  Epidemiological early warning systems for dengue fever rely on up-to-date
epidemiological data to forecast future incidence. However, epidemiological
data typically requires time to be available, due to the application of
time-consuming laboratorial tests. This implies that epidemiological models
need to issue predictions with larger antecedence, making their task even more
difficult. On the other hand, online platforms, such as Twitter or Google,
allow us to obtain samples of users' interaction in near real-time and can be
used as sensors to monitor current incidence. In this work, we propose a
framework to exploit online data sources to mitigate the lack of up-to-date
epidemiological data by obtaining estimates of current incidence, which are
then explored by traditional epidemiological models. We show that the proposed
framework obtains more accurate predictions than alternative approaches, with
statistically better results for delays greater or equal to 4 weeks.
