# Enhancement of Epidemiological Models for Dengue Fever Based on Twitter Data

## TL;DR

This paper proposes a framework that leverages Twitter data to improve the timeliness and accuracy of dengue fever epidemiological models, especially for long-term predictions, by providing near real-time incidence estimates.

## Contribution

It introduces a novel framework that integrates online social media data with traditional models to enhance early warning systems for dengue fever.

## Key findings

- Framework achieves more accurate predictions for delays ≥ 4 weeks.
- Twitter data effectively estimates current dengue incidence.
- Improves early warning capabilities for epidemiological models.

## Abstract

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.