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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.

Infectious diseases impose considerable burden on society, despite significant advances in technology and medicine over the past century. Advanced warning can be helpful in mitigating and preparing for an impending or ongoing epidemic. Historically, such a capability has lagged for many reasons, including in particular the uncertainty in the current state of the system and in the understanding of the processes that drive epidemic trajectories. Presently we have access to data, models, and computational resources that enable the development of epidemiological forecasting systems. Indeed, several recent challenges hosted by the U.S. government have fostered an open and collaborative environment for the development of these technologies. The primary focus of these challenges has been to develop statistical and computational methods for epidemiological forecasting, but here we consider a serious alternative based on collective human judgment. We created the web-based “Epicast” forecasting system which collects and aggregates epidemic predictions made in real-time by human participants, and with these forecasts we ask two questions: how accurate is human judgment, and how do these forecasts compare to their more computational, data-driven alternatives? To address the former, we assess by a variety of metrics how accurately humans are able to predict influenza and chikungunya trajectories. As for the latter, we show that real-time, combined human predictions of the 2014–2015 and 2015–2016 U.S. flu seasons are often more accurate than the same predictions made by several statistical systems, especially for short-term targets. We conclude that there is valuable predictive power in collective human judgment, and we discuss the benefits and drawbacks of this approach., Author summary Despite advanced and widely accessible health care, a large number of annual deaths in the United States are attributable to infectious diseases like influenza. Many of these cases could be easily prevented if sufficiently advanced warning was available. This is the main goal of epidemiological forecasting, a relatively new field that attempts to predict when and where disease outbreaks will occur. In response to growing interest in this endeavor, many forecasting frameworks have been developed for a variety of diseases. We ask whether an approach based on collective human judgment can be used to produce reasonable forecasts and how such forecasts compare with forecasts produced by purely data-driven systems. To answer this, we collected simple predictions in real-time from a set of expert and non-expert volunteers during the 2014–2015 and 2015–2016 U.S. flu seasons and during the 2014–2015 chikungunya invasion of Central America, and we report several measures of accuracy based on these predictions. By comparing these predictions with published forecasts of data-driven methods, we build an intuition for the difficulty of the task and learn that there is real value in collective human judgment.