Optimizing the impact of low-efficacy influenza vaccines

Contributed by Burton H. Singer, March 30, 2018 (sent for review February 9, 2018; reviewed by Anthony S. Fauci and David Fisman)
April 30, 2018
115 (20) 5151-5156

Significance

The efficacy of the influenza vaccine against the predominant influenza strain appears to be relatively low during this 2017–2018 season. Our analyses demonstrate the substantial effect of even low-efficacy vaccines in averting infections, hospitalizations, and particularly deaths. Our results also demonstrate that the health burden resulting from influenza is more sensitive to changes to vaccination coverage than to changes to vaccine efficacy. We further determined the uptake distribution of the 140 million doses available that would maximize the effectiveness of vaccination. Our results inform current public health policies and underscore the importance of influenza vaccination.

Abstract

The efficacy of influenza vaccines varies from one year to the next, with efficacy during the 2017–2018 season anticipated to be lower than usual. However, the impact of low-efficacy vaccines at the population level and their optimal age-specific distribution have yet to be ascertained. Applying an optimization algorithm to a mathematical model of influenza transmission and vaccination in the United States, we determined the optimal age-specific uptake of low-efficacy vaccine that would minimize incidence, hospitalization, mortality, and disability-adjusted life-years (DALYs), respectively. We found that even relatively low-efficacy influenza vaccines can be highly impactful, particularly when vaccine uptake is optimally distributed across age groups. As vaccine efficacy declines, the optimal distribution of vaccine uptake shifts toward the elderly to minimize mortality and DALYs. Health practitioner encouragement and concerted recruitment efforts are required to achieve optimal coverage among target age groups, thereby minimizing influenza morbidity and mortality for the population overall.

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Data Availability

Data deposition: The replication data reported in this paper have been deposited in the Harvard Dataverse, https://dataverse.harvard.edu (available at https://doi.org/10.7910/DVN/LB6CFZ).

Acknowledgments

P.S., J.M., M.C.F., and A.P.G. were supported by National Institutes of Health Grants U01 GM105627 and U01 GM087719.

Supporting Information

Appendix (PDF)

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Information & Authors

Information

Published in

Go to Proceedings of the National Academy of Sciences
Go to Proceedings of the National Academy of Sciences
Proceedings of the National Academy of Sciences
Vol. 115 | No. 20
May 15, 2018
PubMed: 29712866

Classifications

Data Availability

Data deposition: The replication data reported in this paper have been deposited in the Harvard Dataverse, https://dataverse.harvard.edu (available at https://doi.org/10.7910/DVN/LB6CFZ).

Submission history

Published online: April 30, 2018
Published in issue: May 15, 2018

Keywords

  1. mathematical model
  2. age structured
  3. vaccination
  4. DALY
  5. hospitalization

Acknowledgments

P.S., J.M., M.C.F., and A.P.G. were supported by National Institutes of Health Grants U01 GM105627 and U01 GM087719.

Authors

Affiliations

Pratha Sah
Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT 06510;
Jan Medlock
Department of Biomedical Sciences, Oregon State University, Corvallis, OR 97331;
Meagan C. Fitzpatrick
Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT 06510;
Center for Vaccine Development, University of Maryland School of Medicine, Baltimore, MD 21201;
Burton H. Singer1 [email protected]
Emerging Pathogens Institute, University of Florida, Gainesville, FL 32610
Alison P. Galvani
Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT 06510;

Notes

1
To whom correspondence should be addressed. Email: [email protected].
Author contributions: P.S. and A.P.G. designed research; P.S. performed research; P.S. and J.M. contributed new reagents/analytic tools; P.S., M.C.F., and A.P.G. analyzed data; P.S., J.M., M.C.F., B.H.S., and A.P.G. contributed to the modeling; and P.S., M.C.F., B.H.S., and A.P.G. wrote the paper.
Reviewers: A.S.F., National Institute of Allergy and Infectious Diseases; and D.F., University of Toronto.

Competing Interests

The authors declare no conflict of interest.

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    Optimizing the impact of low-efficacy influenza vaccines
    Proceedings of the National Academy of Sciences
    • Vol. 115
    • No. 20
    • pp. 5041-E4732

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