26,829 research outputs found
Multi-scale Population and Mobility Estimation with Geo-tagged Tweets
Recent outbreaks of Ebola and Dengue viruses have again elevated the
significance of the capability to quickly predict disease spread in an emergent
situation. However, existing approaches usually rely heavily on the
time-consuming census processes, or the privacy-sensitive call logs, leading to
their unresponsive nature when facing the abruptly changing dynamics in the
event of an outbreak. In this paper we study the feasibility of using
large-scale Twitter data as a proxy of human mobility to model and predict
disease spread. We report that for Australia, Twitter users' distribution
correlates well the census-based population distribution, and that the Twitter
users' travel patterns appear to loosely follow the gravity law at multiple
scales of geographic distances, i.e. national level, state level and
metropolitan level. The radiation model is also evaluated on this dataset
though it has shown inferior fitness as a result of Australia's sparse
population and large landmass. The outcomes of the study form the cornerstones
for future work towards a model-based, responsive prediction method from
Twitter data for disease spread.Comment: 1st International Workshop on Big Data Analytics for Biosecurity
(BioBAD2015), 4 page
A Statistical Inverse Method for Gridding Passive Microwave Data with Mixed Measurements
When a passive microwave footprint intersects objects on the ground with different spectral characteristics, the corresponding observation is mixed. The retrieval of geophysical parameters is limited by this mixture. We propose to partition the study region into objects following an object-based image analysis procedure and then to refine this partition into small cells. Then, we introduce a statistical method to estimate the brightness temperature (TB) of each cell. The method assumes that TB of the cells corresponding to the same object is identically distributed and that the TB heterogeneity within each cell can be neglected. The implementation is based on an iterative expectation-maximization algorithm. We evaluated the proposed method using synthetic images and applied it to grid the TBs of sample AMSR -2 real data over a coastal region in Argentina.Fil: Grimson, Rafael. Universidad Nacional de San MartÃn; ArgentinaFil: Bali, Juan Lucas. Consejo Nacional de Investigaciones CientÃficas y Técnicas; ArgentinaFil: Rajngewerc, Mariela. Ministerio de Defensa. Instituto de Investigaciones CientÃficas y Técnicas para la Defensa; ArgentinaFil: Martin, Laura San. Universidad Nacional de San MartÃn; ArgentinaFil: Salvia, Maria Mercedes. Universidad Nacional de San MartÃn; Argentina. Consejo Nacional de Investigaciónes CientÃficas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de AstronomÃa y FÃsica del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de AstronomÃa y FÃsica del Espacio; Argentin
Architecture and Information Requirements to Assess and Predict Flight Safety Risks During Highly Autonomous Urban Flight Operations
As aviation adopts new and increasingly complex operational paradigms, vehicle types, and technologies to broaden airspace capability and efficiency, maintaining a safe system will require recognition and timely mitigation of new safety issues as they emerge and before significant consequences occur. A shift toward a more predictive risk mitigation capability becomes critical to meet this challenge. In-time safety assurance comprises monitoring, assessment, and mitigation functions that proactively reduce risk in complex operational environments where the interplay of hazards may not be known (and therefore not accounted for) during design. These functions can also help to understand and predict emergent effects caused by the increased use of automation or autonomous functions that may exhibit unexpected non-deterministic behaviors. The envisioned monitoring and assessment functions can look for precursors, anomalies, and trends (PATs) by applying model-based and data-driven methods. Outputs would then drive downstream mitigation(s) if needed to reduce risk. These mitigations may be accomplished using traditional design revision processes or via operational (and sometimes automated) mechanisms. The latter refers to the in-time aspect of the system concept. This report comprises architecture and information requirements and considerations toward enabling such a capability within the domain of low altitude highly autonomous urban flight operations. This domain may span, for example, public-use surveillance missions flown by small unmanned aircraft (e.g., infrastructure inspection, facility management, emergency response, law enforcement, and/or security) to transportation missions flown by larger aircraft that may carry passengers or deliver products. Caveat: Any stated requirements in this report should be considered initial requirements that are intended to drive research and development (R&D). These initial requirements are likely to evolve based on R&D findings, refinement of operational concepts, industry advances, and new industry or regulatory policies or standards related to safety assurance
Routes for breaching and protecting genetic privacy
We are entering the era of ubiquitous genetic information for research,
clinical care, and personal curiosity. Sharing these datasets is vital for
rapid progress in understanding the genetic basis of human diseases. However,
one growing concern is the ability to protect the genetic privacy of the data
originators. Here, we technically map threats to genetic privacy and discuss
potential mitigation strategies for privacy-preserving dissemination of genetic
data.Comment: Draft for comment
Towards evidence-based, GIS-driven national spatial health information infrastructure and surveillance services in the United Kingdom
The term "Geographic Information Systems" (GIS) has been added to MeSH in 2003, a step reflecting the importance and growing use of GIS in health and healthcare research and practices. GIS have much more to offer than the obvious digital cartography (map) functions. From a community health perspective, GIS could potentially act as powerful evidence-based practice tools for early problem detection and solving. When properly used, GIS can: inform and educate (professionals and the public); empower decision-making at all levels; help in planning and tweaking clinically and cost-effective actions, in predicting outcomes before making any financial commitments and ascribing priorities in a climate of finite resources; change practices; and continually monitor and analyse changes, as well as sentinel events. Yet despite all these potentials for GIS, they remain under-utilised in the UK National Health Service (NHS). This paper has the following objectives: (1) to illustrate with practical, real-world scenarios and examples from the literature the different GIS methods and uses to improve community health and healthcare practices, e.g., for improving hospital bed availability, in community health and bioterrorism surveillance services, and in the latest SARS outbreak; (2) to discuss challenges and problems currently hindering the wide-scale adoption of GIS across the NHS; and (3) to identify the most important requirements and ingredients for addressing these challenges, and realising GIS potential within the NHS, guided by related initiatives worldwide. The ultimate goal is to illuminate the road towards implementing a comprehensive national, multi-agency spatio-temporal health information infrastructure functioning proactively in real time. The concepts and principles presented in this paper can be also applied in other countries, and on regional (e.g., European Union) and global levels
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