title: Optimizing vaccine allocation at different points in time during an epidemic.
abstract: BACKGROUND
  Pandemic influenza A(H1N1) 2009 began spreading around the globe in April of 2009 and vaccination started in October of 2009. In most countries, by the time vaccination started, the second wave of pandemic H1N1 2009 was already under way. With limited supplies of vaccine, we are left to question whether it may be a good strategy to vaccinate the high-transmission groups earlier in the epidemic, but it might be a better use of resources to protect instead the high-risk groups later in the epidemic. To answer this question, we develop a deterministic epidemic model with two age-groups (children and adults) and further subdivide each age group in low and high risk.
  
  
  RESULTS
  We COMPARE optimal vaccination strategies started at various points in time in two different.
  
  
  METHODS
  a population in a developed country where children account for 24% of the population, and a population in a less developed country where children make up the majority of the population, 55%. For each of these populations, we minimize mortality or hospitalizations and we find an optimal vaccination strategy that gives the best vaccine allocation given a starting vaccination time and vaccine coverage level. We find that population structure is an important factor in determining the optimal vaccine distribution. Moreover, the optimal policy is dynamic as there is a switch in the optimal vaccination strategy at some time point just before the peak of the epidemic. For instance, with 25% vaccine coverage, it is better to protect the high-transmission groups before this point, but it is optimal to protect the most vulnerable groups afterward.
  
  
  CONCLUSIONS
  Choosing the optimal strategy before or early in the epidemic makes an important difference in minimizing the number of influenza infections, and consequently the number of influenza deaths or hospitalizations, but the optimal strategy makes little difference after the peak.
authors.affiliation: Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America.
authors.forename: Laura
authors.initials: L
authors.lastname: Matrajt
authors.forename: Ira M
authors.initials: IM
authors.lastname: Longini
mesh_terms: D000328:Adult; D000465:Algorithms; D002648:Child; D019049:Developed Countries; D003906:Developing Countries; D058872:Epidemics / Q000517:prevention & control*; D006801:Humans; D053118:Influenza A Virus, H1N1 Subtype / Q000276:immunology*; D007252:Influenza Vaccines / Q000008:administration & dosage / Q000276:immunology*; D007251:Influenza, Human / Q000453:epidemiology / Q000276:immunology* / Q000635:transmission; D008954:Models, Biological; D013997:Time Factors; D014611:Vaccination / Q000379:methods*
chemical_list: D007252:Influenza Vaccines