Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-29T09:11:26.119Z Has data issue: false hasContentIssue false
  • English
  • Français

Networks of Preparedness and Response During Australian H1N1 Outbreak

Published online by Cambridge University Press:  17 April 2015

Liaquat Hossain Open the ORCID record for Liaquat Hossain [Opens in a new window] ,
Fadl Bdeir ,
John W Crawford  and
Rolf T. Wigand
Liaquat Hossain*
Affiliation:
Division of Information and Technology Studies, University of Hong Kong Complex Systems, School of Civil Engineering Faculty of Engineering and IT, University of Sydney, Australia
Fadl Bdeir
Affiliation:
Complex Systems, School of Civil Engineering Faculty of Engineering and IT, University of Sydney, Australia
John W Crawford
Affiliation:
Sustainable Systems, Rothamsted Research, Hertfordshire, United Kingdom
Rolf T. Wigand
Affiliation:
Departments of Information Science and Management, University of Arkansas, Little Rock, Arksansas
*
Correspondence and reprint requests to Liaquat Hossain, PhD, Division of Information and Technology Studies, University of Hong Kong, Pokfulam, Hong Kong (e-mail: [email protected]).
Get access Rights & Permissions [Opens in a new window]

Abstract

Objective

New theoretical and practical approaches were used to determine the outcome of complex interorganizational networks during the 2009 H1N1 outbreak in Australia.

Methods

Seventy health professionals from different skill sets and organizational positions who participated in the 2009 swine influenza H1N1 outbreak in Australia were surveyed. Interviews were designed to collect both qualitative and quantitative data to build a comprehensive and in-depth understanding of the dynamics of interorganizational networks that evolve during the coordinated response to the H1N1 outbreak. Three main components of network theory, ie, degree centrality, connectedness, and tie strength, were used to construct a performance model for assessing networks of preparedness and response.

Results

We observed that increasing communication frequency and diversifying the tiers of the interorganizational links enhanced the overall network’s performance in the case of formal coordination. Network measures such as centrality, connectedness, and tie strength were relevant and resulted in improving the entire network’s performance during the outbreak.

Conclusion

In the context of a disease outbreak in a complex environment and a large geographical area, this investigation has provided a new perspective for understanding how the structure of a collaborative network of personnel affects the performance of the overall network. (Disaster Med Public Health Preparedness. 2015;9:155-165)

Keywords

networkscoordinationpreparednessresponsedisease outbreaks
Type
Original Research
Information
Disaster Medicine and Public Health Preparedness , Volume 9 , Issue 2 , April 2015 , pp. 155 - 165
Copyright
Copyright © Society for Disaster Medicine and Public Health, Inc. 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1. Girard, MP, Tam, JS, Assossou, OM, Kieny, MP. The 2009 A (H1N1) influenza virus pandemic: a review. Vaccine. 2010;28(31):4895-4902.Google Scholar
2. Strategic Advisory Group of Experts on Immunization—report of the extraordinary meeting on the influenza A (H1N1) 2009 pandemic. 7 July 2009. Wkly Epidemiol Rec. 2009;84(30):301-304.Google Scholar
3. Presanis, AM, De Angelis, D, New York City Swine Flu Investigation Team, et al. The severity of pandemic H1N1 influenza in the United States, from April to July 2009: a Bayesian analysis. PLoS Med. 2009; 6(12):e1000207.CrossRefGoogle ScholarPubMed
4. Hannoun, C. La petite histoire du virus grippal H1N1: de 1918 à 2009. Bull Soc Fr Microbiol. 2010;25:9-20.Google Scholar
5. European Centre for Disease Prevention and Control. ECDC Daily Update—2009 influenza A (H1N1) pandemic; January 18, 2010. http://reliefweb.int/sites/reliefweb.int/files/resources/25EDA177CA846637C12576AF0036D832-Full_Report.pdf. Accessed June 7, 2013.Google Scholar
6. Baber, CJ, Cross, PJ, Smith, P, Robinson, D. Supporting implicit coordination between distributed teams in disaster management. In: Mobile Response. Berlin, Germany: Springer; 2007: 39-50.Google Scholar
7. Tschoegl, L, Below, R, Guha-Sapir, D. An Analytical Review of Selected Data Sets on Natural Disasters and Impacts. Brussels, Belgium: Centre for Research on the Epidemiology of Disasters; March 2006.Google Scholar
8. Hitchcock, PA, Chamberlain, M, Van Wagoner, TV, Inglesby, TV, O'Toole, T. Challenges to global surveillance and response to infectious disease outbreaks of international importance. Biosecur Bioterror. 2007;5(3):206-227.CrossRefGoogle ScholarPubMed
9. Freeman, LC. Centrality in social networks conceptual clarification. Soc Networks. 1978;1(3):215-239.Google Scholar
10. Lin, Z. The dynamics of inter-organizational ties during crises: empirical evidence and computational analysis. Simulation Modelling Practice Theory. 2002;10(5-7):387-415.Google Scholar
11. Burt, RS. Positions in networks. Social. Forces. 1976;55:93-122.CrossRefGoogle Scholar
12. Rosenthal, E. Social networks and team performance. Team Performance Manage. 1997;3(4):288-294.Google Scholar
13. Bavelas, A. Communication patterns in task-oriented groups. J Acoustic Soc Am. 1950;22:725-730.Google Scholar
14. Leavitt, HJ. Some effects of certain communication patterns on group performance. J Abnorm Psychol. 1951;46(1):38-50.Google ScholarPubMed
15. Mulder, M. Group-structure and group-performance. Acta Psychol. 1959;16:356-402.Google Scholar
16. Mohanna, A, Argyle, M. A cross-cultural study of structured groups with unpopular central members. J Abnorm Soc Psychol. 1960;60(1):139-140.Google Scholar
17. Cohen, AM. Changing small-group communication networks. Admin Sci Q. 1962;6(4):443-462.Google Scholar
18. Guetzkow, H, Dill, WR. Factors in the organizational development of task-oriented groups. Sociometry. 1957;20(3):175-204.Google Scholar
19. Shaw, ME. Some effects of problem complexity upon problem solution efficiency in different communication nets. J Exp Psychol. 1954;48(3):211-217.Google Scholar
20. Mulder, M. Communication structure, decision structure and group performance. Sociometry. 1960;23(1):1-14.Google Scholar
21. Freeman, L. Centrality in social networks: conceptual clarification. Social Networks. 1979;1:215-239.Google Scholar
22. Valente, TW. Social Networks and Health: Models, Methods, and Applications. New York, New York: Oxford University Press; 2010.CrossRefGoogle Scholar
23. Granovetter, MS. The strength of weak ties. Am J Sociol. 1973;78(6):1360.CrossRefGoogle Scholar
24. Luke, DA, Harris, JK. Network analysis in public health: history, methods, and applications. Ann Rev Public Health. 2007;28(1):69-93.CrossRefGoogle ScholarPubMed