The role of cellular immunity in Influenza H1N1 population dynamics
Identifieur interne : 000A02 ( Main/Exploration ); précédent : 000A01; suivant : 000A03The role of cellular immunity in Influenza H1N1 population dynamics
Auteurs : Venkata R. Duvvuri [Canada] ; Jane M. Heffernan [Canada] ; Seyed M. Moghadas [Canada] ; Bhargavi Duvvuri [Canada] ; Hongbin Guo [Canada] ; David N. Fisman [Canada] ; Jianhong Wu [Canada] ; Gillian E. Wu [Canada]Source :
- BMC Infectious Diseases [ 1471-2334 ] ; 2012.
Abstract
Pre-existing cellular immunity has been recognized as one of the key factors in determining the outcome of influenza infection by reducing the likelihood of clinical disease and mitigates illness. Whether, and to what extent, the effect of this self-protective mechanism can be captured in the population dynamics of an influenza epidemic has not been addressed.
We applied previous findings regarding T-cell cross-reactivity between the 2009 pandemic H1N1 strain and seasonal H1N1 strains to investigate the possible changes in the magnitude and peak time of the epidemic. Continuous Monte-Carlo Markov Chain (MCMC) model was employed to simulate the role of pre-existing immunity on the dynamical behavior of epidemic peak.
From the MCMC model simulations, we observed that, as the size of subpopulation with partially effective pre-existing immunity increases, the mean magnitude of the epidemic peak decreases, while the mean time to reach the peak increases. However, the corresponding ranges of these variations are relatively small.
Our study concludes that the effective role of pre-existing immunity in alleviating disease outcomes (e.g., hospitalization) of novel influenza virus remains largely undetectable in population dynamics of an epidemic. The model outcome suggests that rapid clinical investigations on T-cell assays remain crucial for determining the protection level conferred by pre-existing cellular responses in the face of an emerging influenza virus.
Url:
DOI: 10.1186/1471-2334-12-329
PubMed: 23192104
PubMed Central: 3552667
Affiliations:
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Duvvuri</name><affiliation wicri:level="2"><nlm:aff id="I1">Centre for Disease Modelling, York University, Toronto, Ontario</nlm:aff><country>Canada</country><placeName><region type="province">Ontario</region></placeName><wicri:cityArea>Centre for Disease Modelling, York University, Toronto</wicri:cityArea></affiliation><affiliation wicri:level="2"><nlm:aff id="I2">Department of Biology, York University, Toronto, Ontario</nlm:aff><country>Canada</country><placeName><region type="province">Ontario</region></placeName><wicri:cityArea>Department of Biology, York University, Toronto</wicri:cityArea></affiliation></author><author><name sortKey="Heffernan, Jane M" sort="Heffernan, Jane M" uniqKey="Heffernan J" first="Jane M" last="Heffernan">Jane M. Heffernan</name><affiliation wicri:level="2"><nlm:aff id="I1">Centre for Disease Modelling, York University, Toronto, Ontario</nlm:aff><country>Canada</country><placeName><region type="province">Ontario</region></placeName><wicri:cityArea>Centre for Disease Modelling, York University, Toronto</wicri:cityArea></affiliation><affiliation wicri:level="2"><nlm:aff id="I3">Department of Mathematics & Statistics, York University, Toronto, Ontario</nlm:aff><country>Canada</country><placeName><region type="province">Ontario</region></placeName><wicri:cityArea>Department of Mathematics & Statistics, York University, Toronto</wicri:cityArea></affiliation></author><author><name sortKey="Moghadas, Seyed M" sort="Moghadas, Seyed M" uniqKey="Moghadas S" first="Seyed M" last="Moghadas">Seyed M. Moghadas</name><affiliation wicri:level="2"><nlm:aff id="I1">Centre for Disease Modelling, York University, Toronto, Ontario</nlm:aff><country>Canada</country><placeName><region type="province">Ontario</region></placeName><wicri:cityArea>Centre for Disease Modelling, York University, Toronto</wicri:cityArea></affiliation><affiliation wicri:level="2"><nlm:aff id="I3">Department of Mathematics & Statistics, York University, Toronto, Ontario</nlm:aff><country>Canada</country><placeName><region type="province">Ontario</region></placeName><wicri:cityArea>Department of Mathematics & Statistics, York University, Toronto</wicri:cityArea></affiliation></author><author><name sortKey="Duvvuri, Bhargavi" sort="Duvvuri, Bhargavi" uniqKey="Duvvuri B" first="Bhargavi" last="Duvvuri">Bhargavi Duvvuri</name><affiliation wicri:level="2"><nlm:aff id="I4">School of Kinesiology and Health Sciences, York University, Toronto, Ontario</nlm:aff><country>Canada</country><placeName><region type="province">Ontario</region></placeName><wicri:cityArea>School of Kinesiology and Health Sciences, York University, Toronto</wicri:cityArea></affiliation></author><author><name sortKey="Guo, Hongbin" sort="Guo, Hongbin" uniqKey="Guo H" first="Hongbin" last="Guo">Hongbin Guo</name><affiliation wicri:level="2"><nlm:aff id="I1">Centre for Disease Modelling, York University, Toronto, Ontario</nlm:aff><country>Canada</country><placeName><region type="province">Ontario</region></placeName><wicri:cityArea>Centre for Disease Modelling, York University, Toronto</wicri:cityArea></affiliation></author><author><name sortKey="Fisman, David N" sort="Fisman, David N" uniqKey="Fisman D" first="David N" last="Fisman">David N. Fisman</name><affiliation wicri:level="2"><nlm:aff id="I5">Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario</nlm:aff><country>Canada</country><placeName><region type="province">Ontario</region></placeName><wicri:cityArea>Dalla Lana School of Public Health, University of Toronto, Toronto</wicri:cityArea></affiliation></author><author><name sortKey="Wu, Jianhong" sort="Wu, Jianhong" uniqKey="Wu J" first="Jianhong" last="Wu">Jianhong Wu</name><affiliation wicri:level="2"><nlm:aff id="I1">Centre for Disease Modelling, York University, Toronto, Ontario</nlm:aff><country>Canada</country><placeName><region type="province">Ontario</region></placeName><wicri:cityArea>Centre for Disease Modelling, York University, Toronto</wicri:cityArea></affiliation><affiliation wicri:level="2"><nlm:aff id="I3">Department of Mathematics & Statistics, York University, Toronto, Ontario</nlm:aff><country>Canada</country><placeName><region type="province">Ontario</region></placeName><wicri:cityArea>Department of Mathematics & Statistics, York University, Toronto</wicri:cityArea></affiliation></author><author><name sortKey="Wu, Gillian E" sort="Wu, Gillian E" uniqKey="Wu G" first="Gillian E" last="Wu">Gillian E. Wu</name><affiliation wicri:level="2"><nlm:aff id="I2">Department of Biology, York University, Toronto, Ontario</nlm:aff><country>Canada</country><placeName><region type="province">Ontario</region></placeName><wicri:cityArea>Department of Biology, York University, Toronto</wicri:cityArea></affiliation><affiliation wicri:level="2"><nlm:aff id="I4">School of Kinesiology and Health Sciences, York University, Toronto, Ontario</nlm:aff><country>Canada</country><placeName><region type="province">Ontario</region></placeName><wicri:cityArea>School of Kinesiology and Health Sciences, York University, Toronto</wicri:cityArea></affiliation></author></analytic><series><title level="j">BMC Infectious Diseases</title><idno type="eISSN">1471-2334</idno><imprint><date when="2012">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>Pre-existing cellular immunity has been recognized as one of the key factors in determining the outcome of influenza infection by reducing the likelihood of clinical disease and mitigates illness. Whether, and to what extent, the effect of this self-protective mechanism can be captured in the population dynamics of an influenza epidemic has not been addressed.</p></sec><sec><title>Methods</title><p>We applied previous findings regarding T-cell cross-reactivity between the 2009 pandemic H1N1 strain and seasonal H1N1 strains to investigate the possible changes in the magnitude and peak time of the epidemic. Continuous Monte-Carlo Markov Chain (MCMC) model was employed to simulate the role of pre-existing immunity on the dynamical behavior of epidemic peak.</p></sec><sec><title>Results</title><p>From the MCMC model simulations, we observed that, as the size of subpopulation with partially effective pre-existing immunity increases, the mean magnitude of the epidemic peak decreases, while the mean time to reach the peak increases. However, the corresponding ranges of these variations are relatively small.</p></sec><sec><title>Conclusions</title><p>Our study concludes that the effective role of pre-existing immunity in alleviating disease outcomes (e.g., hospitalization) of novel influenza virus remains largely undetectable in population dynamics of an epidemic. The model outcome suggests that rapid clinical investigations on T-cell assays remain crucial for determining the protection level conferred by pre-existing cellular responses in the face of an emerging influenza virus.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Shinde, V" uniqKey="Shinde V">V Shinde</name></author><author><name sortKey="Bridges, C" uniqKey="Bridges C">C Bridges</name></author><author><name sortKey="Uyeki, T" uniqKey="Uyeki T">T Uyeki</name></author><author><name sortKey="Shu, B" uniqKey="Shu B">B Shu</name></author><author><name sortKey="Balish, A" uniqKey="Balish A">A Balish</name></author><author><name sortKey="Xu, X" uniqKey="Xu X">X Xu</name></author><author><name sortKey="Lindstrom, Sea" uniqKey="Lindstrom S">Sea Lindstrom</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Zimmer, S" uniqKey="Zimmer S">S Zimmer</name></author><author><name sortKey="Burke, D" 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last="Duvvuri">Venkata R. Duvvuri</name></region><name sortKey="Duvvuri, Bhargavi" sort="Duvvuri, Bhargavi" uniqKey="Duvvuri B" first="Bhargavi" last="Duvvuri">Bhargavi Duvvuri</name><name sortKey="Duvvuri, Venkata R" sort="Duvvuri, Venkata R" uniqKey="Duvvuri V" first="Venkata R" last="Duvvuri">Venkata R. Duvvuri</name><name sortKey="Fisman, David N" sort="Fisman, David N" uniqKey="Fisman D" first="David N" last="Fisman">David N. Fisman</name><name sortKey="Guo, Hongbin" sort="Guo, Hongbin" uniqKey="Guo H" first="Hongbin" last="Guo">Hongbin Guo</name><name sortKey="Heffernan, Jane M" sort="Heffernan, Jane M" uniqKey="Heffernan J" first="Jane M" last="Heffernan">Jane M. Heffernan</name><name sortKey="Heffernan, Jane M" sort="Heffernan, Jane M" uniqKey="Heffernan J" first="Jane M" last="Heffernan">Jane M. Heffernan</name><name sortKey="Moghadas, Seyed M" sort="Moghadas, Seyed M" uniqKey="Moghadas S" first="Seyed M" last="Moghadas">Seyed M. Moghadas</name><name sortKey="Moghadas, Seyed M" sort="Moghadas, Seyed M" uniqKey="Moghadas S" first="Seyed M" last="Moghadas">Seyed M. Moghadas</name><name sortKey="Wu, Gillian E" sort="Wu, Gillian E" uniqKey="Wu G" first="Gillian E" last="Wu">Gillian E. Wu</name><name sortKey="Wu, Gillian E" sort="Wu, Gillian E" uniqKey="Wu G" first="Gillian E" last="Wu">Gillian E. Wu</name><name sortKey="Wu, Jianhong" sort="Wu, Jianhong" uniqKey="Wu J" first="Jianhong" last="Wu">Jianhong Wu</name><name sortKey="Wu, Jianhong" sort="Wu, Jianhong" uniqKey="Wu J" first="Jianhong" last="Wu">Jianhong Wu</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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