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The Role of Event-Based Biosurveillance in Biodefense

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Abstract

The term biosurveillance refers to the collection, analysis, and dissemination of multiple types of data for early warning, early detection, situational awareness, and consequence management support of biological events. This broad view encompasses both traditional and more recent approaches to surveillance as they relate to natural and intentional outbreaks and epidemics in human and agricultural populations. This chapter focuses on event-based biosurveillance utilizing data available from the internet. We provide an overview of the process of event-based biosurveillance, describe important computer methods for handling and analyzing collected data, and discuss future directions and research needs.

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Notes

  1. 1.

    https://lucene.apache.org

  2. 2.

    http://www.elastic.co

  3. 3.

    A detailed case study illustrating the application of biosurveillance related to an outbreak of dengue fever can be found in [9].

References

  1. Paquet C, Coulombier D, Kaiser R, Ciotti M. Epidemic intelligence: a new framework for strengthening disease surveillance in Europe. Euro Surveill. 2006;11(12):212–4.

    Article  CAS  Google Scholar 

  2. Endsley MR. Toward a theory of situation awareness in dynamic systems. Hum Factors. 1995;37(1):32–64.

    Article  Google Scholar 

  3. O’Shea J. Digital disease detection: A systematic review of event-based internet biosurveillance systems. Int J Med Inform. 2017;101:15–22. https://doi.org/10.1016/j.ijmedinf.2017.01.019.

    Article  PubMed  Google Scholar 

  4. Declich S, Carter AO. Public health surveillance: historical origins, methods and evaluation. Bull World Health Organ. 1994;72(2):285–304.

    CAS  PubMed  PubMed Central  Google Scholar 

  5. CDC. Global disease detection operations center: event-based surveillance. 2016. https://www.cdc.gov/globalhealth/healthprotection/gddopscenter/how.html. Accessed 26 Aug 2017.

  6. Brownstein JS, Freifeld CC, Madoff LC. Digital disease detection – harnessing the Web for public health surveillance. N Engl J Med. 2009;360(21):2153–7. https://doi.org/10.1056/NEJMp0900702.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Choi J, Cho Y, Shim E, Woo H. Web-based infectious disease surveillance systems and public health perspectives: a systematic review. BMC Public Health. 2016;16(1):1238. https://doi.org/10.1186/s12889-016-3893-0.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Hartley D, Nelson N, Walters R, Arthur R, Yangarber R, Madoff L, Linge J, Mawudeku A, Collier N, Brownstein J, Thinus G, Lightfoot N. Landscape of international event-based biosurveillance. Emerg Health Threats J. 2010;3:e3. https://doi.org/10.3134/ehtj.10.003.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Hartley DM, Nelson NP, Arthur RR, Barboza P, Collier N, Lightfoot N, Linge JP, van der Goot E, Mawudeku A, Madoff LC, Vaillant L, Walters R, Yangarber R, Mantero J, Corley CD, Brownstein JS. An overview of internet biosurveillance. Clin Microbiol Infect. 2013;19(11):1006–13. https://doi.org/10.1111/1469-0691.12273.

    Article  CAS  PubMed  Google Scholar 

  10. Milinovich GJ, Williams GM, Clements AC, Hu W. Internet-based surveillance systems for monitoring emerging infectious diseases. Lancet Infect Dis. 2014;14(2):160–8. https://doi.org/10.1016/S1473-3099(13)70244-5.

    Article  PubMed  Google Scholar 

  11. WHO. Early detection, assessment and response to acute public health events: implementation of early warning and response with a focus on event-based surveillance. 2014. http://www.who.int/ihr/publications/WHO_HSE_GCR_LYO_2014.4/en/. Accessed 26 Aug 2017.

  12. Mykhalovskiy E, Weir L. The global public health intelligence network and early warning outbreak detection: a Canadian contribution to global public health. Can J Public Health. 2006;97(1):42–4.

    PubMed  Google Scholar 

  13. Linge J, Steinberger R, Fuart F, Bucci S, Belyaeva J, Gemo M, Al-Khudhairy D, Yangarber R, van der Goot E. MedISys: medical information system. In: Asimakopoulou E, Bessis N, editors. Advanced ICTs for disaster management and threat detection: collaborative and distributed frameworks, Chapter 9. New York: IGI Global; 2010. p. 131–42.

    Chapter  Google Scholar 

  14. Collier N, Doan S, Kawazoe A, Goodwin RM, Conway M, Tateno Y, Ngo QH, Dien D, Kawtrakul A, Takeuchi K, Shigematsu M, Taniguchi K. BioCaster: detecting public health rumors with a Web-based text mining system. Bioinformatics. 2008;24(24):2940–1. https://doi.org/10.1093/bioinformatics/btn534.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Nelson NP, Brownstein JS, Hartley DM. Event-based biosurveillance of respiratory disease in Mexico, 2007–2009: connection to the 2009 influenza A(H1N1) pandemic? Euro Surveill. 2010;15(30)

    Google Scholar 

  16. Nelson NP, Yang L, Reilly AR, Hardin JE, Hartley DM. Event-based internet biosurveillance: relation to epidemiological observation. Emerg Themes Epidemiol. 2012;9(1):4. https://doi.org/10.1186/1742-7622-9-4.

    Article  PubMed  PubMed Central  Google Scholar 

  17. DHS. National Biosurveillance Integration Center Strategic Plan. 2012. https://www.dhs.gov/sites/default/files/publications/nbic-strategic-plan-public-2012.pdf. Accessed 26 Aug 2017.

  18. National Strategy for Biosurveillance. https://obamawhitehouse.archives.gov/sites/default/files/National_Strategy_for_Biosurveillance_July_2012.pdf. Accessed 12 Dec 2018

  19. Margevicius KJ, Generous N, Taylor-McCabe KJ, Brown M, Daniel WB, Castro L, Hengartner A, Deshpande A. Advancing a framework to enable characterization and evaluation of data streams useful for biosurveillance. PLoS One. 2014;9(1):e83730. https://doi.org/10.1371/journal.pone.0083730.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Gottesdiener E. Good practices for developing user requirements. CrossTalk J Defense Software Eng. 2008;21(3):13–7.

    Google Scholar 

  21. HHS. Enterprise life cycle artifacts. 2014. https://www.hhs.gov/ocio/eplc/Enterprise%20Performance%20Lifecycle%20Artifacts/eplc_artifacts.html. Accessed 26 Aug 2017.

  22. Mitre. Eliciting, collecting, and developing requirements. 2017. https://www.mitre.org/publications/systems-engineering-guide/se-lifecycle-building-blocks/requirements-engineering/eliciting-collecting-and-developing-requirements. Accessed 13 Aug 2017.

  23. Software Engineering Institute. A framework for software product line practice, Ver. 5.0, Requirements engineering. (2017). https://www.mitre.org/publications/systems-engineering-guide/se-lifecycle-building-blocks/r. Accessed 13 Aug 2017

  24. Walters RA, Harlan PA, Nelson NP, Hartley DM, Voeller JG. Data sources for biosurveillance. In: Voeller JG, editor. Wiley handbook of science and technology for homeland security, vol 4: Wiley; 2010. p. 2431–47. https://doi.org/10.1002/9780470087923.hhs15.

  25. Charles-Smith LE, Reynolds TL, Cameron MA, Conway M, Lau EH, Olsen JM, Pavlin JA, Shigematsu M, Streichert LC, Suda KJ, Corley CD. Using social media for actionable disease surveillance and outbreak management: A systematic literature review. PLoS One. 2015;10(10):e0139701. https://doi.org/10.1371/journal.pone.0139701.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Hartley DM. Using social media and internet data for public health surveillance: the importance of talking. Milbank Q. 2014;92(1):34–9. https://doi.org/10.1111/1468-0009.12039.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Hartley DM, Giannini CM, Wilson S, Frieder O, Margolis PA, Kotagal UR, White DL, Connelly BL, Wheeler DS, Tadesse DG, Macaluso M. Coughing, sneezing, and aching online: Twitter and the volume of influenza-like illness in a pediatric hospital. PLoS One. 2017;12(7):e0182008. https://doi.org/10.1371/journal.pone.0182008.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Volkova S, Charles LE, Harrison J, Corley CD. Uncovering the relationships between military community health and affects expressed in social media. EPJ Data Sci. 2017;6:9. https://doi.org/10.1140/epjds/s13688-017-0102-z.

    Article  Google Scholar 

  29. Bach M, Jordan S, Hartung S, Santos-Hövener C, Wright MT. Participatory epidemiology: the contribution of participatory research to epidemiology. Emerg Themes Epidemiol. 2017. doi:https://doi.org/10.1186/s12982-017-0056-4

  30. Paolotti D, Carnahan A, Colizza V, Eames K, Edmunds J, Gomes G, Koppeschaar C, Rehn M, Smallenburg R, Turbelin C, Van Noort S, Vespignani A. Web-based participatory surveillance of infectious diseases: the influenzanet participatory surveillance experience. Clin Microbiol Infect. 2014;20(1):17–21. https://doi.org/10.1111/1469-0691.12477.

    Article  CAS  PubMed  Google Scholar 

  31. Smolinski MS, Crawley AW, Baltrusaitis K, Chunara R, Olsen JM, Wójcik O, Santillana M, Nguyen A, Brownstein JS. Flu near you: crowdsourced symptom reporting spanning 2 influenza seasons. Am J Public Health. 2015;105(10):2124–30. https://doi.org/10.2105/AJPH.2015.302696.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Nguyen A, Brownstein JS. Flu near you: crowdsourced symptom reporting spanning 2 influenza seasons. Am J Public Health. 2015;105(10):2124–30. https://doi.org/10.2105/AJPH.2015.302696.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Garimella VRK, Alfayad A, Weber I. Social media image analysis for public health. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. New York: ACM; 2016.

    Google Scholar 

  34. Generous N, Margevicius KJ, Taylor-McCabe KJ, Brown M, Daniel WB, Castro L, Hengartner A, Deshpande A. Selecting essential information for biosurveillance – a multi-criteria decision analysis. PLoS One. 2014;9(1):e86601. https://doi.org/10.1371/journal.pone.0086601.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Collmann J, Matei SA, editors. Ethical reasoning in big data: an exploratory analysis, Computational social sciences. Series. Basel: Springer; 2016.

    Google Scholar 

  36. Collmann J, Robinson A. Designing ethical practice in biosurveillance: the project Argus doctrine. In: Zeng D, et al., editors. Infectious disease informatics and biosurveillance, integrated series in information systems, vol. 27. New York: Springer; 2011.

    Google Scholar 

  37. Collier N. What’s unusual in online disease outbreak news? J Biomed Semantics. 2010;1(1):2. https://doi.org/10.1186/2041-1480-1-2.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Torii M, Yin L, Nguyen T, Mazumdar CT, Liu H, Hartley DM, Nelson NP. An exploratory study of a text classification framework for Internet-based surveillance of emerging epidemics. Int J Med Inform. 2011;80(1):56–66. https://doi.org/10.1016/j.ijmedinf.2010.10.015.

    Article  PubMed  Google Scholar 

  39. Studer R, Benjamins R, Fensel D. Knowledge engineering: principles and methods. Data Knowl Eng. 1998;25(1–2):161–98.

    Article  Google Scholar 

  40. Collier N, Kawazoe A, Jin L, Shigematsu M, Dien D, Barrero RA, Takeuchi K, Kawtrakul A. A multilingual ontology for infectious disease surveillance: rationale, design and challenges. Lang Resour Eval. 2006;40:405–13.

    Article  Google Scholar 

  41. Smith WP, Chappell AR, Corley CD. Medical and Transmission Vector Vocabulary Alignment with Schema.org. In Proceedings of the International Conference on Biomedical Ontology (ICBO 2015), July 27-30, 2015, Lisbon, Portugal, edited by FM Couto and J Hastings. Aachen: CEUR Workshop Proceedings. http://ceur-ws.org/Vol-1515/regular11.pdf

  42. Daughton AR, Priedhorsky R, Fairchild G, Generous N, Hengartner A, Abeyta E, Velappan N, Lillo A, Stark K, Deshpande A. A globally-applicable disease ontology for biosurveillance. Anthology of Biosurveillance Diseases (ABD). 2016. https://arxiv.org/abs/1609.05774

  43. Wilson JM, Polyak MG, Blake JW, Collmann J. A heuristic indication and warning staging model for detection and assessment of biological events. J Am Med Inform Assoc. 2008;15(2):158–71. https://doi.org/10.1197/jamia.M2558.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Keller M, Freifeld CC, Brownstein JS. Automated vocabulary discovery for geo-parsing online epidemic intelligence. BMC Bioinf. 2009;10:385. https://doi.org/10.1186/1471-2105-10-385.

    Article  Google Scholar 

  45. Yangarber R, Jokipii L, Rauramo A, Huttunen S. Extracting information about outbreaks of infectious epidemics. In: Proceedings of the Human Language Technology Conference/Conference on Empirical Methods in Natural Language Processing: HLT/EMNLP-2005. Vancouver, Canada, 2005; pp. 22–23. Available at: http://aclweb.org/anthology/H/H05/H05-2012.pdf.

  46. Grein TW, Kamara KB, Rodier G, Plant AJ, Bovier P, Ryan MJ, Ohyama T, Heymann DL. Rumors of disease in the global village: outbreak verification. Emerg Infect Dis. 2000;6(2):97–102.

    Article  CAS  Google Scholar 

  47. Barboza P, Vaillant L, Mawudeku A, Nelson NP, Hartley DM, Madoff LC, Linge JP, Collier N, Brownstein JS, Yangarber R, Astagneau P, Early Alerting Reporting Project of The Global Health Security Initiative. Evaluation of epidemic intelligence systems integrated in the early alerting and reporting project for the detection of A/H5N1 influenza events. PLoS One. 2013;8(3):e57252. https://doi.org/10.1371/journal.pone.0057252.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Velasco E, Agheneza T, Denecke K, Kirchner G, Eckmanns T. Social media and internet-based data in global systems for public health surveillance: a systematic review. Milbank Q. 2014;92(1):7–33. https://doi.org/10.1111/1468-0009.12038.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Corley CD, Lancaster MJ, Brigantic RT, Chung JS, Walters RA, Arthur RR, Bruckner-Lea CJ, Calapristi A, Dowling G, Hartley DM, Kennedy S, Kircher A, Klucking S, Lee EK, McKenzie T, Nelson NP, Olsen J, Pancerella C, Quitugua TN, Reed JT, Thomas CS. Assessing the continuum of event-based biosurveillance through an operational lens. Biosecur Bioterror. 2012;10(1):131–41. https://doi.org/10.1089/bsp.2011.0096.

    Article  PubMed  Google Scholar 

  50. McGrath JW. Biological impact of social disruption resulting from epidemic disease. Am J Phys Anthropol. 1991;84(4):407–19.

    Article  CAS  Google Scholar 

  51. Collmann J, Blake J, Bridgeland D, Kinne L, Yossinger NS, Dillon R, Martin S, Zou K. Measuring the potential for mass displacement in menacing contexts. J Refug Stud. 2016;29(3):273–94. https://doi.org/10.1093/jrs/few017.

    Article  Google Scholar 

  52. Briggs CL, Mantini-Briggs C. Stories in the time of cholera: racial profiling during a medical nightmare. Berkeley: University of California Press; 2004.

    Google Scholar 

  53. Crouch A. Japanese biological warfare in China: one family’s encounter. In the inventory of the Sheldon H. Harris Papers, Box 7, Folder 1. Hoover Institution Archives, Stanford University, Stanford, California. http://www.oac.cdlib.org/findaid/ark:/13030/kt8f59r20s/entire_text/. Accessed 12 Dec 2018

  54. Ammari T, Schoenebeck S. Understanding and supporting fathers and fatherhood on social media sites. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. New York: ACM; 2015. p. 1905–14.

    Google Scholar 

  55. De Choudhury M, Counts S, Horvitz E. Predicting postpartum changes in emotion and behavior via social media. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York: ACM; 2013. p. 3267–76.

    Chapter  Google Scholar 

  56. Lin YR. Assessing sentiment segregation in urban communities. In: Proceedings of the 2014 International Conference on Social Computing. SocialCom ‘14. New York: ACM; 2014. p. 9:1–8. https://doi.org/10.1145/2639968.2640066.

    Chapter  Google Scholar 

  57. Valdes JMD, Eisenstein J, De Choudhury M. Psychological effects of urban crime gleaned from social media. In: Proceedings of ICWSM; 2015. http://www.aaai.org/ocs/index.php/ICWSM/ICWSM15/paper/view/10563.

    Google Scholar 

  58. Harris J, Mansour R, Choucair B, Olson J, Nissen C, Bhatt J, Brown S. Health department use of social media to identify foodborne illness-Chicago, Illinois, 2013-2014. MMWR. 2014;63(32):681.

    PubMed  Google Scholar 

  59. Paul MJ, Dredze M. Drug extraction from the web: summarizing drug experiences with multi-dimensional topic models. In: Proceedings of HLT-NAACL. Atlanta: Association for Computational Linguistics; 2013. p. 168–78.

    Google Scholar 

  60. Ortony A. On making believable emotional agents believable. In: Trappl R, Petta P, Payr S, editors. Emotions in humans and artifacts. Cambridge, MA: MIT; 2003.

    Google Scholar 

  61. Pennebaker J. Using computer analyses to identify language style and aggressive intent: the secret life of function words. Dynamics of Asymmetric Conflict. 2011;4(2):92–102. https://doi.org/10.1080/17467586.2011.627932.

    Article  Google Scholar 

  62. Corley CD, Pullum LL, Hartley DM, Benedum C, Noonan C. Disease prediction models and operational readiness. PLoS One. 2014;9(3):e91989. https://doi.org/10.1371/journal.pone.0091989.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Schmidhuber J. Deep learning in neural networks: an overview. Neural Networks. 2015;61:85–117.

    Article  Google Scholar 

  64. Nguyen TH, Grishman, R. Event detection and domain adaptation with convolutional neural networks. 2015. Volume 2. Short Papers, 365.

    Google Scholar 

  65. Dong L, Wei F, Zhou M, Xu K. Question answering over freebase with multi-column convolutional neural networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, vol. Volume 1. Beijing: ACL; 2015. p. 260–9.

    Google Scholar 

  66. Lei T, Xin Y, Zhang Y, Barzilay R, Jaakkola T. Low-rank tensors for scoring dependency structures. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, vol. Volume 1; 2014. p. 1381–91.

    Google Scholar 

  67. Godin F, Vandersmissen B, De Neve W, Van de Walle R. Multimedia Lab@ ACL W-NUT NER shared task: named entity recognition for Twitter microposts using distributed word representations. ACL-IJCNLP. 2015;2015:146.

    Google Scholar 

  68. Wu Y, Schuster M, Chen Z, Le QV, Norouzi M, Macherey W, et al. Google’s neural machine translation system: bridging the gap between human and machine translation; 2016. arXiv preprint arXiv:1609.08144.

    Google Scholar 

  69. Tang D, Wei F, Yang N, Zhou M, Liu T, Qin B. Learning sentiment-specific word embedding for Twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, vol. 1; 2014. p. 1555–65.

    Google Scholar 

  70. Yin W, Schütze H. Convolutional neural network for paraphrase identification. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Denver: Association for Computational Linguistics; 2015. p. 901–11.

    Google Scholar 

  71. Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. In: Proceedings of the International Conference on Learning Representations (ICLR); 2015. arXiv preprint arXiv 1409.0473.

    Google Scholar 

  72. Cho K, Dzmitry Bahdanau D, Bougares F, Schwenk H, Bengio Y. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Proceedings of the Empirical Methods in Natural Language Processing (EMNLP) Conference; 2014. arXiv preprint arXiv:1406.1078.

    Google Scholar 

  73. Srivastava N, Mansimov E, Salakhutdinov R. Unsupervised learning of video representations using LSTMs; 2015. arXiv preprint arXiv:1502.04681.

    Google Scholar 

  74. Stoto MA. Biosurveillance capability requirements for the global health security agenda: lessons from the 2009 H1N1 pandemic. Biosecur Bioterror. 2014;12(5):225–30. https://doi.org/10.1089/bsp.2014.0030.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Burkom HS, Loschen WA, Mnatsakanyan ZR, Lombardo JS. Tradeoffs driving policy and research decisions in biosurveillance. Johns Hopkins APL Tech Dig. 2008;27(4):299–312.

    Google Scholar 

  76. Buehler JW, Berkelman RL, Hartley DM, Peters CJ. Syndromic surveillance and bioterrorism-related epidemics. Emerg Infect Dis. 2003;9(10):1197–204.

    Article  Google Scholar 

  77. Hartley DM. Space imaging and rift valley fever vectors. In: Laxminaryan R, Macauley MK, editors. The value of information: methodological frontiers and new applications for realizing social benefit. Springer; 2012.

    Google Scholar 

  78. Macauley MK, Laxminarayan R (2010) The value of information: methodological frontiers and new applications for realizing social benefit. Resources for the future conference summary. http://www.rff.org/research/publications/value-information-methodological-frontiers-and-new-applications-realizing. Accessed 22 Aug 2017.

  79. Laxminarayan R, Macauley MK. The value of information: methodological frontiers and new applications for realizing social benefit. Springer; 2012.

    Google Scholar 

  80. Chen H, Zeng D, Yan P. Infectious disease informatics: syndromic surveillance for public health and biodefense, integrated series in information systems 21. New York: Springer; 2010.

    Book  Google Scholar 

  81. Wong W-K, Moore AW. Classical time-series methods for biosurveillance. In: Wagner MM, Moore AW, Aryel RM, editors. Handbook of biosurveillance. MA: Elsevier Academic Press; 2006.

    Google Scholar 

  82. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature. 2009;457:1012–4. https://doi.org/10.1038/nature07634.

    Article  CAS  PubMed  Google Scholar 

  83. Pollett S, Boscardin WJ, Azziz-Baumgartner E, et al. Evaluating Google Flu trends in Latin America: important lessons for the next phase of digital disease detection. Clin Infect Dis. 2017;64:42–3. https://doi.org/10.1093/cid/ciw657.

    Article  Google Scholar 

  84. Santillana M. Perspectives on the future of internet search engines and biosurveillance systems. Clin Infect Dis. 2017;64:34–41. https://doi.org/10.1093/cid/ciw660.

    Article  Google Scholar 

  85. Lazer D, Kennedy R, King G, Vespignani A. The parable of Google Flu: traps in big data analysis. Science. 2014;343(6176):1203–5. https://doi.org/10.1126/science.1248506.

    Article  CAS  PubMed  Google Scholar 

  86. Bansal S, Chowell G, Simonsen L, Vespignani A, Viboud C. Big data for infectious disease surveillance and modeling. J Infect Dis. 2016;214(suppl_4):S375–9. https://doi.org/10.1093/infdis/jiw400.

    Article  PubMed  PubMed Central  Google Scholar 

  87. Santillana M, Nguyen AT, Dredze M, Paul MJ, Nsoesie EO, Brownstein JS. Combining search, social media, and traditional data sources to improve influenza surveillance. PLoS Comput Biol. 2015;11:e1004513. https://doi.org/10.1371/journal.pcbi.1004513.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Parker J, Yates A, Goharian N, Frieder O. Health-related hypothesis generation using social media data. Soc Net Anal Min. 2015;5(1) https://doi.org/10.1007/s13278-014-0239-8.

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Hartley, D.M., Mui, WL., Corley, C.D. (2019). The Role of Event-Based Biosurveillance in Biodefense. In: Singh, S., Kuhn, J. (eds) Defense Against Biological Attacks. Springer, Cham. https://doi.org/10.1007/978-3-030-03053-7_3

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