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PLoS Comput Biol. 2019 Mar 20;15(3):e1006710. doi: 10.1371/journal.pcbi.1006710. eCollection 2019 Mar.

An agent-based model of dengue virus transmission shows how uncertainty about breakthrough infections influences vaccination impact projections.

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Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, United States of America.
Fogarty International Center, National Institutes of Health, Bethesda, MD, United States of America.
Department of Epidemiology and Biostatistics, Indiana University, Bloomington, IN, United States of America.
Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA.
Department of Entomology and Nematology, University of California, Davis, CA, United States of America.
Department of Global Community Health and Behavioral Sciences, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, United States of America.
Institute for Behavioral and Community Health, Graduate School of Public Health, San Diego State University, San Diego, CA, United States of America.
Department of Environmental Sciences, Emory University, Atlanta, GA, United States of America.
Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, United States of America.


Prophylactic vaccination is a powerful tool for reducing the burden of infectious diseases, due to a combination of direct protection of vaccinees and indirect protection of others via herd immunity. Computational models play an important role in devising strategies for vaccination by making projections of its impacts on public health. Such projections are subject to uncertainty about numerous factors, however. For example, many vaccine efficacy trials focus on measuring protection against disease rather than protection against infection, leaving the extent of breakthrough infections (i.e., disease ameliorated but infection unimpeded) among vaccinees unknown. Our goal in this study was to quantify the extent to which uncertainty about breakthrough infections results in uncertainty about vaccination impact, with a focus on vaccines for dengue. To realistically account for the many forms of heterogeneity in dengue virus (DENV) transmission, which could have implications for the dynamics of indirect protection, we used a stochastic, agent-based model for DENV transmission informed by more than a decade of empirical studies in the city of Iquitos, Peru. Following 20 years of routine vaccination of nine-year-old children at 80% coverage, projections of the proportion of disease episodes averted varied by a factor of 1.76 (95% CI: 1.54-2.06) across the range of uncertainty about breakthrough infections. This was equivalent to the range of vaccination impact projected across a range of uncertainty about vaccine efficacy of 0.268 (95% CI: 0.210-0.329). Until uncertainty about breakthrough infections can be addressed empirically, our results demonstrate the importance of accounting for it in models of vaccination impact.

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