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Resilient Information Networks for Coordination of Foodborne Disease Outbreaks

Published online by Cambridge University Press:  17 April 2015

Liaquat Hossain*
Affiliation:
Department of Information Management, Division of Information and Technology Studies, Faculty of Education, University of Hong Kong, Hong Kong
Muhammad Rabiul Hassan
Affiliation:
Department of Electrical and Information Engineering, University of Sydney, Sydney, New South Wales, Australia
Rolf T. Wigand
Affiliation:
Departments of Information Science & Management, University of Arkansas, Little Rock, Arkansas
*
Correspondence and reprint requests to Liaquat Hossain, Department of Information Management, Division of Information and Technology Studies, Faculty of Education, University of Hong Kong, Pokfulam Road Hong Kong, Hong Kong (e-mail: lhossain@hku.hk).

Abstract

Foodborne disease outbreaks are increasingly being seen as a greater concern by public health authorities. It has also become a global research agenda to identify improved pathways to coordinating outbreak detection. Furthermore, a significant need exists for timely coordination of the detection of potential foodborne disease outbreaks to reduce the number of infected individuals and the overall impact on public health security. This study aimed to offer an effective approach for coordinating foodborne disease outbreaks. First, we identify current coordination processes, complexities, and challenges. We then explore social media surveillance strategies, usage, and the power of these strategies to influence decision-making. Finally, based on informal (social media) and formal (organizational) surveillance approaches, we propose a hybrid information network model for improving the coordination of outbreak detection. (Disaster Med Public Health Preparedness. 2015;9:186-198)

Type
Original Research
Copyright
Copyright © Society for Disaster Medicine and Public Health, Inc. 2015 

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References

1. Schlundt, J. New directions in foodborne disease prevention. Int J Food Microbiol. 2002;78:3-17.Google Scholar
2. Lynch, M, Painter, J, Woodruff, R, et al. Surveillance for Foodborne-Disease Outbreaks — United States, 1998-2002. MMWR CDC Surveil Sum. 2006;55(SS10):1-34. http://www.cdc.gov/mmwr/preview/mmwrhtml/ss5510a1.htm?_cid=ss. Accessed January 12, 2015.Google Scholar
4. Gould, LH, Walsh, KA, Vieira, AR, et al. Surveillance for foodborne disease outbreaks—United States, 1998-2008. MMWR CDC Surveill Summ . 2014;62(2):1-34.Google Scholar
5. Portal, DAD, Karras, DJ. Update on emerging infections: news from the Centers for Disease Control and Prevention. Ann Emerg Med. 2013;62(1):91-93.Google Scholar
6. Angulo, FJ, Kirk, MD, McKay, I, et al. Foodborne disease in Australia: the OzFoodNet experience. Clin Infect Dis . 2008;47(3):392-400.Google Scholar
7. Sobel, J, Griffin, PM, Slutsker, L, et al. Investigation of multistate foodborne disease outbreaks. Public Health Rep . 2002;117(1):8.Google Scholar
8. Uddin, S, Hossain, L. Disaster coordination preparedness of soft-target organizations. Disasters. 2011;35(3):623-638.Google Scholar
9. Malone, TW, Crowston, K. What is coordination theory and how can it help design cooperative work systems? In: Proceedings of the 1990 ACM Conference on Computer-supported Cooperative Work. ACM Press; 1990:357-370.Google Scholar
10. Li, C, Sun, A. Fine-grained location extraction from tweets with temporal awareness. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM Press; 2014:43-52.CrossRefGoogle Scholar
11. Kim, KD, Hossain, L, Uddin, S. Situated response and learning of distributed bushfire coordinating teams. J Homeland Secur Emerg Manage. 2013;10(1):95-111.Google Scholar
12. Marvin, HJP, et al. A working procedure for identifying emerging food safety issues at an early stage: implications for European and international risk management practices. Food Control. 2009;20:345-356.Google Scholar
13. Dato, V, Wagner, MM, Fapohunda, A. How outbreaks of infectious disease are detected: a review of surveillance systems and outbreaks. Public Health Rep. 2004;119(5):464-471.Google Scholar
14. Bdeir, F, Hossain, L, Crawford, J, et al. Inter-organisational coordination of H1N1 outbreak: data collection, and analyses of a pilot field study. J Decision Systems. 2014;23(2):151-166.Google Scholar
15. Woteki, CE, Kineman, BD. Challenges and approaches to reducing foodborne illness. Annu Rev Nutr . 2003;23(1):315-344.Google Scholar
16. Boxrud, D, Monson, T, Stiles, T, et al. The role, challenges, and support of Pulsenet Laboratories in detecting foodborne disease outbreaks. Public Health Rep. 2010;125(Suppl 2):57.Google Scholar
17. May, L, Chretien, JP, Pavlin, JA. Beyond traditional surveillance: applying syndromic surveillance to developing settings–opportunities and challenges. BMC Public Health. 2009;9(1):242.Google Scholar
18. Newkirk, RW, Bender, JB, Hedberg, CW. The potential capability of social media as a component of food safety and food terrorism surveillance systems. Foodborne Pathog Dis. 2012;9(2):120-124.Google Scholar
19. Ahmed, A, Scheepers, H, Stockdale, R. Social media research: a review of academic research and future research directions. Pacific Asia Journal of the Association for Information Systems. 2014;6(1):3.Google Scholar
20. Yates, D, Paquette, S. Emergency knowledge management and social media technologies: A case study. Int J Inf Manage. 2011;31:6-13.Google Scholar
21. Bosch, H, et al. ScatterBlogs2: real-time monitoring of microblog messages through user-guided filtering. IEEE Trans Vis Comput Graph. 2013;19(12):2022-2231.Google Scholar
22. MacEachren, A, et al. SensePlace2: GeoTwitter analytics support for situational awareness. In: Visual Analytics Science and Technology (VAST), 2011 IEEE Conference on. IEEE; 2011:181-190.Google Scholar
23. Gaspar, R, et al. Tweeting during food crises: a psychosocial analysis of threat coping expressions in Spain, during the 2011 European EHEC outbreak. Int J Hum Comput Stud. 2014;72:239-254.Google Scholar
24. Schmidt, CW. Using social media to predict and track disease outbreaks. Environ Health Perspect. 2012;120(1):a30-a33.Google Scholar
25. Corley, CD, Mikler, AR, Singh, KP, et al. Monitoring influenza trends through mining social media. Paper presented at: the International Conference on Bioinformatics and Computational Biology (BIOCOMP09); July 2009; Las Vegas, NV.Google Scholar
26. Paul, MJ, Dredze, M. You Are What You Tweet: Analyzing Twitter for Public Health. In: Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media ICWSM; 2011:265-272.Google Scholar
27. Chunara, R, Andrews, JR, Brownstein, JS. Social and news media enable estimation of epidemiological patterns early in the 2010 Haitian cholera outbreak. Am J Trop Med Hyg. 2012;86(1):39-45.Google Scholar
28. Bernardo, T, et al. Scoping review on search queries and social media for disease surveillance: a chronology of innovation. J Med Internet Res. 2013;15(7):e147.Google Scholar
29. Szomszor, M, Kostkova, P, De Quincey, E. #Swineflu: Twitter predicts swine flu outbreak in 2009. In: Electronic Healthcare. Springer Berlin Heidelberg; 2011:18-26.Google Scholar
30. Kalampokis, E, Tambouris, E, Tarabanis, K. Understanding the predictive power of social media. Internet Res. 2013;23(5):544-559.Google Scholar
31. James, KJ, Albrecht, JA, Litchfield, RE, et al. A summative evaluation of a food safety social marketing campaign “4-Day Throw-Away” using traditional and social media. J Food Sci Educ. 2013;12(3):48-55.Google Scholar
32. Kemble, SK, et al. Foodborne outbreak of group A Streptococcus pharyngitis associated with a high school dance team banquet—Minnesota, 2012. Clin Infect Dis. 2013;57(5):648-654.Google Scholar
33. Rutsaert, P, Regan, A, Pieniak, Z, et al. The use of social media in food risk and benefit communication. Trends Food Sci Technol. 2013;30:84-91.Google Scholar
34. Velsen, L, et al. Should health organizations use Web 2.0 media in times of an infectious disease crisis? An in-depth qualitative study of citizens’ information behavior during an EHEC outbreak. J Med Internet Res. 2012;14(6):e181.Google Scholar
35. Krause, G, et al. SurvNet electronic surveillance system for infectious disease outbreaks, Germany. Emerg Infect Dis. 2007;13(10):1548-1555.Google Scholar
36. Freifeld, CC, Mandl, KD, Reis, BY, et al. HealthMap: global infectious disease monitoring through automated classification and visualization of Internet reports. J Am Med Inform Assoc. 2008;15(2):150-157.Google Scholar
37. Louis, CS, Zorlu, G. Can Twitter predict disease outbreaks? BMJ. 2012;344:e2353.Google Scholar
38. Corley, CD, Cook, DJ, Mikler, AR, et al. Text and structural data mining of influenza mentions in web and social media. Int J Environ Res Public Health. 2010;7(2):596-615.Google Scholar
39. Rogstadius, J, et al. CrisisTracker: crowdsourced social media curation for disaster awareness. IBM J. Res. Dev. 2013;57(5).Google Scholar
40. Signorini, A, Segre, AM, Polgreen, PM. The use of Twitter to track levels of disease activity and public concern in the U.S. during the influenza A H1N1 pandemic. PLoS ONE. 2011;6(5):e19467.Google Scholar