Inter-organisational coordination of H1N1 outbreak: data collection, and analyses of a pilot field study

Bdeir, Fadl; Hossain, Liaquat; Crawford, John W.; Carlsson, Sven

doi:10.1080/12460125.2014.896122

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…In 2014, Cui et al [20] demonstrated the public attention model for epidemics like H1N1 using spatial-temporal patterns. In 2014, Bdeir et al [21] discovered some of hidden social networks between different organizations during H1N1 epidemics. They also suggested that relevant and timely exchange of information between different organizations can result in better prevention of epidemics.…”

Section: Ict and Mathematical Models In H1n1 Epidemicmentioning

confidence: 99%

Smart monitoring and controlling of Pandemic Influenza A (H1N1) using Social Network Analysis and cloud computing

Sandhu

Gill

Sood

2016

Journal of Computational Science

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a b s t r a c t H1N1 is an infectious virus which, when spread affects a large volume of the population. It is an airborne disease that spreads easily and has a high death rate. Development of healthcare support systems using cloud computing is emerging as an effective solution with the benefits of better quality of service, reduced costs and flexibility. In this paper, an effective cloud computing architecture is proposed which predicts H1N1 infected patients and provides preventions to control infection rate. It consists of four processing components along with secure cloud storage medical database. The random decision tree is used to initially assess the infection in any patient depending on his/her symptoms. Social Network Analysis (SNA) is used to present the state of the outbreak. The proposed architecture is tested on synthetic data generated for two million users. The system provided 94% accuracy for the classification and around 81% of the resource utilization on Amazon EC2 cloud. The key point of the paper is the use of SNA graphs to calculate role of an infected user in spreading the outbreak known as Outbreak Role Index (ORI). It will help government agencies and healthcare departments to present, analyze and prevent outbreak effectively.

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Section: Ict and Mathematical Models In H1n1 Epidemicmentioning

confidence: 99%

Smart monitoring and controlling of Pandemic Influenza A (H1N1) using Social Network Analysis and cloud computing

Sandhu

Gill

Sood

2016

Journal of Computational Science

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“…Bdeir et al 14 studied inter-organizational coordination during disease outbreaks and found that the main problem during disasters or emergency situations is that the coordination is not well controlled. Their study further argued that complexity arises when multiple agents interact with each other in a dynamic environment and that coordination is deemed necessary when a task needs to be completed through interdependent activities 14 . Similar situations occur in health organizations, which face coordination complexity in disease disaster because every disease outbreak seems unique by nature or symptoms or level of complexity.…”

Section: Coordination Of Detection Of Foodborne Disease Outbreaksmentioning

confidence: 99%

“…Thus, demand is high for an emergency response system to reduce the complexity in coordination. The question for researchers, practitioners, and policy makers is whether the standard top-down approach with its “hierarchal commands and control structure” successfully deals with high levels of complexity during disease outbreaks, 14 particularly if the outbreaks are caused by food contamination, or whether a new or complementary method (bottom-up approach) of coordinating complexity would have a much better success rate. This question remains almost unanswered in the literature.…”

Section: Coordination Of Detection Of Foodborne Disease Outbreaksmentioning

confidence: 99%

Resilient Information Networks for Coordination of Foodborne Disease Outbreaks

Hossain

Hassan

Wigand

2015

Disaster med. public health prep.

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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.

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“…These roles and responsibilities most importantly apply to collaborative structures and coordination roles in the organizational network [8–11]. Second, in line with the need for clarity in collaborative structures and coordination roles, it is important to identify those network participants with the potential to play a brokerage or mediating role in the network [12–15]. Network brokers have the potential to create an inclusive collaborative network and can facilitate the coordination in a network.…”

Section: Introductionmentioning

confidence: 99%

Collaborative emergency preparedness and response to cross-institutional outbreaks of multidrug-resistant organisms: a scenario-based approach in two regions of the Netherlands

et al. 2019

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BackgroundThe likelihood of large-scale outbreaks of multidrug-resistant organisms (MDRO) is growing. MDRO outbreaks can affect a wide range of healthcare institutions. Control of such outbreaks requires structured collaboration between professionals from all involved healthcare institutions, but guidelines for cross-institutional procedures are, however, often missing. Literature indicates that such multi-actor collaboration is most promising when effective network brokers are present, and when the collaborative actors have clarity about the different roles and responsibilities in the outbreak response network, including collaborative structures and coordination roles. Studying these factors in an imaginary MDRO outbreak scenario, we gained insights into the expectations that health professionals in the Netherlands have in regard to the procedures required to best respond to any future cross-institutional MDRO outbreaks.MethodsFor exploration purpose, a focus group discussion with ten healthcare professionals was held. Subsequently, an online-survey was conducted among 56 healthcare professionals in two Dutch regions. The survey data was analysed using social network analyses (clique analysis and centrality analysis), which provided insights into the collaborative structures and potential brokers in the outbreak response networks. Additionally, respondents were asked which healthcare institutions and which professions they would prefer as coordinating actors in the collaborative network.ResultsOur results show a relatively high level of perceived clarity about the roles and responsibilities that healthcare professionals have during a joint outbreak response. The regional outbreak response networks which were studied appeared inclusive and integrated, with many overlapping groups of fully-connected healthcare actors. Social network analyses resulted in the identification of several central actors from different healthcare institutions with the potential to take on a brokerage role in the collaboration. Actors in the outbreak response networks also showed to prefer several healthcare professionals to take on the coordination roles.ConclusionExpected collaborative structures during an imaginary regional MDRO outbreak response are relatively dense and integrated. In regard to the coordination of an MDRO outbreak response, based on both the network analysis results and the preferred coordination roles, our findings support a governance structure with several healthcare institutions involved in responding to future cross-institutional MDRO outbreaks.Electronic supplementary materialThe online version of this article (10.1186/s12889-018-6376-7) contains supplementary material, which is available to authorized users.

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Inter-organisational coordination of H1N1 outbreak: data collection, and analyses of a pilot field study

Cited by 4 publications

References 11 publications

Smart monitoring and controlling of Pandemic Influenza A (H1N1) using Social Network Analysis and cloud computing

Smart monitoring and controlling of Pandemic Influenza A (H1N1) using Social Network Analysis and cloud computing

Resilient Information Networks for Coordination of Foodborne Disease Outbreaks

Collaborative emergency preparedness and response to cross-institutional outbreaks of multidrug-resistant organisms: a scenario-based approach in two regions of the Netherlands

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