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Online flu epidemiological deep modeling on disease contact network

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Abstract

The surveillance and preventions of infectious disease epidemics such as influenza and Ebola are important and challenging issues. It is therefore crucial to characterize the disease progress and epidemics process efficiently and accurately. Computational epidemiology can model the progression of the disease and its underlying contact network, but as yet lacks the ability to process of real-time and fine-grained surveillance data. Social media, on the other hand, provides timely and detailed disease surveillance but is insensible to the underlying contact network and disease model. To address these challenges simultaneously, this paper proposes a novel semi-supervised neural network framework that integrates the strengths of computational epidemiology and social media mining techniques for influenza epidemiological modeling. Specifically, this framework learns social media users’ health states and intervention actions in real time, regularized by the underlying disease model and contact network. The learned knowledge from social media can then be fed into the computational epidemic model to improve the efficiency and accuracy of disease diffusion modeling. We propose an online optimization algorithm that iteratively processes the above interactive learning process. The extensive experimental results provided demonstrated that our approach can not only outperform competing methods by a substantial margin in forecasting disease outbreaks, but also characterize the individual-level disease progress and diffusion effectively and efficiently.

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Notes

  1. https://www.cdc.gov/flu/glossary/index.htm

  2. https://www.census.gov/data.html

  3. DEMHS regions are defined by the Division of Emergency Management and Homeland Security.

  4. Influenza report for Connecticut: http://www.ct.gov/dph/cwp/view.asp?a=3136&q=410788. Accessed Apr 2016.

  5. CDC Flu Symptoms & Severity: http://www.cdc.gov/flu/professionals/acip/clinical.htm

References

  1. Achrekar H, Gandhe A, Lazarus R, Yu S-H, Liu B (2011) Predicting flu trends using Twitter data. In: INFOCOM WKSHPS, pp 702–707

  2. Achrekar H, Gandhe A, Lazarus R, Yu S-H, Liu B (2013) Online social networks flu trend tracker: a novel sensory approach to predict flu trends. In: Biomedical engineering systems and technologies. Springer, pp 353–368

  3. Anderson RM, May RM (1979) Population biology of infectious diseases part i. Nature 280:361–7

    Article  Google Scholar 

  4. Barrett C, Beckman R, Khan M, Kumar V, Marathe M, Stretz P, Dutta T, Lewis B (2009) Generation and analysis of large synthetic social contact networks. In: WSC, pp 1003–1014

  5. Barrett C, Bisset K, Eubank S, Feng X, Marathe M (2008) Episimdemics: an efficient algorithm for simulating the spread of infectious disease over large realistic social networks. In: ICS, pp 1–12

  6. Barrett C, Beckman R, Khan M, Anil Kumar V, Marathe M, Stretz PE, Dutta T, Lewis B (2009) Generation and analysis of large synthetic social contact networks. In: Winter simulation conference. Winter simulation conference, pp 1003–1014

  7. Bhatele A, Yeom J. -S., Jain N, Kuhlman CJ, Livnat Y, Bisset KR, Kale LV, Marathe MV (2017) Massively parallel simulations of spread of infectious diseases over realistic social networks. In: Proceedings of the 17th IEEE/ACM international symposium on cluster, cloud and grid computing. IEEE Press, pp 689–694

  8. Bishop CM, et al. (2006) Pattern recognition and machine learning, vol 4. Springer, New York

    Google Scholar 

  9. Bisset K, Chen J, Feng X, Kumar VSA, Marathe M (2009) Epifast: a fast algorithm for large scale realistic epidemic simulations on distributed memory systems. In: ICS, pp 430–439

  10. Bisset KR, Chen J, Feng X, Kumar V, Marathe M (2009) Epifast: a fast algorithm for large scale realistic epidemic simulations on distributed memory systems. In: ICS. ACM, pp 430–439

  11. Brennan S, Sadilek A, Kautz H (2013) Towards understanding global spread of disease from everyday interpersonal interactions. In: IJCAI. AAAI Press, pp 2783–2789

  12. Centers for Disease Control and Prevention (CDC) (2015) CDC fluview interactive. Accessed May 31, 2015. http://www.cdc.gov/flu/weekly/fluviewinteractive.htm

  13. Chen L, Hossain KT, Butler P, Ramakrishnan N, Prakash BA (2014) Flu gone viral: syndromic surveillance of flu on Twitter using temporal topic models. In: ICDM. IEEE, pp 2783–2789

  14. Choisy M, Guégan J-F, Rohani P (2007) Mathematical modeling of infectious diseases dynamics. Encyclopedia of infectious diseases: modern methodologies, pp 379–404

  15. Collier N, Son NT, Nguyen NM (2011) Omg u got flu? analysis of shared health messages for bio-surveillance. J Biomedical Semantics 2(S-5):S9

    Article  Google Scholar 

  16. Craft ME, Volz E, Packer C, Meyers LA (2011) Disease transmission in territorial populations: the small-world network of serengeti lions. J R Soc Interface 8 (59):776–786

    Article  Google Scholar 

  17. Culotta A (2010) Towards detecting influenza epidemics by analyzing Twitter messages. In: Proceedings of the First Workshop on Social Media Analytics. ACM, pp 115–122

  18. Dredze M, Paul MJ, Bergsma S, Tran H (2013) Carmen: a Twitter geolocation system with applications to public health. In: AAAI workshop on expanding the boundaries of HIAI. Citeseer, pp 20–24

  19. Gao Y, Zhao L (2018) Incomplete label multi-task ordinal regression for spatial event scale forecasting. In: AAAI conference on artificial intelligence

  20. Gough K (1977) The estimation of latent and infectious periods. Biometrika 64 (3):559–565

    Article  Google Scholar 

  21. Groendyke C, Welch D, Hunter DR (2012) A network-based analysis of the 1861 hagelloch measles data. Biometrics 68(3):755–765

    Article  Google Scholar 

  22. Hirose H, Wang L (2012) Prediction of infectious disease spread using Twitter: a case of influenza. In: PAAP. IEEE, pp 100–105

  23. Krieck M, Dreesman J, Otrusina L, Denecke K (2011) A new age of public health: Identifying disease outbreaks by analyzing tweets. In: Websci

  24. Lamb A, Paul MJ, Dredze M (2013) Separating fact from fear: tracking flu infections on Twitter. In: HLT-NAACL, pp 789–795

  25. Murray JD (2002) Mathematical biology i: an introduction, vol 17 of interdisciplinary applied mathematics

  26. Pan American Health Organization (PAHO) (2015) PAHO interactive. Accessed May 31, 2015. www.paho.org/hq/

  27. Paul MJ, Dredze M (2012) A model for mining public health topics from Twitter. Health 11:16–6

    Google Scholar 

  28. Presanis AM, De Angelis D, Hagy A, Reed C, Riley S, Cooper BS, Finelli L, Biedrzycki P, Lipsitch M, et al. (2009) The severity of pandemic H1N1 influenza in the united states, from april to July 2009: a Bayesian analysis. PLoS Med 6(12):e1000207

    Article  Google Scholar 

  29. Vynnycky E, White RG (2010) An introduction to infectious disease modelling. Oxford University Press, Oxford

    Google Scholar 

  30. Wang J, Zhao L (2018) Multi-instance domain adaptation for vaccine adverse event detection. In: Proceedings of the 2018 World Wide Web conference on World Wide Web. International World Wide Web conferences steering committee, pp 97–106

  31. Wang J, Zhao L, Ye Y, Zhang Y (2018) Adverse event detection by integrating twitter data and vaers. Journal of Biomedical Semantics 9(1):19

    Article  Google Scholar 

  32. World Health Organization (WHO) (2015) WHO ebola data and statistics. Accessed May 29, 2015. http://apps.who.int/gho/data/view.ebola-sitrep.ebola-summary-latest

  33. World Health Organization (WHO) (2015) WHO influenza (season) fact sheet. Accessed May 15, 2015. http://www.who.int/mediacentre/factsheets/fs211/en/

  34. Yu H, Ho C, Juan Y, Lin C (2013) Libshorttext: a library for short-text classification and analysis. Technical report. http://www.csie.ntu.edu.tw/∼cjlin/papers/libshorttext.pdf

  35. Zhao L, Chen F, Lu C-T, Ramakrishnan N (2015) Spatiotemporal event forecasting in social media. In: SDM, vol 15. SIAM, pp 963–971

  36. Zhao L, Chen F, Lu C-T, Ramakrishnan N (2016) Multi-resolution spatial event forecasting in social media. In: 2016 IEEE 16Th international conference on data mining (ICDM). IEEE, pp 689–698

  37. Zhao L, Ye J, Chen F, Lu C-T, Ramakrishnan N (2016) Hierarchical incomplete multi-source feature learning for spatiotemporal event forecasting. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 2085–2094

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Acknowledgments

This work was supported by the National Science Foundation grants: 1755850 and 1841520. We are also grateful of the support of computational resources partially supported by NVIDA company. This research was also supported by the Intelligence Advanced Research Projects Activity (IARPA) via DoI/NBC contract number D12PC000337. Additionally, this work has been partially supported by DTRA CNIMS Contract HDTRA1-11-D-0016-0001.

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Authors and Affiliations

  1. Department of Information Science and Technology, George Mason University, Fairfax, VA, 22030, USA

    Liang Zhao

  2. Biocomplexity Institute of Virginia Tech, Arlington, VA, 22203, USA

    Jiangzhuo Chen

  3. Department of Computer Science, University at Albany, Albany, NY, 12222, USA

    Feng Chen

  4. Department of Computer Science, Texas Tech University, Lubbock, TX, 79409, USA

    Fang Jin

  5. Microsoft, Redmond, WA, 98052, USA

    Wei Wang

  6. Department of Computer Science, Virginia Tech, Arlington, VA, 22203, USA

    Chang-Tien Lu & Naren Ramakrishnan

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  1. Liang Zhao
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  2. Jiangzhuo Chen
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  3. Feng Chen
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  4. Fang Jin
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  5. Wei Wang
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Correspondence to Liang Zhao.

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Zhao, L., Chen, J., Chen, F. et al. Online flu epidemiological deep modeling on disease contact network. Geoinformatica 24, 443–475 (2020). https://doi.org/10.1007/s10707-019-00376-9

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  • DOI: https://doi.org/10.1007/s10707-019-00376-9

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