research-article

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.

References

[1]

Julio Albinati,Wagner Meira Jr, and Gisele Lobo Pappa. 2016. An Accurate Gaussian Process-Based Early Warning System for Dengue Fever. In 2016 Brazilian Conference on Intelligent Systems, BRACIS 2016, Recife, Brazil, October 10--13, 2016.

[2]

Benjamin M. Althouse, Yih Yng Ng, and Derek A. T. Cummings. 2011. Prediction of Dengue Incidence Using Search Query Surveillance. PLoS Negl Trop Dis 5, 8 (08 2011), 1--7.

[3]

David A. Broniatowski, Michael J. Paul, and Mark Dredze. 2013. National and Local Influenza Surveillance through Twitter: An Analysis of the 2012--2013 Influenza Epidemic. PLoS ONE 8, 12 (12 2013).

[4]

Nazri Che Dom, A Abu Hassan, Z Abd Latif, and Rodziah Ismail. 2013. Generating temporal model using climate variables for the prediction of dengue cases in Subang Jaya, Malaysia. Asian Pacific Journal of Tropical Disease 3, 5 (2013), 352--361.

[5]

Matthew D Eastin, Eric Delmelle, Irene Casas, Joshua Wexler, and Cameron Self. 2014. Intra-and interseasonal autoregressive prediction of dengue outbreaks using local weather and regional climate for a tropical environment in Colombia. The American journal of tropical medicine and hygiene 91, 3 (2014), 598--610.

[6]

Nicholas Generous, Geoffrey Fairchild, Alina Deshpande, Sara Y. Del Valle, and Reid Priedhorsky. 2014. Global Disease Monitoring and Forecasting with Wikipedia. PLoS Comput Biol 10, 11 (11 2014), 1--16.

[7]

Myriam Gharbi, Philippe Quenel, Joël Gustave, Sylvie Cassadou, Guy La Ruche, Laurent Girdary, and Laurence Marrama. 2011. Time series analysis of dengue incidence in Guadeloupe, French West Indies: forecasting models using climate variables as predictors. BMC infectious diseases 11 (2011).

[8]

Janaína Gomide, Adriano Veloso, Wagner Meira, Jr., Virgílio Almeida, Fabrício Benevenuto, Fernanda Ferraz, and Mauro Teixeira. 2011. Dengue Surveillance Based on a Computational Model of Spatio-temporal Locality of Twitter. In Proceedings of the 3rd International Web Science Conference (WebSci '11). ACM, New York, NY, USA, Article 3, 8 pages.

Digital Library

[9]

Instituto Brasileiro de Geografia e EstatÃstica. 2014. Pesquisa Nacional por Amostra de DomicÃlios -- PNAD 2014. http://ibge.gov.br/home/estatistica/populacao/acessoainternet2014/default_xls.shtm. (2014). {Online; accessed 18-April-2017; in Portuguese}.

[10]

Edson Zangiacomi Martinez and Elisângela Aparecida Soares da Silva. 2011. Predicting the number of cases of dengue infection in Ribeirão Preto, São Paulo State, Brazil, using a SARIMA model. Cadernos de saude publica 27, 9 (2011), 1809--1818.

[11]

S Promprou,MJaroensutasinee, and K Jaroensutasinee. 2006. Forecasting dengue haemorrhagic fever cases in Southern Thailand using ARIMA Models. Dengue Bulletin 30 (2006), 99.

[12]

Carl Edward Rasmussen and Christopher K. I. Williams. 2006. Gaussian Processes for Machine Learning. The MIT Press, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142.

[13]

Takeshi Sakaki, Makoto Okazaki, and Yutaka Matsuo. 2010. Earthquake Shakes Twitter Users: Real-time Event Detection by Social Sensors. In Proceedings of the 19th International Conference on World Wide Web (WWW '10). ACM, New York, NY, USA, 851--860.

Digital Library

[14]

Bhatt Samir, PeterW. Gething, Oliver J. Brady, Jane P. Messina, AndrewW. Farlow, Catherine L. Moyes, John M. Drake, John S. Brownstein, Anne G. Hoen, Osman Sankoh, Monica F. Myers, Dylan B. George, Thomas Jaenisch, G. R. William Wint, Cameron P. Simmons, Thomas W. Scott, Jeremy J. Farrar, and Simon I. Hay. 2013. The global distribution and burden of dengue. Nature 496, 7446 (apr 2013), 504--507. http://www.nature.com/nature/journal/v496/n7446/abs/ nature12060.html#supplementary-information

[15]

Jeffrey Shaman, Alicia Karspeck, Wan Yang, James Tamerius, and Marc Lipsitch. 2013. Real-time influenza forecasts during the 2012--2013 season. Nature communications 4 (2013).

[16]

Roberto CSNP Souza, Denise EF de Brito, Renato M Assunção, and Wagner Meira Jr. 2015. A latent shared-component generative model for real-time disease surveillance using Twitter data. arXiv preprint arXiv:1510.05981 (2015).

[17]

Roberto C. S. N. P. Souza, Denise E. F. de Brito, Rodrigo L. Cardoso, Derick M. de Oliveira, Wagner Meira Jr., and Gisele L. Pappa. 2014. An Evolutionary Methodology for Handling Data Scarcity and Noise in Monitoring Real Events from Social Media Data. In Advances in Artificial Intelligence - IBERAMIA 2014 - 14th Ibero-American Conference on AI, Santiago de Chile, Chile, November 24--27, 2014, Proceedings (Lecture Notes in Computer Science), Ana L. C. Bazzan and Karim Pichara (Eds.), Vol. 8864. Springer, 295--306.

[18]

Alison Wiltshire. 2006. Developing early warning systems: A checklist. In Proc. 3rd Int. Conf. Early Warning (EWC).

[19]

World Health Organization Media Centre. 2016. Dengue and severe dengue. http://www.who.int/mediacentre/factsheets/fs117/en/. (2016). {Online; accessed 25-April-2016}.

Cited By

Arifi DResch BSantillana MGuan WKnoblauch SLautenbach SJaenisch TMorales IHavas C(2025)Geosocial Media’s Early Warning Capabilities Across US County-Level Political Clusters: Observational StudyJMIR Infodemiology10.2196/585395(e58539)Online publication date: 30-Jan-2025
https://doi.org/10.2196/58539
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
Show More Cited By

Index Terms

Enhancement of Epidemiological Models for Dengue Fever Based on Twitter Data

Recommendations

Forecasting Dengue Fever Epidemics using ARIMA Model
AICCC '19: Proceedings of the 2019 2nd Artificial Intelligence and Cloud Computing Conference

Despite the control and surveillance of dengue fever in various ways, but still found many dengue patients in Thailand every year. The main objective of the research is to develop a model for the prediction of dengue fever. Model development on data of ...
Infodemiology for Syndromic Surveillance of Dengue and Typhoid Fever in the Philippines

Finding determinants of disease outbreaks before its occurrence is necessary in reducing its impact in populations. The supposed advantage of obtaining information brought by automated systems fall short because of the inability to access real-time data ...
A Critical Study of Selected Classification Algorithms for Dengue Fever and Dengue Hemorrhagic Fever
FIT '13: Proceedings of the 2013 11th International Conference on Frontiers of Information Technology

Dengue fever is viral infection caused by dengue virus which is transmitted in human body by bite of female Eddie mosquito. There are 50 million people suffer from it globally every year. Pakistan has been victim of this rapidly growing disease from ...

Comments

Information & Contributors

Information

Published In

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]

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 July 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Conference

DH '17

DH '17: International Conference on Digital Health

July 2 - 5, 2017

London, United Kingdom

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

11
Total Citations
View Citations
133
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Arifi DResch BSantillana MGuan WKnoblauch SLautenbach SJaenisch TMorales IHavas C(2025)Geosocial Media’s Early Warning Capabilities Across US County-Level Political Clusters: Observational StudyJMIR Infodemiology10.2196/585395(e58539)Online publication date: 30-Jan-2025
https://doi.org/10.2196/58539
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
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten