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Enhancement of Epidemiological Models for Dengue Fever Based on Twitter Data

Authors:

Julio Albinati,

Wagner Meira, Jr.,

Gisele L. Pappa,

Mauro Teixeira,

Cecilia Marques-ToledoAuthors Info & Claims

DH '17: Proceedings of the 2017 International Conference on Digital Health

Pages 109 - 118

https://doi.org/10.1145/3079452.3079464

Published: 02 July 2017 Publication History

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.

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Cited By

Mesquita SPerfeito LPaolotti DGonçalves-Sá J(2025)Epidemiological methods in transition: Minimizing biases in classical and digital approachesPLOS Digital Health10.1371/journal.pdig.00006704:1(e0000670)Online publication date: 13-Jan-2025
https://doi.org/10.1371/journal.pdig.0000670
Melo CMageste LGuaraldo LPaula DWakimoto M(2024)Use of Digital Tools in Arbovirus Surveillance: Scoping ReviewJournal of Medical Internet Research10.2196/5747626(e57476)Online publication date: 18-Nov-2024
https://doi.org/10.2196/57476
Sylvestre EJoachim CCécilia-Joseph EBouzillé GCampillo-Gimenez BCuggia MCabié A(2022)Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic reviewPLOS Neglected Tropical Diseases10.1371/journal.pntd.001005616:1(e0010056)Online publication date: 7-Jan-2022
https://doi.org/10.1371/journal.pntd.0010056
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Enhancement of Epidemiological Models for Dengue Fever Based on Twitter Data

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cover image ACM Other conferences

DH '17: Proceedings of the 2017 International Conference on Digital Health

July 2017

256 pages

ISBN:9781450352499

DOI:10.1145/3079452

General Chair:
Patty Kostkova
University College London
,
Program Chairs:
Floriana Grasso
University of Liverpool
,
Carlos Castillo
Eurecat
,
Yelena Mejova
QCRI
,
Arnold Bosman
Transmissible

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 July 2017

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DH '17: International Conference on Digital Health

July 2 - 5, 2017

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Cited By

Mesquita SPerfeito LPaolotti DGonçalves-Sá J(2025)Epidemiological methods in transition: Minimizing biases in classical and digital approachesPLOS Digital Health10.1371/journal.pdig.00006704:1(e0000670)Online publication date: 13-Jan-2025
https://doi.org/10.1371/journal.pdig.0000670
Melo CMageste LGuaraldo LPaula DWakimoto M(2024)Use of Digital Tools in Arbovirus Surveillance: Scoping ReviewJournal of Medical Internet Research10.2196/5747626(e57476)Online publication date: 18-Nov-2024
https://doi.org/10.2196/57476
Sylvestre EJoachim CCécilia-Joseph EBouzillé GCampillo-Gimenez BCuggia MCabié A(2022)Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic reviewPLOS Neglected Tropical Diseases10.1371/journal.pntd.001005616:1(e0010056)Online publication date: 7-Jan-2022
https://doi.org/10.1371/journal.pntd.0010056
Brum PCândido Teixeira MVimieiro RAraújo EMeira Jr WLobo Pappa G(2022)Political polarization on Twitter during the COVID-19 pandemic: a case study in BrazilSocial Network Analysis and Mining10.1007/s13278-022-00949-x12:1Online publication date: 23-Sep-2022
https://doi.org/10.1007/s13278-022-00949-x
Jiang YHuang XLi Z(2021)Spatiotemporal Patterns of Human Mobility and Its Association with Land Use Types during COVID-19 in New York CityISPRS International Journal of Geo-Information10.3390/ijgi1005034410:5(344)Online publication date: 18-May-2021
https://doi.org/10.3390/ijgi10050344
Shahid FOny SAlbi TChellappan SVashistha AIslam A(2020)Learning from Tweets: Opportunities and Challenges to Inform Policy Making During Dengue EpidemicProceedings of the ACM on Human-Computer Interaction10.1145/33928754:CSCW1(1-27)Online publication date: 29-May-2020
https://dl.acm.org/doi/10.1145/3392875
Amin SUddin MZeb MAlarood AMahmoud MAlkinani M(2020)Detecting Dengue/Flu Infections Based on Tweets Using LSTM and Word EmbeddingIEEE Access10.1109/ACCESS.2020.30311748(189054-189068)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3031174
Barros JDuggan JRebholz-Schuhmann D(2019)Internet-Based Sources of Health Information: A Systematic Literature Review (Preprint)Journal of Medical Internet Research10.2196/13680Online publication date: 24-Feb-2019
https://doi.org/10.2196/13680
Guimarães ABispo TAzevedo AMacedo H(2019)LaNa2Proceedings of the XV Brazilian Symposium on Information Systems10.1145/3330204.3330281(1-8)Online publication date: 20-May-2019
https://dl.acm.org/doi/10.1145/3330204.3330281
Kostkova P(2018)Disease surveillance data sharing for public health: the next ethical frontiersLife Sciences, Society and Policy10.1186/s40504-018-0078-x14:1Online publication date: 4-Jul-2018
https://doi.org/10.1186/s40504-018-0078-x

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