34Monitoring changes in infectious disease incidence is fundamental to outbreak detection and 35 response, intervention outcome monitoring, and identifying environmental correlates of 36 transmission. In the case of dengue, little is known about how consistently surveillance data track 37 disease burden in a population over time. Here we use four years of monthly dengue incidence 38 data from three sources -population-based ('passive') surveillance including suspected cases, 39 'sentinel' surveillance with 100% laboratory confirmation and complete reporting, and door-to-40 door ('cohort') surveillance conducted three times per week -in Iquitos, Peru, to quantify their 41 relative consistency and timeliness. Data consistency was evaluated using annual and monthly 42 expansion factors (EFs) as cohort incidence divided by incidence in each surveillance system, to 43 assess their reliability for estimating disease burden (annual) and monitoring disease trends 44 (monthly). Annually, passive surveillance data more closely estimated cohort incidence (average 45 annual EF=5) than did data from sentinel surveillance (average annual EF=19). Monthly passive 46 surveillance data generally were more consistent (ratio of sentinel/passive EF standard 47 deviations=2.2) but overestimated incidence in 26% (11/43) of months, most often during the 48 second half of the annual high season as dengue incidence typically wanes from its annual peak. 49Increases in sentinel surveillance incidence were correlated temporally (correlation coefficient = 50 0.86) with increases in the cohort, while passive surveillance data were significantly correlated at 51 both zero-lag and a one-month lag (0.63 and 0.44, respectively). Together these results suggest 52 that, rather than relying on a single data stream, a clearer picture of changes in infectious disease 53 incidence might be achieved by combining the timeliness of sentinel surveillance with the 54 representativeness of passive surveillance.Infectious disease surveillance in developing countries is often challenged by limited 57 public health resources, insufficient laboratory capacity, and incomplete reporting [1]. In order to 58 obtain high-quality data in the face of these and other challenges, the World Health Organization 59 (WHO) has recommended sentinel surveillance for many infectious diseases [2,3]. In sentinel 60 surveillance systems, resources are focused on collecting complete, timely data from a subset of 61 healthcare facilities or laboratories [4], thus requiring fewer resources than would be needed to 62 actively collect the same quality of data from all facilities (population-based active surveillance). 63 Passive surveillance systems, in which data collection is dependent on reporting by healthcare 64 facilities, are representative by virtue of being population-based, but are also subject to under-65 detection and underreporting [5]. The goal of this study is to evaluate the public health utility of 66 sentinel surveillance compared to passive surveillance for measu...

Given the limited effectiveness of strategies based solely on vector control to reduce dengue virus (DENV) transmission, it is expected that an effective vaccine could play a pivotal role in reducing the global disease burden of dengue. Of several dengue vaccines under development, Dengvaxia® from Sanofi Pasteur recently became the first to become licensed in select countries and to achieve WHO recommendation for use in certain settings, despite the fact that a number of uncertainties about its profile complicate projections of its public health impact. We used a stochastic, agent-based model for DENV transmission to perform simulations of the public health impact of dengue vaccines in light of two key uncertainties: (1) "statistical uncertainty" about the numerical value of the vaccine's efficacy against disease, and (2) "biological uncertainty" about the extent to which its efficacy against disease derives from the amelioration of symptoms, blocking of DENV infection, or some combination thereof. Simulations of a generic dengue vaccine showed that the proportion of disease episodes averted following 20 years of routine vaccination of nine-year olds at 80% coverage was sensitive to both the numerical value of vaccine efficacy and to the extent to which efficacy derives from blocking of DENV infection. Simulations of a vaccine resembling Dengvaxia® took into account that vaccine trial results substantially reduced statistical uncertainty but did not address biological uncertainty, resulting in the proportion of disease episodes averted being more sensitive to biological uncertainty than to statistical uncertainty. Taken together, our results indicate limitations associated with the use of symptomatic disease as the primary endpoint of dengue vaccine trials and highlight the importance of considering multiple forms of uncertainty in projections of a vaccine's public health impact.