Blog
About

2
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      @choo: Tracking Pollen and Hayfever in the UK Using Social Media

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Allergic rhinitis (hayfever) affects a large proportion of the population in the United Kingdom. Although relatively easily treated with medication, symptoms nonetheless have a substantial adverse effect on wellbeing during the summer pollen season. Provision of accurate pollen forecasts can help sufferers to manage their condition and minimise adverse effects. Current pollen forecasts in the UK are based on a sparse network of pollen monitoring stations. Here, we explore the use of “social sensing” (analysis of unsolicited social media content) as an alternative source of pollen and hayfever observations. We use data from the Twitter platform to generate a dynamic spatial map of pollen levels based on user reports of hayfever symptoms. We show that social sensing alone creates a spatiotemporal pollen measurement with remarkable similarity to measurements taken from the established physical pollen monitoring network. This demonstrates that social sensing of pollen can be accurate, relative to current methods, and suggests a variety of future applications of this method to help hayfever sufferers manage their condition.

          Related collections

          Most cited references 23

          • Record: found
          • Abstract: not found
          • Article: not found

          AN AUTOMATIC VOLUMETRIC SPORE TRAP

           J. HIRST (1952)
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Burden of allergic disease in the UK: secondary analyses of national databases.

            Although allergy represents an important source of patient morbidity and healthcare utilization, there is little reliable information on the overall disease burden posed by allergic conditions in the UK. Focusing on the following conditions: allergic rhinitis, anaphylaxis, asthma, conjunctivitis, eczema/dermatitis, food allergy and urticaria/angioedema, we sought to (i) describe the prevalence, incidence and outcomes of allergic disorders; (ii) describe the NHS healthcare burden posed by allergic disorders; (iii) estimate the costs of allergic disorders from a healthcare perspective. Secondary analyses of data from the Health Survey for England, Scottish Health Survey, International Study of Allergies and Asthma in Childhood, European Community Respiratory Health Survey, Morbidity Statistics from General Practice 1991/1992, Royal College of General Practitioners Weekly Returns Service, Prescribing Analysis and Cost data, Hospital Episodes Statistics and national mortality data. Thirty-nine percent of children and 30% of adults have been diagnosed with one or more atopic conditions. Six percent of general practice consultations and 0.8% of hospital admissions are for allergic diseases. Treatments for asthma and other allergic disorders currently account for 10% of primary care prescribing costs. Direct NHS costs for managing allergic problems are estimated at over one billion UK pounds per annum. Allergic disorders are common throughout the UK, affecting males and females of all ages and peoples from all social classes and ethnic groups. They currently represent a substantial burden of morbidity and health service cost.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Big data for health.

              This paper provides an overview of recent developments in big data in the context of biomedical and health informatics. It outlines the key characteristics of big data and how medical and health informatics, translational bioinformatics, sensor informatics, and imaging informatics will benefit from an integrated approach of piecing together different aspects of personalized information from a diverse range of data sources, both structured and unstructured, covering genomics, proteomics, metabolomics, as well as imaging, clinical diagnosis, and long-term continuous physiological sensing of an individual. It is expected that recent advances in big data will expand our knowledge for testing new hypotheses about disease management from diagnosis to prevention to personalized treatment. The rise of big data, however, also raises challenges in terms of privacy, security, data ownership, data stewardship, and governance. This paper discusses some of the existing activities and future opportunities related to big data for health, outlining some of the key underlying issues that need to be tackled.
                Bookmark

                Author and article information

                Affiliations
                [1 ]Computer Science, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4QE, UK; sophie_cowie@ 123456hotmail.com (S.C.); rudy.d.arthur@ 123456gmail.com (R.A.)
                [2 ]The Alan Turing Institute, 96 Euston Road, London NW1 2DB, UK
                Author notes
                [* ]Correspondence: h.t.p.williams@ 123456exeter.ac.uk ; Tel.: +44-139-272-3777
                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                14 December 2018
                December 2018
                : 18
                : 12
                30558222 6308444 10.3390/s18124434 sensors-18-04434
                © 2018 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                Categories
                Article

                Biomedical engineering

                crowdsourcing, hayfever, social media, pollen, social sensing

                Comments

                Comment on this article