 An agent-based model of dengue virus transmission shows how uncertainty about breakthrough infections influences vaccination impact projectionsT Alex Perkins, Robert C Reiner, Guido España, Quirine A ten Bosch, Amit Verma, Kelly A Liebman, Valerie A Paz-Soldan, John P Elder, Amy C Morrison, Steven T Stoddard, Uriel Kitron, Gonzalo M Vazquez-Prokopec, Thomas W Scott and David L Smith PLOS Computational Biology, 2019, vol. 15, issue 3, 1-32 Abstract: 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.Author summary: Vaccines are vital tools for safeguarding public health from a variety of infectious disease threats. When decisions are being made about investments in vaccination, computational models provide decision makers with projections of the benefits of vaccination. There are many types of uncertainty that can affect these projections, such as statistical uncertainty about the extent to which vaccination reduces one’s risk of experiencing disease. While this type of uncertainty is well accounted for by vaccine trials, a different type of uncertainty often is not: whether the vaccine blocks infection altogether or simply reduces the severity of disease symptoms. In the case of the latter, “breakthrough infections” occur, meaning that those who are vaccinated are protected but those who are not receive little or no indirect benefit from herd immunity. Focusing on a newly licensed vaccine for dengue, we developed and applied a new simulation model of dengue virus transmission to assess the extent to which uncertainty about breakthrough infections contributes to uncertainty about vaccination impact. We found that a vaccine that prevents breakthrough infections is capable of nearly doubling the impact of vaccination as compared to a vaccine that confers protection solely by reducing the severity of disease symptoms. Date: 2019 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1006710 DOI: 10.1371/journal.pcbi.1006710 Access Statistics for this article More articles in PLOS Computational Biology from Public Library of Science |
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