri:level="1"><nlm:affiliation>School of Civil Engineering, University of Tehran, Tehran, Iran.</nlm:affiliation><country xml:lang="fr">Iran</country><wicri:regionArea>School of Civil Engineering, University of Tehran, Tehran</wicri:regionArea><wicri:noRegion>Tehran</wicri:noRegion></affiliation></author><author><name sortKey="Gandomi, Amir H" sort="Gandomi, Amir H" uniqKey="Gandomi A" first="Amir H" last="Gandomi">Amir H. Gandomi</name><affiliation wicri:level="1"><nlm:affiliation>Faculty of Engineering & Information Technology, University of Technology Sydney, NSW 2007, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>Faculty of Engineering & Information Technology, University of Technology Sydney, NSW 2007</wicri:regionArea><wicri:noRegion>NSW 2007</wicri:noRegion></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:32834632</idno><idno type="pmid">32834632</idno><idno type="doi">10.1016/j.chaos.2020.110118</idno><idno type="pmc">PMC7367045</idno><idno type="wicri:Area/Main/Corpus">000502</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">000502</idno><idno type="wicri:Area/Main/Curation">000502</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Curation">000502</idno><idno type="wicri:Area/Main/Exploration">000502</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries.</title><author><name sortKey="Salgotra, Rohit" sort="Salgotra, Rohit" uniqKey="Salgotra R" first="Rohit" last="Salgotra">Rohit Salgotra</name><affiliation wicri:level="1"><nlm:affiliation>Dept. of ECE, Thapar Institute of Engineering & Technology, Patiala, India.</nlm:affiliation><country xml:lang="fr">Inde</country><wicri:regionArea>Dept. of ECE, Thapar Institute of Engineering & Technology, Patiala</wicri:regionArea><wicri:noRegion>Patiala</wicri:noRegion></affiliation></author><author><name sortKey="Gandomi, Mostafa" sort="Gandomi, Mostafa" uniqKey="Gandomi M" first="Mostafa" last="Gandomi">Mostafa Gandomi</name><affiliation wicri:level="1"><nlm:affiliation>School of Civil Engineering, University of Tehran, Tehran, Iran.</nlm:affiliation><country xml:lang="fr">Iran</country><wicri:regionArea>School of Civil Engineering, University of Tehran, Tehran</wicri:regionArea><wicri:noRegion>Tehran</wicri:noRegion></affiliation></author><author><name sortKey="Gandomi, Amir H" sort="Gandomi, Amir H" uniqKey="Gandomi A" first="Amir H" last="Gandomi">Amir H. Gandomi</name><affiliation wicri:level="1"><nlm:affiliation>Faculty of Engineering & Information Technology, University of Technology Sydney, NSW 2007, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>Faculty of Engineering & Information Technology, University of Technology Sydney, NSW 2007</wicri:regionArea><wicri:noRegion>NSW 2007</wicri:noRegion></affiliation></author></analytic><series><title level="j">Chaos, solitons, and fractals</title><idno type="ISSN">0960-0779</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">COVID-19 or SARS-Cov-2, affecting 6 million people and more than 300,000 deaths, the global pandemic has engulfed more than 90% countries of the world. The virus started from a single organism and is escalating at a rate of 3% to 5% daily and seems to be a never ending process. Understanding the basic dynamics and presenting new predictions models for evaluating the potential effect of the virus is highly crucial. In present work, an evolutionary data analytics method called as Genetic programming (GP) is used to mathematically model the potential effect of coronavirus in 15 most affected countries of the world. Two datasets namely confirmed cases (CC) and death cases (DC) were taken into consideration to estimate, how transmission varied in these countries between January 2020 and May 2020. Further, a percentage rise in the number of daily cases is also shown till 8 June 2020 and it is expected that Brazil will have the maximum rise in CC and USA have the most DC. Also, prediction of number of new CC and DC cases for every one million people in each of these countries is presented. The proposed model predicted that the transmission of COVID-19 in China is declining since late March 2020; in Singapore, France, Italy, Germany and Spain the curve has stagnated; in case of Canada, South Africa, Iran and Turkey the number of cases are rising slowly; whereas for USA, UK, Brazil, Russia and Mexico the rate of increase is very high and control measures need to be taken to stop the chains of transmission. Apart from that, the proposed prediction models are simple mathematical equations and future predictions can be drawn from these general equations. From the experimental results and statistical validation, it can be said that the proposed models use simple linkage functions and provide highly reliable results for time series prediction of COVID-19 in these countries.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">32834632</PMID><DateRevised><Year>2020</Year><Month>08</Month><Day>24</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">0960-0779</ISSN><JournalIssue CitedMedium="Print"><Volume>140</Volume><PubDate><Year>2020</Year><Month>Nov</Month></PubDate></JournalIssue><Title>Chaos, solitons, and fractals</Title><ISOAbbreviation>Chaos Solitons Fractals</ISOAbbreviation></Journal><ArticleTitle>Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries.</ArticleTitle><Pagination><MedlinePgn>110118</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.chaos.2020.110118</ELocationID><Abstract><AbstractText>COVID-19 or SARS-Cov-2, affecting 6 million people and more than 300,000 deaths, the global pandemic has engulfed more than 90% countries of the world. The virus started from a single organism and is escalating at a rate of 3% to 5% daily and seems to be a never ending process. Understanding the basic dynamics and presenting new predictions models for evaluating the potential effect of the virus is highly crucial. In present work, an evolutionary data analytics method called as Genetic programming (GP) is used to mathematically model the potential effect of coronavirus in 15 most affected countries of the world. Two datasets namely confirmed cases (CC) and death cases (DC) were taken into consideration to estimate, how transmission varied in these countries between January 2020 and May 2020. Further, a percentage rise in the number of daily cases is also shown till 8 June 2020 and it is expected that Brazil will have the maximum rise in CC and USA have the most DC. Also, prediction of number of new CC and DC cases for every one million people in each of these countries is presented. The proposed model predicted that the transmission of COVID-19 in China is declining since late March 2020; in Singapore, France, Italy, Germany and Spain the curve has stagnated; in case of Canada, South Africa, Iran and Turkey the number of cases are rising slowly; whereas for USA, UK, Brazil, Russia and Mexico the rate of increase is very high and control measures need to be taken to stop the chains of transmission. Apart from that, the proposed prediction models are simple mathematical equations and future predictions can be drawn from these general equations. From the experimental results and statistical validation, it can be said that the proposed models use simple linkage functions and provide highly reliable results for time series prediction of COVID-19 in these countries.</AbstractText><CopyrightInformation>© 2020 Published by Elsevier Ltd.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Salgotra</LastName><ForeName>Rohit</ForeName><Initials>R</Initials><AffiliationInfo><Affiliation>Dept. of ECE, Thapar Institute of Engineering & Technology, Patiala, India.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Gandomi</LastName><ForeName>Mostafa</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>School of Civil Engineering, University of Tehran, Tehran, Iran.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Gandomi</LastName><ForeName>Amir H</ForeName><Initials>AH</Initials><AffiliationInfo><Affiliation>Faculty of Engineering & Information Technology, University of Technology Sydney, NSW 2007, Australia.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>07</Month><Day>17</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Chaos Solitons Fractals</MedlineTA><NlmUniqueID>100971564</NlmUniqueID><ISSNLinking>0960-0779</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">COVID-19</Keyword><Keyword MajorTopicYN="N">Coronavirus</Keyword><Keyword MajorTopicYN="N">Countries of the world</Keyword><Keyword MajorTopicYN="N">Gene expression programming (GEP)</Keyword><Keyword MajorTopicYN="N">SARS-CoV-2</Keyword><Keyword MajorTopicYN="N">Time series forecasting</Keyword></KeywordList><CoiStatement>The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>06</Month><Day>20</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>07</Month><Day>10</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>8</Month><Day>25</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>8</Month><Day>25</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>8</Month><Day>25</Day><Hour>6</Hour><Minute>1</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32834632</ArticleId><ArticleId IdType="doi">10.1016/j.chaos.2020.110118</ArticleId><ArticleId IdType="pii">S0960-0779(20)30515-4</ArticleId><ArticleId IdType="pii">110118</ArticleId><ArticleId IdType="pmc">PMC7367045</ArticleId></ArticleIdList><pmc-dir>pmcsd</pmc-dir>
<ReferenceList><Reference><Citation>Euro Surveill. 2020 Feb;25(5):</Citation><ArticleIdList><ArticleId IdType="pubmed">32046819</ArticleId></ArticleIdList></Reference><Reference><Citation>Chaos Solitons Fractals. 2020 Sep;138:109945</Citation><ArticleIdList><ArticleId IdType="pubmed">32508399</ArticleId></ArticleIdList></Reference><Reference><Citation>Nature. 2004 May 13;429(6988):180-4</Citation><ArticleIdList><ArticleId IdType="pubmed">15141212</ArticleId></ArticleIdList></Reference><Reference><Citation>Euro Surveill. 2020 Jan;25(4):</Citation><ArticleIdList><ArticleId IdType="pubmed">32019669</ArticleId></ArticleIdList></Reference><Reference><Citation>J Clin Med. 2020 Feb 17;9(2):</Citation><ArticleIdList><ArticleId IdType="pubmed">32079150</ArticleId></ArticleIdList></Reference><Reference><Citation>J Clin Med. 2020 Feb 19;9(2):</Citation><ArticleIdList><ArticleId IdType="pubmed">32093043</ArticleId></ArticleIdList></Reference><Reference><Citation>Lancet. 2020 Feb 15;395(10223):497-506</Citation><ArticleIdList><ArticleId IdType="pubmed">31986264</ArticleId></ArticleIdList></Reference><Reference><Citation>J Mol Graph Model. 2002 Jan;20(4):269-76</Citation><ArticleIdList><ArticleId IdType="pubmed">11858635</ArticleId></ArticleIdList></Reference><Reference><Citation>Sci Data. 2019 Aug 16;6(1):150</Citation><ArticleIdList><ArticleId IdType="pubmed">31420560</ArticleId></ArticleIdList></Reference><Reference><Citation>Chaos Solitons Fractals. 2020 May;134:109761</Citation><ArticleIdList><ArticleId IdType="pubmed">32308258</ArticleId></ArticleIdList></Reference><Reference><Citation>Sci Total Environ. 2020 Aug 1;728:138778</Citation><ArticleIdList><ArticleId IdType="pubmed">32335405</ArticleId></ArticleIdList></Reference><Reference><Citation>Chaos Solitons Fractals. 2020 Jul;136:109889</Citation><ArticleIdList><ArticleId IdType="pubmed">32406395</ArticleId></ArticleIdList></Reference><Reference><Citation>Rand Health Q. 2017 Jun 19;6(3):5</Citation><ArticleIdList><ArticleId IdType="pubmed">28845357</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>Australie</li><li>Inde</li><li>Iran</li></country></list><tree><country name="Inde"><noRegion><name sortKey="Salgotra, Rohit" sort="Salgotra, Rohit" uniqKey="Salgotra R" first="Rohit" last="Salgotra">Rohit Salgotra</name></noRegion></country><country name="Iran"><noRegion><name sortKey="Gandomi, Mostafa" sort="Gandomi, Mostafa" uniqKey="Gandomi M" first="Mostafa" last="Gandomi">Mostafa Gandomi</name></noRegion></country><country name="Australie"><noRegion><name sortKey="Gandomi, Amir H" sort="Gandomi, Amir H" uniqKey="Gandomi A" first="Amir H" last="Gandomi">Amir H. Gandomi</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Sante/explor/CovidFranceV1/Data/Main/Exploration
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000F82 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 000F82 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Sante
|area= CovidFranceV1
|flux= Main
|étape= Exploration
|type= RBID
|clé= pubmed:32834632
|texte= Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries.
}}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/Main/Exploration/RBID.i -Sk "pubmed:32834632" \
| HfdSelect -Kh $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd \
| NlmPubMed2Wicri -a CovidFranceV1
| This area was generated with Dilib version V0.6.37. |  |