A realistic two-strain model for MERS-CoV infection uncovers the high risk for epidemic propagation
Identifieur interne : 000211 ( Pmc/Checkpoint ); précédent : 000210; suivant : 000212A realistic two-strain model for MERS-CoV infection uncovers the high risk for epidemic propagation
Auteurs : Tridip Sardar [Inde] ; Indrajit Ghosh [Inde] ; Xavier Rod [Espagne] ; Joydev Chattopadhyay [Inde]Source :
- PLoS Neglected Tropical Diseases [ 1935-2727 ] ; 2020.
Abstract
Middle East Respiratory Syndrome Coronavirus (MERS-CoV) causes severe acute respiratory illness with a case fatality rate (CFR) of 35,5%. The highest number of MERS-CoV cases are from Saudi-Arabia, the major worldwide hotspot for this disease. In the absence of neither effective treatment nor a ready-to-use vaccine and with yet an incomplete understanding of its epidemiological cycle, prevention and containment measures can be derived from mathematical models of disease epidemiology. We constructed 2-strain models to predict past outbreaks in the interval 2012–2016 and derive key epidemiological information for Macca, Madina and Riyadh. We approached variability in infection through three different disease incidence functions capturing social behavior in response to an epidemic (e.g. Bilinear, BL; Non-monotone, NM; and Saturated, SAT models). The best model combination successfully anticipated the total number of MERS-CoV clinical cases for the 2015–2016 season and accurately predicted both the number of cases at the peak of seasonal incidence and the overall shape of the epidemic cycle. The evolution in the basic reproduction number (
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DOI: 10.1371/journal.pntd.0008065
PubMed: 32059047
PubMed Central: 7046297
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">A realistic two-strain model for MERS-CoV infection uncovers the high risk for epidemic propagation</title><author><name sortKey="Sardar, Tridip" sort="Sardar, Tridip" uniqKey="Sardar T" first="Tridip" last="Sardar">Tridip Sardar</name><affiliation wicri:level="1"><nlm:aff id="aff001"><addr-line>Department of Mathematics, Dinabandhu Andrews College, Kolkata, India</addr-line></nlm:aff><country xml:lang="fr">Inde</country><wicri:regionArea>Department of Mathematics, Dinabandhu Andrews College, Kolkata</wicri:regionArea><wicri:noRegion>Kolkata</wicri:noRegion></affiliation></author><author><name sortKey="Ghosh, Indrajit" sort="Ghosh, Indrajit" uniqKey="Ghosh I" first="Indrajit" last="Ghosh">Indrajit Ghosh</name><affiliation wicri:level="1"><nlm:aff id="aff002"><addr-line>Agricultural and Ecological Research Unit, Indian Statistical Institute, Kolkata, India</addr-line></nlm:aff><country xml:lang="fr">Inde</country><wicri:regionArea>Agricultural and Ecological Research Unit, Indian Statistical Institute, Kolkata</wicri:regionArea><wicri:noRegion>Kolkata</wicri:noRegion></affiliation></author><author><name sortKey="Rod, Xavier" sort="Rod, Xavier" uniqKey="Rod X" first="Xavier" last="Rod">Xavier Rod</name><affiliation wicri:level="3"><nlm:aff id="aff003"><addr-line>ICREA &CLIMA (Climate and Health Program), ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain</addr-line></nlm:aff><country xml:lang="fr">Espagne</country><wicri:regionArea>ICREA &CLIMA (Climate and Health Program), ISGlobal, Barcelona Institute for Global Health, Barcelona</wicri:regionArea><placeName><settlement type="city">Barcelone</settlement><region nuts="2" type="region">Catalogne</region></placeName></affiliation></author><author><name sortKey="Chattopadhyay, Joydev" sort="Chattopadhyay, Joydev" uniqKey="Chattopadhyay J" first="Joydev" last="Chattopadhyay">Joydev Chattopadhyay</name><affiliation wicri:level="1"><nlm:aff id="aff002"><addr-line>Agricultural and Ecological Research Unit, Indian Statistical Institute, Kolkata, India</addr-line></nlm:aff><country xml:lang="fr">Inde</country><wicri:regionArea>Agricultural and Ecological Research Unit, Indian Statistical Institute, Kolkata</wicri:regionArea><wicri:noRegion>Kolkata</wicri:noRegion></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">32059047</idno><idno type="pmc">7046297</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7046297</idno><idno type="RBID">PMC:7046297</idno><idno type="doi">10.1371/journal.pntd.0008065</idno><date when="2020">2020</date><idno type="wicri:Area/Pmc/Corpus">000978</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000978</idno><idno type="wicri:Area/Pmc/Curation">000978</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000978</idno><idno type="wicri:Area/Pmc/Checkpoint">000211</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">000211</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">A realistic two-strain model for MERS-CoV infection uncovers the high risk for epidemic propagation</title><author><name sortKey="Sardar, Tridip" sort="Sardar, Tridip" uniqKey="Sardar T" first="Tridip" last="Sardar">Tridip Sardar</name><affiliation wicri:level="1"><nlm:aff id="aff001"><addr-line>Department of Mathematics, Dinabandhu Andrews College, Kolkata, India</addr-line></nlm:aff><country xml:lang="fr">Inde</country><wicri:regionArea>Department of Mathematics, Dinabandhu Andrews College, Kolkata</wicri:regionArea><wicri:noRegion>Kolkata</wicri:noRegion></affiliation></author><author><name sortKey="Ghosh, Indrajit" sort="Ghosh, Indrajit" uniqKey="Ghosh I" first="Indrajit" last="Ghosh">Indrajit Ghosh</name><affiliation wicri:level="1"><nlm:aff id="aff002"><addr-line>Agricultural and Ecological Research Unit, Indian Statistical Institute, Kolkata, India</addr-line></nlm:aff><country xml:lang="fr">Inde</country><wicri:regionArea>Agricultural and Ecological Research Unit, Indian Statistical Institute, Kolkata</wicri:regionArea><wicri:noRegion>Kolkata</wicri:noRegion></affiliation></author><author><name sortKey="Rod, Xavier" sort="Rod, Xavier" uniqKey="Rod X" first="Xavier" last="Rod">Xavier Rod</name><affiliation wicri:level="3"><nlm:aff id="aff003"><addr-line>ICREA &CLIMA (Climate and Health Program), ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain</addr-line></nlm:aff><country xml:lang="fr">Espagne</country><wicri:regionArea>ICREA &CLIMA (Climate and Health Program), ISGlobal, Barcelona Institute for Global Health, Barcelona</wicri:regionArea><placeName><settlement type="city">Barcelone</settlement><region nuts="2" type="region">Catalogne</region></placeName></affiliation></author><author><name sortKey="Chattopadhyay, Joydev" sort="Chattopadhyay, Joydev" uniqKey="Chattopadhyay J" first="Joydev" last="Chattopadhyay">Joydev Chattopadhyay</name><affiliation wicri:level="1"><nlm:aff id="aff002"><addr-line>Agricultural and Ecological Research Unit, Indian Statistical Institute, Kolkata, India</addr-line></nlm:aff><country xml:lang="fr">Inde</country><wicri:regionArea>Agricultural and Ecological Research Unit, Indian Statistical Institute, Kolkata</wicri:regionArea><wicri:noRegion>Kolkata</wicri:noRegion></affiliation></author></analytic><series><title level="j">PLoS Neglected Tropical Diseases</title><idno type="ISSN">1935-2727</idno><idno type="eISSN">1935-2735</idno><imprint><date when="2020">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Middle East Respiratory Syndrome Coronavirus (MERS-CoV) causes severe acute respiratory illness with a case fatality rate (CFR) of 35,5%. The highest number of MERS-CoV cases are from Saudi-Arabia, the major worldwide hotspot for this disease. In the absence of neither effective treatment nor a ready-to-use vaccine and with yet an incomplete understanding of its epidemiological cycle, prevention and containment measures can be derived from mathematical models of disease epidemiology. We constructed 2-strain models to predict past outbreaks in the interval 2012–2016 and derive key epidemiological information for Macca, Madina and Riyadh. We approached variability in infection through three different disease incidence functions capturing social behavior in response to an epidemic (e.g. Bilinear, BL; Non-monotone, NM; and Saturated, SAT models). The best model combination successfully anticipated the total number of MERS-CoV clinical cases for the 2015–2016 season and accurately predicted both the number of cases at the peak of seasonal incidence and the overall shape of the epidemic cycle. The evolution in the basic reproduction number (<italic>R</italic><sub><italic>0</italic></sub>) warns that MERS-CoV may easily take an epidemic form. The best model correctly captures this feature, indicating a high epidemic risk (1≤<italic>R</italic><sub><italic>0</italic></sub>≤2,5) in Riyadh and Macca and confirming the alleged co-circulation of more than one strain. Accurate predictions of the future MERS-CoV peak week, as well as the number of cases at the peak are now possible. These results indicate public health agencies should be aware that measures for strict containment are urgently needed before new epidemics take off in the region.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Sabir, J S M" uniqKey="Sabir J">J.S.M. Sabir</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Chowell, G" uniqKey="Chowell G">G. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">PLoS Negl Trop Dis</journal-id><journal-id journal-id-type="iso-abbrev">PLoS Negl Trop Dis</journal-id><journal-id journal-id-type="publisher-id">plos</journal-id><journal-id journal-id-type="pmc">plosntds</journal-id><journal-title-group><journal-title>PLoS Neglected Tropical Diseases</journal-title></journal-title-group><issn pub-type="ppub">1935-2727</issn><issn pub-type="epub">1935-2735</issn><publisher><publisher-name>Public Library of Science</publisher-name><publisher-loc>San Francisco, CA USA</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">32059047</article-id><article-id pub-id-type="pmc">7046297</article-id><article-id pub-id-type="doi">10.1371/journal.pntd.0008065</article-id><article-id pub-id-type="publisher-id">PNTD-D-18-01971</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Biology and life sciences</subject><subj-group><subject>Organisms</subject><subj-group><subject>Viruses</subject><subj-group><subject>RNA viruses</subject><subj-group><subject>Coronaviruses</subject></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Biology and Life Sciences</subject><subj-group><subject>Microbiology</subject><subj-group><subject>Medical Microbiology</subject><subj-group><subject>Microbial Pathogens</subject><subj-group><subject>Viral Pathogens</subject><subj-group><subject>Coronaviruses</subject></subj-group></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Medicine and Health Sciences</subject><subj-group><subject>Pathology and Laboratory Medicine</subject><subj-group><subject>Pathogens</subject><subj-group><subject>Microbial Pathogens</subject><subj-group><subject>Viral Pathogens</subject><subj-group><subject>Coronaviruses</subject></subj-group></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Biology and Life Sciences</subject><subj-group><subject>Organisms</subject><subj-group><subject>Viruses</subject><subj-group><subject>Viral Pathogens</subject><subj-group><subject>Coronaviruses</subject></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Medicine and Health Sciences</subject><subj-group><subject>Epidemiology</subject><subj-group><subject>Infectious Disease Epidemiology</subject></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Medicine and Health Sciences</subject><subj-group><subject>Infectious Diseases</subject><subj-group><subject>Infectious Disease Epidemiology</subject></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Medicine and Health Sciences</subject><subj-group><subject>Epidemiology</subject></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>People and places</subject><subj-group><subject>Geographical locations</subject><subj-group><subject>Asia</subject><subj-group><subject>Saudi Arabia</subject></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Biology and Life Sciences</subject><subj-group><subject>Organisms</subject><subj-group><subject>Eukaryota</subject><subj-group><subject>Animals</subject><subj-group><subject>Vertebrates</subject><subj-group><subject>Amniotes</subject><subj-group><subject>Mammals</subject><subj-group><subject>Camels</subject></subj-group></subj-group></subj-group></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Medicine and Health Sciences</subject><subj-group><subject>Pulmonology</subject><subj-group><subject>Respiratory Infections</subject></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Medicine and Health Sciences</subject><subj-group><subject>Infectious Diseases</subject><subj-group><subject>Zoonoses</subject></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Research and Analysis Methods</subject><subj-group><subject>Mathematical and Statistical Techniques</subject><subj-group><subject>Mathematical Models</subject></subj-group></subj-group></subj-group></article-categories><title-group><article-title>A realistic two-strain model for MERS-CoV infection uncovers the high risk for epidemic propagation</article-title><alt-title alt-title-type="running-head">Seasonal forecasting of MERS-CoV epidemics</alt-title></title-group><contrib-group><contrib contrib-type="author" equal-contrib="yes"><name><surname>Sardar</surname><given-names>Tridip</given-names></name><role content-type="http://credit.casrai.org/">Conceptualization</role><role content-type="http://credit.casrai.org/">Data curation</role><role content-type="http://credit.casrai.org/">Formal analysis</role><role content-type="http://credit.casrai.org/">Investigation</role><role content-type="http://credit.casrai.org/">Methodology</role><role content-type="http://credit.casrai.org/">Validation</role><role content-type="http://credit.casrai.org/">Writing – original draft</role><role content-type="http://credit.casrai.org/">Writing – review & editing</role><xref ref-type="aff" rid="aff001"><sup>1</sup></xref></contrib><contrib contrib-type="author" equal-contrib="yes"><contrib-id authenticated="true" contrib-id-type="orcid">http://orcid.org/0000-0002-0492-2948</contrib-id><name><surname>Ghosh</surname><given-names>Indrajit</given-names></name><role content-type="http://credit.casrai.org/">Data curation</role><role content-type="http://credit.casrai.org/">Formal analysis</role><role content-type="http://credit.casrai.org/">Methodology</role><role content-type="http://credit.casrai.org/">Software</role><role content-type="http://credit.casrai.org/">Validation</role><role content-type="http://credit.casrai.org/">Writing – review & editing</role><xref ref-type="aff" rid="aff002"><sup>2</sup></xref></contrib><contrib contrib-type="author"><contrib-id authenticated="true" contrib-id-type="orcid">http://orcid.org/0000-0003-4843-6180</contrib-id><name><surname>Rodó</surname><given-names>Xavier</given-names></name><role content-type="http://credit.casrai.org/">Conceptualization</role><role content-type="http://credit.casrai.org/">Formal analysis</role><role content-type="http://credit.casrai.org/">Investigation</role><role content-type="http://credit.casrai.org/">Methodology</role><role content-type="http://credit.casrai.org/">Supervision</role><role content-type="http://credit.casrai.org/">Validation</role><role content-type="http://credit.casrai.org/">Writing – original draft</role><role content-type="http://credit.casrai.org/">Writing – review & editing</role><xref ref-type="aff" rid="aff003"><sup>3</sup></xref><xref ref-type="corresp" rid="cor001">*</xref></contrib><contrib contrib-type="author"><name><surname>Chattopadhyay</surname><given-names>Joydev</given-names></name><role content-type="http://credit.casrai.org/">Conceptualization</role><role content-type="http://credit.casrai.org/">Data curation</role><role content-type="http://credit.casrai.org/">Investigation</role><role content-type="http://credit.casrai.org/">Methodology</role><role content-type="http://credit.casrai.org/">Writing – original draft</role><role content-type="http://credit.casrai.org/">Writing – review & editing</role><xref ref-type="aff" rid="aff002"><sup>2</sup></xref></contrib></contrib-group><aff id="aff001"><label>1</label><addr-line>Department of Mathematics, Dinabandhu Andrews College, Kolkata, India</addr-line></aff><aff id="aff002"><label>2</label><addr-line>Agricultural and Ecological Research Unit, Indian Statistical Institute, Kolkata, India</addr-line></aff><aff id="aff003"><label>3</label><addr-line>ICREA &CLIMA (Climate and Health Program), ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain</addr-line></aff><contrib-group><contrib contrib-type="editor"><name><surname>Althouse</surname><given-names>Benjamin</given-names></name><role>Editor</role><xref ref-type="aff" rid="edit1"></xref></contrib></contrib-group><aff id="edit1"><addr-line>Institute for Disease Modeling, UNITED STATES</addr-line></aff><author-notes><fn fn-type="COI-statement" id="coi001"><p>No authors have competing interests.</p></fn><corresp id="cor001">* E-mail: <email>xavier.rodo@isglobal.org</email></corresp></author-notes><pub-date pub-type="epub"><day>14</day><month>2</month><year>2020</year></pub-date><pub-date pub-type="collection"><month>2</month><year>2020</year></pub-date><volume>14</volume><issue>2</issue><elocation-id>e0008065</elocation-id><history><date date-type="received"><day>14</day><month>12</month><year>2018</year></date><date date-type="accepted"><day>15</day><month>1</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 Sardar et al</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Sardar et al</copyright-holder><license xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License</ext-link>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</license-p></license></permissions><self-uri content-type="pdf" xlink:href="pntd.0008065.pdf"></self-uri><abstract><p>Middle East Respiratory Syndrome Coronavirus (MERS-CoV) causes severe acute respiratory illness with a case fatality rate (CFR) of 35,5%. The highest number of MERS-CoV cases are from Saudi-Arabia, the major worldwide hotspot for this disease. In the absence of neither effective treatment nor a ready-to-use vaccine and with yet an incomplete understanding of its epidemiological cycle, prevention and containment measures can be derived from mathematical models of disease epidemiology. We constructed 2-strain models to predict past outbreaks in the interval 2012–2016 and derive key epidemiological information for Macca, Madina and Riyadh. We approached variability in infection through three different disease incidence functions capturing social behavior in response to an epidemic (e.g. Bilinear, BL; Non-monotone, NM; and Saturated, SAT models). The best model combination successfully anticipated the total number of MERS-CoV clinical cases for the 2015–2016 season and accurately predicted both the number of cases at the peak of seasonal incidence and the overall shape of the epidemic cycle. The evolution in the basic reproduction number (<italic>R</italic><sub><italic>0</italic></sub>) warns that MERS-CoV may easily take an epidemic form. The best model correctly captures this feature, indicating a high epidemic risk (1≤<italic>R</italic><sub><italic>0</italic></sub>≤2,5) in Riyadh and Macca and confirming the alleged co-circulation of more than one strain. Accurate predictions of the future MERS-CoV peak week, as well as the number of cases at the peak are now possible. These results indicate public health agencies should be aware that measures for strict containment are urgently needed before new epidemics take off in the region.</p></abstract><abstract abstract-type="summary"><title>Author summary</title><p>There is currently no way to anticipate MERS-CoV epidemic outbreaks and strategies for disease prediction and containment are largely undermined by the limited knowledge of its epidemiological cycle. Not an effective treatment nor a vaccine for MERS-CoV exist to date. Instead, using three two-strain mathematical models that incorporate human social behavior as different disease incidence functions (e.g. bilinear, non-monotone and saturated), the best model combinations successfully anticipate the occurrence of the peak week in the season and the incidence at the peak. Our results confirm there are currently 2 strains co-circulating in the most populated regions in Saudi Arabia and highlight the high risk for large epidemic outbreaks, while the role of super-spreaders appears irrelevant for disease spread.</p></abstract><funding-group><award-group id="award001"><funding-source><institution-wrap><institution-id institution-id-type="funder-id">http://dx.doi.org/10.13039/501100002809</institution-id><institution>Generalitat de Catalunya</institution></institution-wrap></funding-source><award-id>SLT002/16/00466</award-id><principal-award-recipient><contrib-id authenticated="true" contrib-id-type="orcid">http://orcid.org/0000-0003-4843-6180</contrib-id><name><surname>Rodó</surname><given-names>Xavier</given-names></name></principal-award-recipient></award-group><award-group id="award002"><funding-source><institution-wrap><institution-id institution-id-type="funder-id">http://dx.doi.org/10.13039/501100010426</institution-id><institution>UGC-DAE Consortium for Scientific Research, University Grants Commission</institution></institution-wrap></funding-source><award-id>UGC-JRF23402016</award-id><principal-award-recipient><contrib-id authenticated="true" contrib-id-type="orcid">http://orcid.org/0000-0002-0492-2948</contrib-id><name><surname>Ghosh</surname><given-names>Indrajit</given-names></name></principal-award-recipient></award-group><funding-statement>I.G. was supported by the research fellowship from University Grants Commission (<ext-link ext-link-type="uri" xlink:href="https://www.ugc.ac.in/">https://www.ugc.ac.in/</ext-link>), Government of India. X.R. acknowledges the support of a fellowship by the PERIS PICAT project SLT002/16/00466 (<email>peris@gencat.cat</email>| <ext-link ext-link-type="uri" xlink:href="http://canalsalut.gencat.cat">http://canalsalut.gencat.cat</ext-link>) from the Catalan Ministry of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</funding-statement></funding-group><counts><fig-count count="8"></fig-count><table-count count="3"></table-count><page-count count="20"></page-count></counts><custom-meta-group><custom-meta><meta-name>PLOS Publication Stage</meta-name><meta-value>vor-update-to-uncorrected-proof</meta-value></custom-meta><custom-meta><meta-name>Publication Update</meta-name><meta-value>2020-02-27</meta-value></custom-meta><custom-meta id="data-availability"><meta-name>Data Availability</meta-name><meta-value>The website containing all MERS-CoV dataset used is the following: <ext-link ext-link-type="uri" xlink:href="http://empres-i.fao.org/eipws3g/">http://empres-i.fao.org/eipws3g/</ext-link></meta-value></custom-meta></custom-meta-group></article-meta><notes><title>Data Availability</title><p>The website containing all MERS-CoV dataset used is the following: <ext-link ext-link-type="uri" xlink:href="http://empres-i.fao.org/eipws3g/">http://empres-i.fao.org/eipws3g/</ext-link></p></notes></front></pmc><affiliations><list><country><li>Espagne</li><li>Inde</li></country><region><li>Catalogne</li></region><settlement><li>Barcelone</li></settlement></list><tree><country name="Inde"><noRegion><name sortKey="Sardar, Tridip" sort="Sardar, Tridip" uniqKey="Sardar T" first="Tridip" last="Sardar">Tridip Sardar</name></noRegion><name sortKey="Chattopadhyay, Joydev" sort="Chattopadhyay, Joydev" uniqKey="Chattopadhyay J" first="Joydev" last="Chattopadhyay">Joydev Chattopadhyay</name><name sortKey="Ghosh, Indrajit" sort="Ghosh, Indrajit" uniqKey="Ghosh I" first="Indrajit" last="Ghosh">Indrajit Ghosh</name></country><country name="Espagne"><region name="Catalogne"><name sortKey="Rod, Xavier" sort="Rod, Xavier" uniqKey="Rod X" first="Xavier" last="Rod">Xavier Rod</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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