Abstract
Influenza-like illness (ILI) is an acute respiratory infection causes substantial mortality and morbidity. Predict Influenza trends and response to a health disease rapidly is crucial to diminish the loss of life. In this paper, we employ the long short term memory (LSTM) recurrent neural networks to forecast the influenza trends. We are the first one to use multiple and novel data sources including virologic surveillance, influenza geographic spread, Google trends, climate and air pollution to predict influenza trends. Moreover, We find there are several environmental and climatic factors have the significant correlation with ILI rate.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Lofgren, E., Fefferman, N.H., Naumov, Y.N., Gorski, J., Naumova, E.N.: Influenza seasonality: underlying causes and modeling theories. J. Virol. 81(11), 5429–5436 (2007)
Mcneil, D.G.: This Flu Season Is the Worst in Nearly a Decade (2018). https://www.nytimes.com/2018/01/26/health/flu-rates-deaths.html. Accessed 13 Mar 2018
Centers for Disease Control and Prevention: Situation Update: Summary of Weekly FluView Report (2018). https://www.cdc.gov/flu/weekly/summary.htm. Accessed 5 Apr 2018
Han, M., Yan, M., Li, J., Ji, S., Li, Y.: Generating uncertain networks based on historical network snapshots. In: Du, D.-Z., Zhang, G. (eds.) COCOON 2013. LNCS, vol. 7936, pp. 747–758. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38768-5_68
O’Connor, F.: Google Flu Trends calls out sick, indefinitely (2015). https://www.pcworld.com/article/2974153/websites/google-flu-trends-calls-out-sick-indefinitely.html. Accessed 13 Mar 2018
Dugas, A.F., Hsieh, Y.H., Levin, S.R., Pines, J.M., Mareiniss, D.P., Mohareb, A., Gaydos, C.A., Perl, T.M., Rothman, R.E.: Google flu trends: correlation with emergency department influenza rates and crowding metrics. Clin. Infect. Dis. 54(4), 463–469 (2012)
Santillana, M., Nguyen, A.T., Dredze, M., Paul, M.J., Nsoesie, E.O., Brownstein, J.S.: Combining search, social media, and traditional data sources to improve influenza surveillance. PLoS Comput. Biol. 11(10), e1004513 (2015)
Han, M., Yan, M., Cai, Z., Li, Y.: An exploration of broader influence maximization in timeliness networks with opportunistic selection. J. Netw. Comput. Appl. 63, 39–49 (2016)
Albinali, H., Han, M., Wang, J., Gao, H., Li, Y.: The roles of social network mavens. In: 2016 12th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN, pp. 1–8. IEEE (2016)
Guo, X., Liu, B., Chen, L., Chen, G., Pan, Y., Zhang, J.: Bayesian inference for functional dynamics exploring in fMRI data. Comput. Math. Methods Med. 2016, 1–9 (2016)
Dietterich, T.G.: Machine learning for sequential data: a review. In: Caelli, T., Amin, A., Duin, R.P.W., de Ridder, D., Kamel, M. (eds.) SSPR /SPR 2002. LNCS, vol. 2396, pp. 15–30. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-70659-3_2
Georgia Health News: Georgia’s flu death toll now at 51; season’s peak is still ahead (online)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Azzouni, A., Pujolle, G.: A long short-term memory recurrent neural network framework for network traffic matrix prediction. arXiv preprint arXiv:1705.05690 (2017)
Gers, F.A., Schmidhuber, J., Cummins, F.: Learning to forget: continual prediction with LSTM (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Liu, L., Han, M., Zhou, Y., Wang, Y. (2018). LSTM Recurrent Neural Networks for Influenza Trends Prediction. In: Zhang, F., Cai, Z., Skums, P., Zhang, S. (eds) Bioinformatics Research and Applications. ISBRA 2018. Lecture Notes in Computer Science(), vol 10847. Springer, Cham. https://doi.org/10.1007/978-3-319-94968-0_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-94968-0_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-94967-3
Online ISBN: 978-3-319-94968-0
eBook Packages: Computer ScienceComputer Science (R0)