FRONT. MICROBIOL. 21/05/21 Big Data Impacting Dynamic Food Safety Risk Management in the Food Chain. Introduction An estimated 600 million people fall ill through the consumption of contaminated food and 420,000 die every year, resulting in the loss of 33 million Disability-Adjusted Life Years (World Health Organization, 2015).
These estimates can be used to direct food safety policy and risk management options (Scallan et al., 2011). The need for a risk-based approach for production of safe food is underpinned by the adoption of HACCP with the necessary prerequisite programs. Global megatrends, described as transformative global forces will pose significant challenges to future global food safety, food security, and nutrition (High Level Panel of Experts, 2017; King et al., 2017). Key drivers for change include: Global economic growth/investment/trade pricing, innovation in food production and productivity, structural and socio-economic impacts on food supply chains and changing food safety and quality management systems.
Precision Food Safety: Inputs for Food Safety Management. University of Minnesota - FEV 2019 - Thèse en ligne : The use of spatiotemporal analytical tools to inform decisions and policy in One Health scenarios. COVID-19 et suivi de données (big data) FRONTIERS IN VETERINARY SCIENCE 17/07/17 Translating Big Data into Smart Data for Veterinary Epidemiology. Introduction As our capacity to collect and store data continues to expand rapidly, challenges in veterinary epidemiology are shifting from data acquisition to translating data into meaningful insights about animal health.
While human medicine and public health have harnessed big data to optimize “precision” care and track trends in human diseases (1–8), big data in the field of veterinary medicine have been mostly focused on spatial analyses and bioinformatics (9–13). ENVA 07/06/19 Thèse en ligne : ÉMERGENCE DE L’INTELLIGENCE ARTIFICIELLE ET UTILISATION DES TECHNOLOGIES BIG DATA EN MÉDECINE VÉTÉRINAIRE : IMPORTANCE DE LA SENSIBILISATION DES FUTURS VÉTÉRINAIRES. FRONT. VET. SCI. 02/10/18 Detecting and predicting emerging disease in poultry with the implementation of new technologies and big data. Introduction Global population growth, along with rising affluence in Asia, are driving up not only our total demand for food, but also the amount of protein required to feed all of humanity (1, 2).
More specifically, the world's population is expected to grow to over 9 billion people by 2050, and demand for poultry, which represents a relatively healthy and efficient source of protein, is likely to be double from what it was in 2005. At the same time, it is expected that the world will consume 40% more chicken eggs (3). Reaching these levels of production requires intensification of poultry operations, and this will translate into larger farms with more poultry houses and birds.
A concern is raised in the literature that intensive systems of livestock production may be more vulnerable to outbreaks of disease in both farmed animals and in human populations (4, 5). Part I. EQUINE VETERINARY JOURNAL 03/06/19 Harnessing big data for equine health. Computers and Electronics in Agriculture Volume 151, August 2018, Big data and machine learning for crop protection. AGPROFESSIONAL 03/08/16 Fighting pests and disease with big data agriculture.
CGIAR 11/04/18 Big Data to the rescue for cassava disease monitoring in Uganda. Photo by Neil Palmer / CIAT The sight of the huge freshly roasted tubers by the roadside along the Kampala-Gulu highway is quite odd to the uninitiated. Their behemoth size; at times the diameter of a male forearm can be overwhelming to behold, let alone eat. However, for the price of less than a third a US dollar, it is not easy to suppress the temptation to buy one, even just for curiosity’s sake. A short bite into the crusty exterior leads you into the belly of the tuber; still warm from the fire, the soft starchy innards soon turn sweet as they dissolve with every chewing motion. A few bites in and you’ll surely need a drink to wash down all that starch. Unfortunately, this rosy culinary picture is being severely affected by a host of diseases ravaging cassava plantations of smallholder farmers who still grow the indigenous variety of the crop. EFSA 01/07/19 Les données de sécurité alimentaire accessibles grâce à une nouvelle console pour développeurs d'applications.
Un nouveau « portail pour développeurs » utilisant la technologie API (interface de programmation d'applications) a été mis en place pour rendre les informations de l'EFSA facilement accessibles aux développeurs de logiciels, leur consentant ainsi de concevoir de nouvelles applications créatives ou des outils innovants destinés aux évaluateurs du risque ou aux consommateurs.
CDC - JUNE 2019 - Using Big Data to Monitor the Introduction and Spread of Chikungunya, Europe, 2017. Joacim Rocklöv , Yesim Tozan, Aditya Ramadona, Maquines O.
Sewe, Bertrand Sudre, Jon Garrido, Chiara Bellegarde de Saint Lary, Wolfgang Lohr, and Jan C. Semenza. JORF 28/04/19 Décret n° 2019-378 du 26 avril 2019 relatif aux conditions de collecte et de traitement de données épidémiologiques par des personnes agréées. EFSA 30/01/19 Guidelines for reporting 2018 prevalence sample‐based data in accordance with SSD2 data model. EFSA 30/01/19 Data dictionaries‐guidelines for reporting 2018 data on zoonoses, antimicrobial resistance and food‐borne outbreaks. EFSA 21/01/19 Animal health: harmonised data collection for more effective risk assessment.
Journées de la recherche avicole et palmipèdes à foie gras via YOUTUBE - 2017 - Au sommaire: Elevage de précision et Big data : un nouveau challenge pour la filière avicole. FRONT. VET. SCI. 02/10/18 Detecting and predicting emerging disease in poultry with the implementation of new technologies and big data. 1University of Guelph, Canada Future demands for food will place agricultural systems under pressure to increase production.
Poultry is accepted as a good source of protein and the poultry industry will be forced to intensify production in many countries, leading to greater numbers of farms that house birds at elevated densities. Increasing farmed poultry can facilitate enhanced transmission of infectious pathogens among birds, such as avian influenza virus among others, which have the potential to induce widespread mortality in poultry and cause considerable economic losses.
Additionally, the capability of some emerging poultry pathogens to cause zoonotic human infection will be increased as greater numbers of poultry operations could increase human contact with poultry pathogens. In order to combat the increased risk of spread of infectious disease in poultry due to intensified systems of production, rapid detection and diagnosis is paramount. Edited by: * Correspondence: Dr. ANIMALS 26/09/17 VetCompass Australia: A National Big Data Collection System for Veterinary Science. MDPI and ACS Style McGreevy, P.; Thomson, P.; Dhand, N.K.; Raubenheimer, D.; Masters, S.; Mansfield, C.S.; Baldwin, T.; Soares Magalhaes, R.J.; Rand, J.; Hill, P.; Peaston, A.; Gilkerson, J.; Combs, M.; Raidal, S.; Irwin, P.; Irons, P.; Squires, R.; Brodbelt, D.; Hammond, J.
VetCompass Australia: A National Big Data Collection System for Veterinary Science. Animals 2017, 7, 74. AMA Style. SCIENCES ET AVENIR 27/10/14 Les "big data", nouvel outil contre les épidémies comme Ebola ? BIG DATA. Neuf jours avant que la propagation d'Ebola soit officiellement déclarée par l'OMS le 23 mars comme étant une épidémie, un groupe de chercheurs et de spécialistes informatiques à Boston avait déjà remarqué la diffusion du virus de la fièvre hémorragique en Guinée. En épluchant les réseaux sociaux, les bulletins d'information locaux et d'autres bases de données, l'algorithme développé par la société HealthMap (littéralement "carte de la santé") aurait rapidement détecté la propagation d'une "mystérieuse fièvre hémorragique" en Afrique de l'Ouest.
IMVETERINARIA_ES 10/04/18 El potencial del Big Data en epidemiología veterinaria. El análisis de Big Data se utiliza para comprender riesgos y minimizar el impacto de los problemas adversos en la salud animal a través de la identificación de poblaciones de alto riesgo, combinando datos o procesos que actúan en múltiples escalas a través de enfoques de modelación epidemiológica, y aprovechando datos de alta velocidad para monitorear las tendencias de salud animal, y detectar nuevas amenazas y retos.
NATURE 07/03/18 Infection forecasts powered by big data Web searches, medical records and networks of local volunteers are enabling faster control of disease outbreaks. Even though you know it’s a sensible idea, you’re on the fence about whether it would be worth the bother to have this season’s influenza vaccine.
But a quick glance at the flu forecast on your phone sets you straight: there’s a warning about a recent spike of cases nearby, so you head to the clinic rather than risk a feverish week in bed. Epidemiologists eagerly anticipate such a future, in which they can track infectious diseases with the same confidence as meteorologists mapping the weather. But those making predictions of this type face a serious problem. “There is just not a lot of observational data in the disease world,” says Cécile Viboud, an epidemiologist at the US National Institutes of Health Fogarty International Center in Bethesda, Maryland. “It’s several orders of magnitude less than what we have in other fields.” Going viral.
AGFUNDERNEWS 22/01/18 BREAKING EXCLUSIVE: KisanHub Raises £1.75m for Big Data Potato Platform. BMC VETERINARY RESEARCH 22/12/17 Data distribution in public veterinary service: health and safety challenges push for context-aware systems. The increasing interdependence between humans, animals and their products as well as the close association with companion animals have encouraged a change in the public health system thinking .
The study of public veterinary systems has rapidly grown as a domain in itself supported by the adoption since 1984 of the “One Health” paradigm as an effective strategy for the prevention and control of zoonoses [2, 3]. In an increasingly globalised world, this new approach encompasses zoonotic infections, food safety, the environment and the health delivery systems. The integration of the epidemiological and economic frameworks within the new technological turn called the Internet of Things (IoT), led to an escalating number of distributed sensors, which, together with the laboratory data, make available large amounts of information .
Front. Vet. Sci., 06 October 2017 The Potential for Big Data in Animal Disease Surveillance in Ireland. Introduction Animal Health Surveillance is the systematic collection, collation, analysis interpretation, and dissemination of animal health and welfare data from defined populations. This process is essentially about gathering intelligence to detect either novel animal health-related events or increases in animal health-related events as early as possible to better inform risk management at all levels within the industry (1). The incursion of an exotic disease, such as Foot and Mouth disease, is probably the most significant event from a national animal health perspective. The early detection of a newly emerging disease or the re-emergence of a disease which was previously eradicated from the state before it becomes widespread in the population is another important objective of a well functioning surveillance system. FRONTIERS IN VETERINARY SCIENCE 16/11/17 The Scope of Big Data in One Medicine: Unprecedented Opportunities and Challenges.
Overview and Introduction “Big data” has become a catch phrase across many industries including medicine. As of this writing, a PubMed search for the term “big data” retrieves 10,015 entries, each detailing some aspect of big data in human or veterinary medicine, public heath, veterinary epidemiology, environmental or ecosystem health, and animal husbandry, among others. Occasionally, these papers encompass big data at the intersection of one or more of the above fields, but they generally deal with only one aspect or application of big data. Even for those investigators working with and applying big data to understand health and disease, an individual’s definition of big data is often limited to its use within a particular field of study.
Before delving into the breadth and depth of big data, we start by introducing the reader to our definitions of “one medicine” and “big data” as they will be used throughout this review. One Medicine. PIGPROGRESS 25/08/17 Will Big Data change the future of pig genetics? FRONTIERS IN VETERINARY SCIENCE 23/08/17 Roadmap to the Digital Transformation of Animal Health Data. The Context The use of antimicrobials in livestock production provides a basis to improve animal health and productivity which, in turn, contributes to food security, food safety, animal welfare, protection of livelihoods, and animal resources.
However, there is increasing concern about levels of antimicrobial resistance in bacteria isolated from human, animal, food, and environmental samples and how this relates to the use of antimicrobials in livestock production. The reality is that both the quantity and quality of data available on the usage of antimicrobials in livestock production is grossly inadequate. FRONTIERS IN VETERINARY SCIENCE 17/07/17 Translating Big Data into Smart Data for Veterinary Epidemiology. Introduction As our capacity to collect and store data continues to expand rapidly, challenges in veterinary epidemiology are shifting from data acquisition to translating data into meaningful insights about animal health.
While human medicine and public health have harnessed big data to optimize “precision” care and track trends in human diseases (1–8), big data in the field of veterinary medicine have been mostly focused on spatial analyses and bioinformatics (9–13). WEED RESEARCH 24/05/17 Big Data for weed control and crop protection. Introduction Food production must increase by 70% in order to feed a world population that is expected to reach 9.6 billion by 2050 (Foley, 2011; Foley et al., 2011). This challenge is even greater, when we take into account the scarcity of new arable land, the effects of climate change on agricultural production and the societal demand for decreasing the environmental impact of agriculture (Foley et al., 2011).
Weed management will be of crucial importance, given that crop yield losses caused by weeds (about 32%) are higher than those caused by either pests (18%) or pathogens (15%) (Oerke & Dehne, 2004). However, it is not just farmers who will use Big Data solutions for weed control. In several European countries, the number of invasive plants (IAS, invasive alien species) has significantly increased during the last decades (De Almeida & Freitas, 2012; Pyšek et al., 2012).
Data-driven innovations have already revolutionised several sectors of the economy. Where does the data come from? NAP_EDU - Proceedings of a Workshop (2016) - Big Data and Analytics for Infectious Disease Research, Operations, and Policy. JMLR: Workshop and Conference Proceedings - 2015 - Interactive Visual Big Data Analytics for Large Area Farm Biosecurity Monitoring: i-EKbase System. FRONTIERS IN VETERINARY SCIENCE 22/06/17 Translating Big Data into Smart Data for Veterinary Epidemiology. Front. Vet. Sci. 10/07/17 Tapping the vast potential of the data deluge in small-scale food-animal production businesses: challenges to near real-time data analysis and interpretation. POURQUOI DOCTEUR 16/03/14 Des résultats erronés 100 semaines sur 108 - Big data : Google très mauvais pour prédire l’épidémie de grippe. FRONTIERS IN VETERINARY SCIENCE 22/06/17 Translating Big Data into Smart Data for Veterinary Epidemiology. NIH 14/11/16 NIH-led effort examines use of big data for infectious disease surveillance.
News Release. INSERM - Big data en santé. Dossier réalisé en collaboration avec Rodolphe Thiebaut, directeur de l’équipe Statistiques pour la médecine translationnelle (unité 1219 Inserm/Inria), enseignant à l’Institut de santé publique d’épidémiologie et de développement (ISPED, Bordeaux), directeur de l’unité de soutien méthodologique à la recherche clinique et épidémiologique au CHU de Bordeaux et chercheur au Vaccine Research Institute (Créteil).
COMPUTER SCIENCE 22/01/15 Using a Big Data Database to Identify Pathogens in Protein Data Space. Parasit Vectors. 2016; 9: 303. Use of big data in the surveillance of veterinary diseases: early detection of tick paralysis in companion animals. INSTITUTO POLITECNICO DE BRAGANCA - 2014 - Thèse en ligne : Meta-analysis of the incidence of food-borne pathogens in Portuguese meats and their products. UNIVERSITY OF LIVERPOOL 27/08/14 Big data approach identifies Europe’s most dangerous human and domestic animal pathogens. The most dangerous 100 pathogens in Europe have been identified The pathogens posing the greatest risk to Europe based upon a proxy for impact have been identified by University of Liverpool researchers using a ‘big data’ approach to scientific research. The researchers from the University’s Institute of Infection and Global Health ranked the top 100 pathogens affecting humans and the top 100 affecting domestic animals using a system which, they believe, will help governments across the continent plan for risks associated with the spread of infectious diseases, including as a result of climate change, and for biosecurity.
Risk The top risk for both humans and animals was E.coli and in humans this was followed by two forms of HIV, Hepatitis C and Staphylococcus aureus, a bacteria which causes food poisoning and is increasingly resistant to antibiotics. To compile the list, the researchers used the EID2 database developed at Liverpool. Dr Marie McIntyre led the study. Hirsch Index.