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Author Notes:

Correspondence to: Michael Haber, Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322. mhaber@emory.edu

Subjects:

Research Funding:

This research was supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health (NIH) under Award R01AI110474.

Keywords:

  • Science & Technology
  • Life Sciences & Biomedicine
  • Physical Sciences
  • Mathematical & Computational Biology
  • Public, Environmental & Occupational Health
  • Medical Informatics
  • Medicine, Research & Experimental
  • Statistics & Probability
  • Research & Experimental Medicine
  • Mathematics
  • household
  • influenza
  • maximum likelihood
  • observational studies
  • vaccine effectiveness
  • CHILDREN
  • INFECTIONS
  • PARAMETERS
  • EFFICACY
  • DISEASE
  • COHORT
  • SEASON
  • TIME
  • AGE

Maximum likelihood estimation of influenza vaccine effectiveness against transmission from the household and from the community

Tools:

Journal Title:

Statistics in Medicine

Volume:

Volume 37, Number 6

Publisher:

, Pages 970-982

Type of Work:

Article | Post-print: After Peer Review

Abstract:

Influenza vaccination is recommended as the best way to protect against influenza infection and illness. Due to seasonal changes in influenza virus types and subtypes, a new vaccine must be produced, and vaccine effectiveness (VE) must be estimated, annually. Since 2010, influenza vaccination has been recommended universally in the United States, making randomized clinical trials unethical. Recent studies have used a monitored household cohort study design to determine separate VE estimates against influenza transmission from the household and community. We developed a probability model and accompanying maximum likelihood procedure to estimate vaccine-related protection against transmission of influenza from the household and the community. Using agent-based stochastic simulations, we validated that we can obtain maximum likelihood estimates of transmission parameters and VE close to their true values. Sensitivity analyses to examine the effect of deviations from our assumptions were conducted. We used our method to estimate transmission parameters and VE from data from a monitored household study in Michigan during the 2012-2013 influenza season and were able to detect a significant protective effect of influenza vaccination against community-acquired transmission.

Copyright information:

© 2017 John Wiley & Sons, Ltd.

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