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Transmission Model Analysis of Nontypeable Haemophilus Influenzae

  • James S. Koopman
  • Ximin Lin
  • Stephen E. Chick
  • Janet R. Gilsdorf
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 70)

Summary

The effects of immunity stimulated by natural colonization with Nontypeable Haemophilus influenzae (NTHi) were assessed using population models of transmission and data from the literature on NTHi colonization prevalence by age, NTHi acute otitis media (AOM) incidence by age, NTHi antibody levels, and colonization duration. The models allowed both contact patterns and immunity to influence colonization and disease patterns by age. To fit the data, the models required colonization to stimulate immunity affecting both transmission (susceptibility and contagiousness) and pathogenicity (AOM given colonization). Model analysis demonstrated that immunity affecting transmission influenced AOM incidence in the first year of life from 4.6 to 39.5 times as much as immunity reducing pathogenicity. This differential decreased with age until age three and then rose again. It was important, however, across all age groups. The conclusion that immunity affecting transmission had larger effects on AOM incidence than immunity affecting pathogenicity was robust to model form and to reasonable variation in the data. Because sensitivity to NTHi strain interactions and age patterns of infection by strain could not be assessed and because data on the distribution of NTHi strains across all ages are deficient, this conclusion must still be viewed as tentative. Nonetheless, these results make it imperative that trials of potential NTHi vaccines be designed to insure accurate assessment of effects on transmission. The models presented here provide the basis for the construction of discrete individual simulation models for use in designing the most informative and powerful vaccine trials.

Key words

Infection Models Vaccines Transmission Bacteria 

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Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  • James S. Koopman
    • 1
    • 2
  • Ximin Lin
    • 1
  • Stephen E. Chick
    • 3
    • 4
  • Janet R. Gilsdorf
    • 1
    • 5
  1. 1.Department of EpidemiologyUniversity of MichiganAnn Arbor
  2. 2.Center for the Study of Complex SystemsUniversity of MichiganAnn Arbor
  3. 3.Department of Industrial and Operations EngineeringUniversity of MichiganAnn Arbor
  4. 4.INSEADFontainbleauFrance
  5. 5.Department of PediatricsUniversity of MichiganAnn Arbor

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