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Epidemiological early warning systems for dengue fever rely on up-to-dateepidemiological data to forecast future incidence. However, epidemiologicaldata typically requires time to be available, due to the application oftime-consuming laboratorial tests. This implies that epidemiological modelsneed to issue predictions with larger antecedence, making their task even moredifficult. 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 beused as sensors to monitor current incidence. In this work, we propose aframework to exploit online data sources to mitigate the lack of up-to-dateepidemiological data by obtaining estimates of current incidence, which arethen explored by traditional epidemiological models. We show that the proposedframework obtains more accurate predictions than alternative approaches, withstatistically better results for delays greater or equal to 4 weeks.

Enhancement of Epidemiological Models for Dengue Fever Based on Twitter Data