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

T Alex Perkins,Quirine A Ten Bosch,Amy C Morrison,Steven T Stoddard,David L Smith,Robert C Reiner,Thomas W Scott,Guido España,Kelly A Liebman,Amit Verma,Gonzalo M Vazquez-Prokopec,Valerie A Paz-Soldan,John P Elder,Uriel Kitron,Samuel Alizon

doi:10.1371/journal.pcbi.1006710

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.

Highlights

Computational models have much to contribute to the advancement of vaccines as tools for public health benefit
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

Summary

Introduction

Computational models have much to contribute to the advancement of vaccines as tools for public health benefit. Other aspects of a vaccine’s profile can include whether protection is “leaky” (reduces per-event risk uniformly for all) or “all or none” (reduces risk fully, but only for some), or whether protection wanes over time or depends on an individual’s age or other characteristics. Uncertainty about these and other aspects of a vaccine’s profile can be addressed through sensitivity analysis [10,11] or by fitting a model to vaccine trial data [12,13]. Uncertainty about a vaccine’s profile can be propagated into uncertainty about vaccination impact

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Results

Discussion

Conclusion