Quantifying uncertainty in citizen weather data

The sheer volume of citizen weather data collected and uploaded to online data hubs is immense. However as with any citizen data it is difficult to assess the accuracy of the measurements. Within this project we quantify just how much data is available, where it comes from, the frequency at which it is collected, and the types of automatic weather stations being used. We also list the numerous possible sources of error and uncertainty within citizen weather observations before showing evidence of such effects in real data. A thorough intercomparison field study was conducted, testing popular models of citizen weather stations. From this study we were able to parameterise key sources of bias. Most significantly the project develops a complete quality control system through which citizen air temperature observations can be passed. The structure of this system was heavily informed by the results of the field study. Using a Bayesian framework the system learns and updates its estimates of the calibration and radiation-induced biases inherent to each station. We then show the benefit of correcting for these learnt biases over using the original uncorrected data. The system also attaches an uncertainty estimate to each observation, which would provide real world applications that choose to incorporate such observations with a measure on which they may base their confidence in the data. The system relies on interpolated temperature and radiation observations from neighbouring professional weather stations for which a Bayesian regression model is used. We recognise some of the assumptions and flaws of the developed system and suggest further work that needs to be done to bring it to an operational setting. Such a system will hopefully allow applications to leverage the additional value citizen weather data brings to longstanding professional observing networks.

Is there a role for telemedicine in adult allergy services?

Telemedicine refers to the application of telecommunication and information technology (IT) in the delivery of health and clinical care at a distance or remotely and can be broadly considered in two modalities: store-and-forward and real-time interactive services. Preliminary studies have shown promising results in radiology, dermatology, intensive care, diabetes, rheumatology and primary care. However, the evidence is limited and hampered by small sample sizes, paucity of randomised controlled studies and lack of data relating to cost-effectiveness, health related quality of life and patient and clinician satisfaction. This review appraises the evidence from studies that have employed telemedicine tools in other disciplines and makes suggestions for its potential applications in specific clinical scenarios in adult allergy services. Possible examples include: triaging patients to determine the need for allergy tests; pre-assessment for specialised treatments such as allergen immunotherapy; follow up to assess treatment response and side effects; and education in self-management plan including training updates for self-injectable adrenaline and nasal spray use. This approach might improve access for those with limited mobility or living far away from regional centres, as well as bringing convenience and cost savings for the patient and service provider. These potential benefits need to be carefully weighed against evidence of service safety and quality. Keys to success include delineation of appropriate clinical scenarios, patient selection, training, IT support and robust information governance framework. Well-designed prospective studies are needed to evaluate its role. This article is protected by copyright. All rights reserved.

The 2012 update to the anticholinergic cognitive burden scale

Background: In 2008, the Anticholinergic Cognitive Burden (ACB) scale was generated through a combination of laboratory data, literature review, and expert opinion. This scale identified an increased risk in mortality and worsening cognitive function in multiple populations, including 13,000 older adults in the United Kingdom. We present an updated scale based on new information and new medications available to the market. Methods: We conducted a systematic review for publications recognizing medications with adverse cognitive effects due to anti-cholinergic properties and found no new medications since 2008.Therefore we identified medications from a review of newly ap-proved medications since 2008 and medications identified throughthe clinical experience of the authors. To be included in the updatedACB scale, medications must have met the following criteria; ACBscore of 1: evidence from in vitro data that the medication has antag-onist activity at muscarinic receptors; ACB score of 2: evidence fromliterature, prescriber’s information, or expert opinion of clinical anti-cholinergic effect; ACB score of 3: evidence from literature, pre-scriber’s information, or expert opinion of the medication causingdelirium. Results: The reviewer panel included two geriatric pharmacists,one geriatric psychiatrist, one geriatrician, and one hospitalist.Twenty-three medications were eligible for review and possible inclu-sion in the updated ACB scale. Of these, seven medications were ex-cluded due to a lack of evidence for anticholinergic activity. Of the re-maining 16 medications, ten had laboratory evidence ofanticholinergic activity and added to the ACB list with a score of one.One medication was added with a score of two. Five medicationswere included in the ACB scale with a score of three.Conclusions: The revised ACB scale provides an update of med-ications with anticholinergic effects that may increase the risk of cog-nitive impairment. Future updates will be routinely conducted tomaintain an applicable library of medications for use in clinical andresearch environments.

Social media in emergency management:Twitter as a tool for communicating risks to the public

One of the main challenges of emergency management lies in communicating risks to the public. On some occasions, risk communicators might seek to increase awareness over emerging risks, while on others the aim might be to avoid escalation of public reactions. Social media accounts offer an opportunity to rapidly distribute critical information and in doing so to mitigate the impact of emergencies by influencing public reactions. This article draws on theories of risk and emergency communication in order to consider the impact of Twitter as a tool for communicating risks to the public. We analyse 10,020 Twitter messages posted by the official accounts of UK local government authorities (councils) in the context of two major emergencies: the heavy snow of December 2010 and the riots of August 2011. Twitter was used in a variety of ways to communicate and manage associated risks including messages to provide official updates, encourage protective behaviour, increase awareness and guide public attention to mitigating actions. We discuss the importance of social media as means of increasing confidence in emergency management institutions.

Random walk with restart over dynamic graphs

Random Walk with Restart (RWR) is an appealing measure of proximity between nodes based on graph structures. Since real graphs are often large and subject to minor changes, it is prohibitively expensive to recompute proximities from scratch. Previous methods use LU decomposition and degree reordering heuristics, entailing O(|V|^3) time and O(|V|^2) memory to compute all (|V|^2) pairs of node proximities in a static graph. In this paper, a dynamic scheme to assess RWR proximities is proposed: (1) For unit update, we characterize the changes to all-pairs proximities as the outer product of two vectors. We notice that the multiplication of an RWR matrix and its transition matrix, unlike traditional matrix multiplications, is commutative. This can greatly reduce the computation of all-pairs proximities from O(|V|^3) to O(|delta|) time for each update without loss of accuracy, where |delta| (<<|V|^2) is the number of affected proximities. (2) To avoid O(|V|^2) memory for all pairs of outputs, we also devise efficient partitioning techniques for our dynamic model, which can compute all pairs of proximities segment-wisely within O(l|V|) memory and O(|V|/l) I/O costs, where 1<=l<=|V| is a user-controlled trade-off between memory and I/O costs. (3) For bulk updates, we also devise aggregation and hashing methods, which can discard many unnecessary updates further and handle chunks of unit updates simultaneously. Our experimental results on various datasets demonstrate that our methods can be 1–2 orders of magnitude faster than other competitors while securing scalability and exactness.