Vaccination programs for endemic infections: modelling real versus apparent impacts of vaccine and infection characteristics

Ragonnet, Romain, Trauer, James M., Denholm, Justin T., Geard, Nicholas L., Hellard, Margaret, and McBryde, Emma (2015) Vaccination programs for endemic infections: modelling real versus apparent impacts of vaccine and infection characteristics. Scientific Reports, 5. 15468. pp. 1-11.

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Vaccine effect, as measured in clinical trials, may not accurately reflect population-level impact. Furthermore, little is known about how sensitive apparent or real vaccine impacts are to factors such as the risk of re-infection or the mechanism of protection. We present a dynamic compartmental model to simulate vaccination for endemic infections. Several measures of effectiveness are calculated to compare the real and apparent impact of vaccination, and assess the effect of a range of infection and vaccine characteristics on these measures. Although broadly correlated, measures of real and apparent vaccine effectiveness can differ widely. Vaccine impact is markedly underestimated when primary infection provides partial natural immunity, when coverage is high and when post-vaccination infectiousness is reduced. Despite equivalent efficacy, 'all or nothing' vaccines are more effective than 'leaky' vaccines, particularly in settings with high risk of re-infection and transmissibility. Latent periods result in greater real impacts when risk of re-infection is high, but this effect diminishes if partial natural immunity is assumed. Assessments of population-level vaccine effects against endemic infections from clinical trials may be significantly biased, and vaccine and infection characteristics should be considered when modelling outcomes of vaccination programs, as their impact may be dramatic.

Item ID: 42208
Item Type: Article (Research - C1)
ISSN: 2045-2322
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This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material.

Funders: National Health and Medical Research Council (NHMRC), Burnet Institute
Projects and Grants: NHMRC CDF1034464
Date Deposited: 30 May 2016 04:21
FoR Codes: 11 MEDICAL AND HEALTH SCIENCES > 1103 Clinical Sciences > 110309 Infectious Diseases @ 40%
11 MEDICAL AND HEALTH SCIENCES > 1117 Public Health and Health Services > 111706 Epidemiology @ 40%
16 STUDIES IN HUMAN SOCIETY > 1605 Policy and Administration > 160508 Health Policy @ 20%
SEO Codes: 92 HEALTH > 9201 Clinical Health (Organs, Diseases and Abnormal Conditions) > 920109 Infectious Diseases @ 60%
92 HEALTH > 9203 Indigenous Health > 920309 Pacific Peoples Health - Health System Performance (incl. Effectiveness of Interventions) @ 20%
92 HEALTH > 9202 Health and Support Services > 920207 Health Policy Evaluation @ 20%
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