Finding optimal vaccination strategies for pandemic influenza using genetic algorithms.
Identifieur interne : 000125 ( 1957/Analysis ); précédent : 000124; suivant : 000126Finding optimal vaccination strategies for pandemic influenza using genetic algorithms.
Auteurs : Rajan Patel [États-Unis] ; Ira M. Longini ; M Elizabeth HalloranSource :
- Journal of theoretical biology [ 0022-5193 ] ; 2005.
Descripteurs français
- KwdFr :
- Adolescent, Adulte, Adulte d'âge moyen, Algorithmes, Enfant, Enfant d'âge préscolaire, Flambées de maladies, Grippe humaine (), Grippe humaine (transmission), Grippe humaine (épidémiologie), Humains, Modèles biologiques, Nourrisson, Nouveau-né, Processus stochastiques, Rationnement des services de santé (), Répartition par âge, Sujet âgé, Vaccination de masse (organisation et administration), Vaccins antigrippaux (administration et posologie), Virus de la grippe A.
- MESH :
- administration et posologie : Vaccins antigrippaux.
- organisation et administration : Vaccination de masse.
- épidémiologie : Grippe humaine.
- Adolescent, Adulte, Adulte d'âge moyen, Algorithmes, Enfant, Enfant d'âge préscolaire, Flambées de maladies, Grippe humaine, Humains, Modèles biologiques, Nourrisson, Nouveau-né, Processus stochastiques, Rationnement des services de santé, Répartition par âge, Sujet âgé, Virus de la grippe A.
English descriptors
- KwdEn :
- Adolescent, Adult, Age Distribution, Aged, Algorithms, Child, Child, Preschool, Disease Outbreaks, Health Care Rationing (methods), Humans, Infant, Infant, Newborn, Influenza A virus, Influenza Vaccines (administration & dosage), Influenza, Human (epidemiology), Influenza, Human (prevention & control), Influenza, Human (transmission), Mass Vaccination (organization & administration), Middle Aged, Models, Biological, Stochastic Processes.
- MESH :
- chemical , administration & dosage : Influenza Vaccines.
- epidemiology : Influenza, Human.
- methods : Health Care Rationing.
- organization & administration : Mass Vaccination.
- prevention & control : Influenza, Human.
- transmission : Influenza, Human.
- Adolescent, Adult, Age Distribution, Aged, Algorithms, Child, Child, Preschool, Disease Outbreaks, Humans, Infant, Infant, Newborn, Influenza A virus, Middle Aged, Models, Biological, Stochastic Processes.
Abstract
In the event of pandemic influenza, only limited supplies of vaccine may be available. We use stochastic epidemic simulations, genetic algorithms (GA), and random mutation hill climbing (RMHC) to find optimal vaccine distributions to minimize the number of illnesses or deaths in the population, given limited quantities of vaccine. Due to the non-linearity, complexity and stochasticity of the epidemic process, it is not possible to solve for optimal vaccine distributions mathematically. However, we use GA and RMHC to find near optimal vaccine distributions. We model an influenza pandemic that has age-specific illness attack rates similar to the Asian pandemic in 1957-1958 caused by influenza A(H2N2), as well as a distribution similar to the Hong Kong pandemic in 1968-1969 caused by influenza A(H3N2). We find the optimal vaccine distributions given that the number of doses is limited over the range of 10-90% of the population. While GA and RMHC work well in finding optimal vaccine distributions, GA is significantly more efficient than RMHC. We show that the optimal vaccine distribution found by GA and RMHC is up to 84% more effective than random mass vaccination in the mid range of vaccine availability. GA is generalizable to the optimization of stochastic model parameters for other infectious diseases and population structures.
DOI: 10.1016/j.jtbi.2004.11.032
PubMed: 15757679
Affiliations:
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pubmed:15757679Le document en format XML
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We use stochastic epidemic simulations, genetic algorithms (GA), and random mutation hill climbing (RMHC) to find optimal vaccine distributions to minimize the number of illnesses or deaths in the population, given limited quantities of vaccine. Due to the non-linearity, complexity and stochasticity of the epidemic process, it is not possible to solve for optimal vaccine distributions mathematically. However, we use GA and RMHC to find near optimal vaccine distributions. We model an influenza pandemic that has age-specific illness attack rates similar to the Asian pandemic in 1957-1958 caused by influenza A(H2N2), as well as a distribution similar to the Hong Kong pandemic in 1968-1969 caused by influenza A(H3N2). We find the optimal vaccine distributions given that the number of doses is limited over the range of 10-90% of the population. While GA and RMHC work well in finding optimal vaccine distributions, GA is significantly more efficient than RMHC. We show that the optimal vaccine distribution found by GA and RMHC is up to 84% more effective than random mass vaccination in the mid range of vaccine availability. GA is generalizable to the optimization of stochastic model parameters for other infectious diseases and population structures.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country></list><tree><noCountry><name sortKey="Halloran, M Elizabeth" sort="Halloran, M Elizabeth" uniqKey="Halloran M" first="M Elizabeth" last="Halloran">M Elizabeth Halloran</name><name sortKey="Longini, Ira M" sort="Longini, Ira M" uniqKey="Longini I" first="Ira M" last="Longini">Ira M. Longini</name></noCountry><country name="États-Unis"><noRegion><name sortKey="Patel, Rajan" sort="Patel, Rajan" uniqKey="Patel R" first="Rajan" last="Patel">Rajan Patel</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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