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

• Type of Material: Article
• Authors: Kylie E. C. Ainslie, Emory University; Michael Haber, Emory University; Ryan E. Malosh, University of Michigan; Joshua G. Petrie, University of Michigan; Arnold S. Monto, University of Michigan
• Language: English
• Date: 2018-03-15
• Publisher: Wiley: 12 months
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2017 John Wiley & Sons, Ltd.
• Final Published Version (URL): http://dx.doi.org/10.1002/sim.7558
• Title of Journal or Parent Work: Statistics in Medicine
• ISSN: 0277-6715
• Volume: 37
• Issue: 6
• Start Page: 970
• End Page: 982
• Grant/Funding Information: This research was supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health (NIH) under Award R01AI110474.
• Keywords: DISEASE; observational studies; SEASON; Life Sciences & Biomedicine; Science & Technology; INFECTIONS; AGE; COHORT; influenza; Mathematics; Statistics & Probability; Research & Experimental Medicine; CHILDREN; EFFICACY; Mathematical & Computational Biology; Physical Sciences; TIME; PARAMETERS; Public, Environmental & Occupational Health; Medicine, Research & Experimental; maximum likelihood; Medical Informatics; household; vaccine effectiveness
• Research Categories: Health Sciences, Epidemiology; Biology, Biostatistics

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.