Enhancement of Epidemiological Models for Dengue Fever Based on Twitter Data

Autor:	Albinati, Julio, Meira Jr., Wagner, Pappa, Gisele L., Teixeira, Mauro, Marques-Toledo, Cecilia
Rok vydání:	2017
Předmět:	Computer Science - Social and Information Networks
Druh dokumentu:	Working Paper
Popis:	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. Comment: ACM Digital Health 2017
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1705.07879 Zobrazit plný text záznamu View this record from Arxiv