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Big Data and Artificial Intelligence for Biodefense: A Genomic-Based Approach for Averting Technological Surprise

  • Willy A. Valdivia-GrandaEmail author
Chapter

Abstract

Emerging technologies that could transform the defensive or offensive edge of a nation are protected by secrecy, denial, and deception. These disruptive capability gains do not follow conventional paths nor an established pattern of adoption. Averting dual-use emerging technologies could radically affect strategic and tactical deterrence of the USA against state and non-state actors pursuing biological weapons. The convergence of artificial intelligence, genomics, Big Data, and nanotechnology is at the center of the biodefense discussion today. Different DNA sequencing and nanosensor technologies capture trillions of bytes of information, which coupled with artificial intelligence could play a key role to avert and counter known and unknown pathogens. Despite the technological progress, biodefense stakeholders still lack access to an analytical enterprise that improves near real-time pathogen outbreak awareness at the global scale and can provide strategic and tactical risk assessment to minimize the impact of biothreats on human, animal, and plants. This chapter discusses how Big Data and artificial intelligence can be used to process genomic information and unstructured qualitative and quantitative datasets to support human-machine teaming for sense, shape, shield, and sustain in multidomain biodefense environment. The implications of this framework are also summarized.

Keywords

Artificial intelligence Big Data Genomics Biodefense Biothreats 

Notes

Acknowledgments

I am grateful to Drs. Juergen Richt and Romelito Lapitan for the insightful discussions and suggestions that improved this manuscript. The research associated with this book chapter was partially funded by the Department of Homeland Security Center of Excellence for Emerging and Zoonotic Animal Disease (CEEZAD), the Kansas Bioscience Authority, the Defense Threat Reduction Agency (DTRA) Biological Engagement Program, and the Department of Homeland Customs and Border Protection Office Ag/Bio-Terror Countermeasures (ABTC).

References

  1. 1.
    Anastasio M. Seven defense priorities for the new administration. 2017.Google Scholar
  2. 2.
    Martellini M. Cyber and chemical, biological, radiological, nuclear, explosives challenges. New York, NY: Springer; 2017.CrossRefGoogle Scholar
  3. 3.
    Gonzalez JP, Souris M, Valdivia-Granda W. Global spread of hemorrhagic fever viruses: predicting pandemics. Methods Mol Biol. 2018;1604:3–31.CrossRefGoogle Scholar
  4. 4.
    United States. Government Accountability Office. High-containment laboratories national strategy for oversight is needed: report to congressional requesters. Washington, DC: U.S. Govt. Accountability Office; 2009.Google Scholar
  5. 5.
    Hottes AK, Rusek B, Sharples FE, National Academy of Sciences (U.S.), Committee on International Security and Arms Control, National Academy of Sciences (U.S.), Committee on Anticipating Biosecurity Challenges of the Global Expansion of High-Containment Biological Laboratories. Biosecurity challenges of the global expansion of high-containment biological laboratories summary of a workshop. Washington, DC: National Academies Press; 2012. p. 204.Google Scholar
  6. 6.
    Laszlo AH, Derrington IM, Ross BC, Brinkerhoff H, Adey A, Nova IC, Craig JM, Langford KW, Samson JM, Daza R, et al. Decoding long nanopore sequencing reads of natural DNA. Nat Biotechnol. 2014;32(8):829–33.CrossRefGoogle Scholar
  7. 7.
    Valdivia-Granda WA. Biosurveillance enterprise for operational awareness, a genomic-based approach for tracking pathogen virulence. Virulence. 2013;4(8):745–51.CrossRefGoogle Scholar
  8. 8.
    Valdivia-Granda WA. Bioinformatics for biodefense: challenges and opportunities. Biosecur Bioterror. 2010;8(1):69–77.CrossRefGoogle Scholar
  9. 9.
    Cropley D. The dark side of creativity. New York: Cambridge University Press; 2010.CrossRefGoogle Scholar
  10. 10.
    United States, Congress, House, Committee on Armed Services Committee on Naval Affairs Committee on National Security Committee on Military A. The 2014 Quadrennial Defense Review: Committee on Armed Services, House of Representatives, One Hundred Thirteenth Congress, second session, hearing held April 3, 2014. Washington: U.S. Government Printing Office; 2014. Washington, DC: For sale by the Superintendent of Documents, U.S. Government Printing Office; 2014Google Scholar
  11. 11.
    Seven defense priorities for the new administration: report of the defense science board. Washington, DC: Defense Science Board; 2016.Google Scholar
  12. 12.
    Carlson R. Biodefense net assessment: causes and consequences of bioeconomic proliferation; 2012.Google Scholar
  13. 13.
    Leehy AG, Wildstein JJ, Schiffer M, United States. Department of Defense, Office of the Secretary of Defense. Military and security developments in China. Hauppauge, NY: Nova Science Publishers; 2012.Google Scholar
  14. 14.
    Vogel KM: Phantom menace or looming danger?: A new framework for assessing bioweapons threats. Baltimore: Johns Hopkins University Press; 2013.Google Scholar
  15. 15.
    Kouzminov A. Biological espionage: special operations of the Soviet and Russian foreign intelligence services in the west. New Delhi: Manas Publications; 2006.Google Scholar
  16. 16.
    Domaradskij IV, Orent LW. Achievements of the soviet biological weapons programme and implications for the future. Rev Sci Tech. 2006;25(1):153–61.CrossRefGoogle Scholar
  17. 17.
    United States. Defense Science Board, United States. Office of the Under Secretary of Defense for Acquisition Technology and Logistics. Report of the defense science board 2008 summer study on capability surprise. Washington, DC: Office of the Under Secretary of Defense for Acquisition, Technology, and Logistics; 2009.Google Scholar
  18. 18.
    Colf LA. Preparing for nontraditional biothreats. Health Secur. 2016;14(1):7–12.CrossRefGoogle Scholar
  19. 19.
    Fan W, Bifet A. Mining big data: current status, and forecast to the future. SIGKDD Explor Newsl. 2013;14(2):1–5.CrossRefGoogle Scholar
  20. 20.
    Aggarwal CC. Outlier ensembles: position paper. SIGKDD Explor Newsl. 2013;14(2):49–58.CrossRefGoogle Scholar
  21. 21.
    Fan J, Liu H. Statistical analysis of big data on pharmacogenomics. Adv Drug Deliv Rev. 2013;65(7):987–1000.CrossRefGoogle Scholar
  22. 22.
    Cohen R, Ruths D. Classifying political orientation on Twitter: it’s not easy! Montreal, QC: McGill University; 2013.Google Scholar
  23. 23.
    Pollett S, Althouse BM, Forshey B, Rutherford GW, Jarman RG. Internet-based biosurveillance methods for vector-borne diseases: are they novel public health tools or just novelties? PLoS Negl Trop Dis. 2017;11(11):e0005871.CrossRefGoogle Scholar
  24. 24.
    Bahk CY, Scales DA, Mekaru SR, Brownstein JS, Freifeld CC. Comparing timeliness, content, and disease severity of formal and informal source outbreak reporting. BMC Infect Dis. 2015;15:135.CrossRefGoogle Scholar
  25. 25.
    Chowell G, Cleaton JM, Viboud C. Elucidating transmission patterns from internet reports: Ebola and Middle East respiratory syndrome as case studies. J Infect Dis. 2016;214(suppl_4):S421–6.CrossRefGoogle Scholar
  26. 26.
    Cleaton JM, Viboud C, Simonsen L, Hurtado AM, Chowell G. Characterizing Ebola transmission patterns based on internet news reports. Clin Infect Dis. 2016;62(1):24–31.CrossRefGoogle Scholar
  27. 27.
    Butler D. When Google got flu wrong. Nature. 2013;494(7436):155–6.CrossRefGoogle Scholar
  28. 28.
    Lyon A, Nunn M, Grossel G, Burgman M. Comparison of web-based biosecurity intelligence systems: BioCaster, EpiSPIDER and HealthMap. Transbound Emerg Dis. 2012;59(3):223–32.CrossRefGoogle Scholar
  29. 29.
    Barboza P, Vaillant L, Le Strat Y, Hartley DM, Nelson NP, Mawudeku A, Madoff LC, Linge JP, Collier N, Brownstein JS, et al. Factors influencing performance of internet-based biosurveillance systems used in epidemic intelligence for early detection of infectious diseases outbreaks. PLoS One. 2014;9(3):e90536.CrossRefGoogle Scholar
  30. 30.
    Nsoesie EO, Brownstein JS, Ramakrishnan N, Marathe MV. A systematic review of studies on forecasting the dynamics of influenza outbreaks. Influenza Other Respir Viruses. 2013;8(3):309–16.CrossRefGoogle Scholar
  31. 31.
    Smolinski MS, Crawley AW, Olsen JM. Finding outbreaks faster. Health Secur. 2017;15(2):215–20.CrossRefGoogle Scholar
  32. 32.
    Patel D, Olson S, Institute of Medicine (U.S.), Planning Committee on Information-Sharing Models and Guidelines for Collaboration: Applications to an Integrated One Health Biosurveillance Strategy, Institute of Medicine (U.S.), Board on Health Sciences Policy. Information sharing and collaboration: applications to integrated biosurveillance: workshop summary. Washington, DC: National Academies Press; 2012.Google Scholar
  33. 33.
    McElroy K, Thomas T, Luciani F. Deep sequencing of evolving pathogen populations: applications, errors, and bioinformatic solutions. Microb Inform Exp. 2014;4(1):1.CrossRefGoogle Scholar
  34. 34.
    Velusamy V, Arshak K, Korostynska O, Oliwa K, Adley C. An overview of foodborne pathogen detection: in the perspective of biosensors. Biotechnol Adv. 2010;28(2):232–54.CrossRefGoogle Scholar
  35. 35.
    Goldstone JA, Bates RH, Epstein DL, Gurr TR, Lustik MB, Marshall MG, Ulfelder J, Woodward M. A global model for forecasting political instability. Am J Polit Sci. 2010;54(1):190–208.CrossRefGoogle Scholar
  36. 36.
    National Research Council (U.S.), Committee on Scientific Milestones for the Development of a Gene Sequence-Based Classification System for the Oversight of Select Agents, National Research Council (U.S.), Board on Life Sciences. Sequence-based classification of select agents: a brighter line. Washington, DC: National Academies Press; 2010.Google Scholar
  37. 37.
    Rotz LD, Khan AS, Lillibridge SR, Ostroff SM, Hughes JM. Public health assessment of potential biological terrorism agents. Emerg Infect Dis. 2002;8(2):225–30.CrossRefGoogle Scholar
  38. 38.
    Ciliberto C, Herbster M, Ialongo AD, Pontil M, Rocchetto A, Severini S, Wossnig L. Quantum machine learning: a classical perspective. Proc Math Phys Eng Sci. 2018;474(2209):20170551.CrossRefGoogle Scholar
  39. 39.
    Dunjko V, Briegel HJ. Machine learning & artificial intelligence in the quantum domain: a review of recent progress. Rep Prog Phys. 2018;81(7):074001.CrossRefGoogle Scholar
  40. 40.
    Kanter JM, Veeramachaneni K. Deep feature synthesis: towards automating data science endeavors. In: 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA); 2015. p. 1–10.Google Scholar
  41. 41.
    Williams AM, Liu Y, Regner KR, Jotterand F, Liu P, Liang M. Artificial intelligence, physiological genomics, and precision medicine. Physiol Genomics. 2018;50(4):237–43.CrossRefGoogle Scholar
  42. 42.
    Murdoch TB, Detsky AS. The inevitable application of big data to health care. JAMA. 2013;309(13):1351–2.CrossRefGoogle Scholar
  43. 43.
    National Research Council (U.S.). Committee for Science and Technology Challenges to U.S. National Security Interests. Report of a workshop on big data. Washington, DC: National Academies Press; 2012.Google Scholar
  44. 44.
    Gill KS. Uncommon voices of AI. AI & Soc. 2017;32(4):475–82.CrossRefGoogle Scholar
  45. 45.
    Swearingen T, Drevo W, Cyphers B, Cuesta-Infante A, Ross A, Veeramachaneni K. ATM: a distributed, collaborative, scalable system for automated machine learning. In: IEEE International Conference on Big Data (Big Data), 11–14 December 2017; 2017. p. 151–62.CrossRefGoogle Scholar
  46. 46.
    Fan L, Wu W, Lu Z, Xu W, Du D-Z. Influence diffusion, community detection, and link prediction in social network analysis. In: Sorokin A, Pardalos PM, editors. Dynamics of information systems: algorithmic approaches, vol. 51. New York: Springer; 2013. p. 305–25.CrossRefGoogle Scholar
  47. 47.
    Garrity GM, Field D, Kyrpides N, Hirschman L, Sansone SA, Angiuoli S, Cole JR, Glockner FO, Kolker E, Kowalchuk G, et al. Toward a standards-compliant genomic and metagenomic publication record. Omics. 2008;12(2):157–60.CrossRefGoogle Scholar
  48. 48.
    Ladner JT, Beitzel B, Chain PS, Davenport MG, Donaldson EF, Frieman M, Kugelman JR, Kuhn JH, O’Rear J, Sabeti PC, et al. Standards for sequencing viral genomes in the era of high-throughput sequencing. MBio. 2014;5(3):e01360–14.CrossRefGoogle Scholar
  49. 49.
    Ziegler BE: Methods for bibliometric analysis of research: renewable energy case study. Massachusetts Institute of Technology; Cambridge, MA 2009.Google Scholar
  50. 50.
    Sun Y, Han J. Mining heterogeneous information networks: a structural analysis approach. SIGKDD Explor Newsl. 2013;14(2):20–8.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Orion Integrated Biosciences, Inc.ManhattanUSA

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