Data-driven surveillance: Effective collection, integration and interpretation of data to support decision-making in a One Health context

Download all references from this literature-based review as a RIS file or as a Bibtex file (remove the “.txt” extension after downloading).

References cited in the paper:

1. Simonsen L, Gog JR, Olson D, Viboud C. Infectious Disease Surveillance in the Big Data Era: Towards Faster and Locally Relevant Systems. J Infect Dis. 2016;214: S380–S385. doi:10.1093/infdis/jiw376
2. Leyens L, Reumann M, Malats N, Brand A. Use of big data for drug development and for public and personal health and care. Genet Epidemiol. 2017;41: 51–60. doi:10.1002/gepi.22012
3. Salathé M. Digital pharmacovigilance and disease surveillance: Combining traditional and big-data systems for better public health. J Infect Dis. 2016;214. doi:10.1093/infdis/jiw281
4. Asokan GV, Asokan V. Leveraging “big data” to enhance the effectiveness of “one health” in an era of health informatics. J Epidemiol Glob Health. 2015;5: 311–314. doi:10.1016/j.jegh.2015.02.001
5. Hoffman S, Podgurski A. Big Bad Data: Law, Public Health, and Biomedical Databases. J Law, Med Ethics. 2013;41: 56–60. doi:10.1111/jlme.12040
6. Vayena E, Salathé M, Madoff LC, Brownstein JS. Ethical Challenges of Big Data in Public Health. PLoS Comput Biol. 2015;11. doi:10.1371/journal.pcbi.1003904
7. Toh S, Platt R. Is Size the Next Big Thing in Epidemiology? Epidemiology. 2013;24: 349–351. doi:10.1097/EDE.0b013e31828ac65e
8. Iwashyna TJ, Liu V. What’s so different about big data?: A primer for clinicians trained to think epidemiologically. Ann Am Thorac Soc. 2014;11. doi:10.1513/AnnalsATS.201405-185AS
9. Gates MC, Holmstrom LK, Biggers KE, Beckham TR. Integrating novel data streams to support biosurveillance in commercial livestock production systems in developed countries: challenges and opportunities. Front Public Heal. 2015;3: 74. doi:10.3389/fpubh.2015.00074
10. VanderWaal K, Morrison RB, Neuhauser C, Vilalta C, Perez AM. Translating Big Data into Smart Data for Veterinary Epidemiology. Front Vet Sci. Frontiers; 2016;4: 110. doi:10.3389/FVETS.2017.00110
11. Marvin HJP, Janssen EM, Bouzembrak Y, Hendriksen PJM, Staats M. Big data in food safety: An overview. Crit Rev Food Sci Nutr. Taylor & Francis; 2017;57: 2286–2295. doi:10.1080/10408398.2016.1257481
12. McCue ME, McCoy AM. The Scope of Big Data in One Medicine: Unprecedented Opportunities and Challenges. Front Vet Sci. 2017;4: 1–23. doi:10.3389/fvets.2017.00194
13. Stevens KB, Pfeiffer DU. Sources of spatial animal and human health data: Casting the net wide to deal more effectively with increasingly complex disease problems. Spat Spatiotemporal Epidemiol. Elsevier Ltd; 2015;13: 15–29. doi:10.1016/j.sste.2015.04.003
14. Nuti S V., Wayda B, Ranasinghe I, Wang S, Dreyer RP, Chen SI, et al. The use of google trends in health care research: A systematic review. PLoS One. 2014;9. doi:10.1371/journal.pone.0109583
15. Kamel Boulos MN, Resch B, Crowley DN, Breslin JG, Sohn G, Burtner R, et al. Crowdsourcing, citizen sensing and sensor web technologies for public and environmental health surveillance and crisis management: trends, OGC standards and application examples. Int J Health Geogr. BioMed Central; 2011;10: 67. doi:10.1186/1476-072X-10-67
16. Han BA, Drake JM. Future directions in analytics for infectious disease intelligence: Toward an integrated warning system for emerging pathogens. EMBO Rep. 2016;17: e201642534. doi:10.15252/embr.201642534
17. Bansal S, Chowell G, Simonsen L, Vespignani A, Viboud C. Big data for infectious disease surveillance and modeling. J Infect Dis. 2016;214. doi:10.1093/infdis/jiw400
18. Definition of Interoperability. 2nd editio. HIMSS Dictionary of Healthcare Information Technology Terms, Acronyms and Organizations. 2010. p. 190.
19. Noy N. Semantic integration: a survey of ontology-based approaches. SIGMOD Rec. 2004;33: 65–70. doi:doi: 10.1145/1041410.1041421
20. Noy NF, McGuinness DL. Ontology Development 101: A Guide to Creating Your First Ontology [Internet]. Stanford Knowledge Systems Laboratory. 2001. doi:10.1016/j.artmed.2004.01.014
21. Khoury MJMJMJ, Ioannidis JPAJPA. Medicine. Big data meets public health. Science. NIH Public Access; 2014;346: 1054–5. doi:10.1126/science.aaa2709
22. Centers for Disease Control and Prevention (CDC). Syndromic surveillance. Reports from a national conference, 2003. Morb Mortal Wkly Rep. 2004;53 Suppl: 1–264.
23. Dórea FC, Vial F. Animal health syndromic surveillance: a systematic literature review of the progress in the last 5 years (2011–2016). Vet Med Reports. 2016;7: 157–169.
24. Dupuy C, Bronner A, Watson E, Wuyckhuise-Sjouke L, Reist M, Fouillet A, et al. Inventory of veterinary syndromic surveillance initiatives in Europe (Triple-S project): current situation and perspectives. Prev Vet Med. Elsevier B.V.; 2013;111: 220–229.
25. Huang T, Lan L, Fang X, An P, Min J, Wang F. Promises and Challenges of Big Data Computing in Health Sciences. Big Data Res. 2015;2: 2–11. doi:10.1016/j.bdr.2015.02.002
26. Wang W, Krishnan E. Big data and clinicians: a review on the state of the science. JMIR Med informatics. JMIR Medical Informatics; 2014;2: e1. doi:10.2196/medinform.2913
27. Brownson RC, Samet JM, Bensyl DM. Applied epidemiology and public health: are we training the future generations appropriately? Ann Epidemiol. 2017;27. doi:10.1016/j.annepidem.2016.12.002
28. Althouse BM, Scarpino S V., Meyers LA, Ayers JW, Bargsten M, Baumbach J, et al. Enhancing disease surveillance with novel data streams: challenges and opportunities. EPJ Data Sci. Althouse et al.; 2015;4: 1–8. doi:10.1140/epjds/s13688-015-0054-0
29. Kostkova P. A roadmap to integrated digital public health surveillance: The vision and the challenges. WWW 2013 Companion – Proceedings of the 22nd International Conference on World Wide Web. 2013.