  Dengue is a major threat to public health in Brazil, the world's sixth
biggest country by population, with over 1.5 million cases recorded in 2019
alone. Official data on dengue case counts is delivered incrementally and, for
many reasons, often subject to delays of weeks. In contrast, data on
dengue-related Google searches and Twitter messages is available in full with
no delay. Here, we describe a model which uses online data to deliver improved
weekly estimates of dengue incidence in Rio de Janeiro. We address a key
shortcoming of previous online data disease surveillance models by explicitly
accounting for the incremental delivery of case count data, to ensure that our
approach can be used in practice. We also draw on data from Google Trends and
Twitter in tandem, and demonstrate that this leads to slightly better estimates
than a model using only one of these data streams alone. Our results provide
evidence that online data can be used to improve both the accuracy and
precision of rapid estimates of disease incidence, even where the underlying
case count data is subject to long and varied delays.
