Optimal control and sensitivity analysis of an influenza model with treatment and vaccination.
Identifieur interne : 002446 ( Main/Exploration ); précédent : 002445; suivant : 002447Optimal control and sensitivity analysis of an influenza model with treatment and vaccination.
Auteurs : J M Tchuenche [Tanzanie] ; S A Khamis ; F B Agusto ; S C MpesheSource :
- Acta biotheoretica [ 1572-8358 ] ; 2011.
Descripteurs français
- KwdFr :
- Antiviraux (administration et posologie), Flambées de maladies (), Grippe humaine (), Grippe humaine (immunologie), Grippe humaine (traitement médicamenteux), Grippe humaine (épidémiologie), Humains, Modèles biologiques, Orthomyxoviridae (immunologie), Simulation numérique, Vaccination (normes), Vaccins antigrippaux (administration et posologie).
- MESH :
- administration et posologie : Antiviraux, Vaccins antigrippaux.
- immunologie : Grippe humaine, Orthomyxoviridae.
- normes : Vaccination.
- traitement médicamenteux : Grippe humaine.
- épidémiologie : Grippe humaine.
- Flambées de maladies, Grippe humaine, Humains, Modèles biologiques, Simulation numérique.
English descriptors
- KwdEn :
- Antiviral Agents (administration & dosage), Computer Simulation, Disease Outbreaks (prevention & control), Humans, Influenza Vaccines (administration & dosage), Influenza, Human (drug therapy), Influenza, Human (epidemiology), Influenza, Human (immunology), Influenza, Human (prevention & control), Models, Biological, Orthomyxoviridae (immunology), Vaccination (standards).
- MESH :
- chemical , administration & dosage : Antiviral Agents, Influenza Vaccines.
- drug therapy : Influenza, Human.
- epidemiology : Influenza, Human.
- immunology : Influenza, Human, Orthomyxoviridae.
- prevention & control : Disease Outbreaks, Influenza, Human.
- standards : Vaccination.
- Computer Simulation, Humans, Models, Biological.
Abstract
We formulate and analyze the dynamics of an influenza pandemic model with vaccination and treatment using two preventive scenarios: increase and decrease in vaccine uptake. Due to the seasonality of the influenza pandemic, the dynamics is studied in a finite time interval. We focus primarily on controlling the disease with a possible minimal cost and side effects using control theory which is therefore applied via the Pontryagin's maximum principle, and it is observed that full treatment effort should be given while increasing vaccination at the onset of the outbreak. Next, sensitivity analysis and simulations (using the fourth order Runge-Kutta scheme) are carried out in order to determine the relative importance of different factors responsible for disease transmission and prevalence. The most sensitive parameter of the various reproductive numbers apart from the death rate is the inflow rate, while the proportion of new recruits and the vaccine efficacy are the most sensitive parameters for the endemic equilibrium point.
DOI: 10.1007/s10441-010-9095-8
PubMed: 20140696
Affiliations:
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Le document en format XML
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xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="immunologie" xml:lang="fr"><term>Grippe humaine</term><term>Orthomyxoviridae</term></keywords><keywords scheme="MESH" qualifier="immunology" xml:lang="en"><term>Influenza, Human</term><term>Orthomyxoviridae</term></keywords><keywords scheme="MESH" qualifier="normes" xml:lang="fr"><term>Vaccination</term></keywords><keywords scheme="MESH" qualifier="prevention & control" xml:lang="en"><term>Disease Outbreaks</term><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="standards" xml:lang="en"><term>Vaccination</term></keywords><keywords scheme="MESH" qualifier="traitement médicamenteux" xml:lang="fr"><term>Grippe humaine</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Grippe humaine</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Computer Simulation</term><term>Humans</term><term>Models, Biological</term></keywords><keywords 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Due to the seasonality of the influenza pandemic, the dynamics is studied in a finite time interval. We focus primarily on controlling the disease with a possible minimal cost and side effects using control theory which is therefore applied via the Pontryagin's maximum principle, and it is observed that full treatment effort should be given while increasing vaccination at the onset of the outbreak. Next, sensitivity analysis and simulations (using the fourth order Runge-Kutta scheme) are carried out in order to determine the relative importance of different factors responsible for disease transmission and prevalence. The most sensitive parameter of the various reproductive numbers apart from the death rate is the inflow rate, while the proportion of new recruits and the vaccine efficacy are the most sensitive parameters for the endemic equilibrium point.</div></front></TEI><affiliations><list><country><li>Tanzanie</li></country></list><tree><noCountry><name sortKey="Agusto, F B" sort="Agusto, F B" uniqKey="Agusto F" first="F B" last="Agusto">F B Agusto</name><name sortKey="Khamis, S A" sort="Khamis, S A" uniqKey="Khamis S" first="S A" last="Khamis">S A Khamis</name><name sortKey="Mpeshe, S C" sort="Mpeshe, S C" uniqKey="Mpeshe S" first="S C" last="Mpeshe">S C Mpeshe</name></noCountry><country name="Tanzanie"><noRegion><name sortKey="Tchuenche, J M" sort="Tchuenche, J M" uniqKey="Tchuenche J" first="J M" last="Tchuenche">J M Tchuenche</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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