Designing Adaptive Systems for Disaster Mitigation and Response:

Comfort, Louise K.; Oh, Namkyung; Ertan, Güneş; Scheinert, Steve

doi:10.2307/j.ctt5hjq0c.7

Cited by 29 publications

(39 citation statements)

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“…Big data analytics privilege large sample sizes because they are understood to reveal patterns of correlation, invisible to the naked eye, from the digital evaluation of unrelated data scraps (Comfort et al, 2010). This algorithmic logic now appears in UK counter-radicalisation policy, where the NHS is specifically selected as a counter-terrorism partner due to its preeminent contact with the public.…”

Section: The Algorithmic Shift In British Counter-radicalisation Policymentioning

confidence: 99%

Algorithmic autoimmunity in the NHS: Radicalisation and the clinic

Heath-Kelly

2016

Security Dialogue

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This article explores the extension of counter-radicalisation practice into the National Health Service (NHS). In the 2011 reformulation of the UK Prevent strategy, the NHS became a key sector for the identification and suppression of 'radicalisation'. Optometrists, dentists, doctors and nurses have been incorporated into counter-terrorism and trained to report signs of radicalisation in patients and staff. This article explores how calculative modalities associated with big data and digital analytics have been translated into the non-digital realm. The surveillance of the whole of the population through the NHS indicates a dramatic policy shift away from linear profiling of those 'suspect communities' previously considered vulnerable to radicalisation. Fixed indicators of radicalisation and risk profiles no longer reduce the sample size for surveillance by distinguishing between risky and non-risky bodies. Instead, the UK government chose the NHS as a preeminent site for counter-terrorism because of the large amount of contact it has with the public. The UK government is developing a novel counter-terrorism policy in the NHS around large-N surveillance and inductive calculation, which demonstrates a translation of algorithmic modalities and calculative regimes. This article argues that this translation produces an autoimmune moment in British security discourse whereby the distinction between suspicious and non-suspicious bodies has collapsed. It explores the training provided to NHS staff, arguing that fixed profiles no longer guide surveillance: rather, surveillance inductively produces the terrorist profile.

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Section: The Algorithmic Shift In British Counter-radicalisation Policymentioning

confidence: 99%

Algorithmic autoimmunity in the NHS: Radicalisation and the clinic

Heath-Kelly

2016

Security Dialogue

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“…It is also central to resilience [30,49]. If a system fails to learn from experienced events, negative or positive, it will spend unnecessary resources in all functions outside the learning part of the SyRes spiral the next time it faces the same or a similar event.…”

Section: Learningmentioning

confidence: 99%

Systemic resilience model

Lundberg

Johansson

2015

Reliability Engineering & System Safety

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Abstract. It has been realized that resilience as a concept involves several contradictory definitions, both for instance resilience as agile adjustment and as robust resistance to situations. Our analysis of resilience concepts and models suggest that beyond simplistic definitions, it is possible to draw up a systemic resilience model (SyRes) that maintains these opposing characteristics without contradiction. We outline six functions in a systemic model, drawing primarily on resilience engineering, and disaster response: anticipation, monitoring, response, recovery, learning, and self-monitoring. The model consists of four areas: Event-based constraints, Functional Dependencies, Adaptive Capacity and Strategy. The paper describes dependencies between constraints, functions and strategies. We argue that models such as SyRes should be useful both for envisioning new resilience methods and metrics, as well as for engineering and evaluating resilient systems.

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“…Third, the risk needs to be communicated to various stakeholders within a wide region. Last, the responders need to mobilize and organize themselves for collective action to reduce risk (Comfort et al 2010 ). These subsets build upon each other and attribute to the development of resiliency.…”

Section: Communication and Sensemakingmentioning

confidence: 99%

“…In regards to information processing, Comfort and her colleagues propose a bowtie model within a disaster management system (Comfort 2007 ;Comfort et al 2010 ). The model consists of three integrated parts: (1) data collection, (2) data analysis, and (3) organizational action (see Fig.…”

Section: Communication Technologymentioning

confidence: 99%

Communicating Resilience

Rivera

Kapucu

2015

Disaster Vulnerability, Hazards and Resilience

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This chapter explores the importance of understanding how social aspects like communication and social capital impact community resilience and infl uence the various public, private, and non-profi t agencies that respond to crises. It also explores the perceptions of emergency management personnel in communicating with the public during a disaster situation. Keywords IntroductionEffective communication is important during each phase of emergency management. In the mitigation and preparedness phases, communication helps to exchange and confi rm information and efforts to be prepared by appropriate stakeholders and organizations prior to an event. During the response and recovery phases, effective communication helps people do the right thing during and after a disaster so they have the best bet of surviving with as little loss as possible. However, communication is not effective in these phases unless it is a two-way process to have input and participation from both the sender and receiver of information. Effective communication has a multitude of components to work in all four phases of emergency management. First and foremost, effective communication requires active listening to ensure understanding of the message and to respond appropriately to the information which could save lives. Of course, to ensure your audience is effectively listening, the speaker must recognize the audience and tailor the message for understanding. Knowing who the audience is in a situation can not only enable the speaker to adjust the message, but also his/her posture, demeanor, and body language for even better message sending. Unfortunately, the decision makers and media did not fully grasp these communication techniques during the 2004 hurricane season in Florida which caused an epidemic of complacency. Complacency: First Hand Experience Florida Hurricane Season 2004During the 2004 Hurricane Season, four hurricanes appeared within about a month timeframe from each other: Charley, Frances, Ivan, and Jeanne. Charley directly affect affected the Western coasts of Florida where it caused power outages, destructions of buildings, and about two dozen deaths. Frances was a Category 4 and caused severe damage to eastern Florida counties leaving millions without electricity. Ivan mostly hit the panhandle while Jeanne weaken over Florida but still caused several million in damage and left millions without electricity. These same four hurricanes, in addition to a multitude of severe tropical storms of that season, devastated the Bahamas, Jamaica, and Haiti. These islands were fl ooded, had major infrastructure destroyed, and no power for exceptionally long periods of time. In Florida, infrastructure is built with better materials to endure more havoc and emergency response and management received far more funding then the island neighbors to the south of Florida. Florida is accustomed to hurricanes. In fact, when there were hurricanes, several families would invite families or neighbors over for hurricane parties. People stocked up...

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Designing Adaptive Systems for Disaster Mitigation and Response:

Cited by 29 publications

References 0 publications

Algorithmic autoimmunity in the NHS: Radicalisation and the clinic

Algorithmic autoimmunity in the NHS: Radicalisation and the clinic

Systemic resilience model

Communicating Resilience

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