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BMC PUBLIC HEALTH 14/05/19 Machine learning to refine decision making within a syndromic surveillance service. One important output of our results is that they provide confidence in the current risk assessment process.

BMC PUBLIC HEALTH 14/05/19 Machine learning to refine decision making within a syndromic surveillance service

The attributes of each individual risk assessment shown to be linked to the final decision as the p-values in Table 1 were nearly all significant, indicating that the current system is robust and provides consistent results. The five classifiers had relatively high accuracy but this metric was dominated by ‘No-action’ outcomes. From a public heath perspective recall is sometimes more useful, as recall records the proportion of events detected, even if some of these are eventually assessed as false. The NB classifier was shown to have a higher recall for ‘Alert’ and ‘Monitor’ outcomes, in comparison with TAN or Multinet which permit augmentation between attributes.

Modified versions of TAN and Multinet, TAN* and Multinet*, were implemented. However, it is imperative to see such a system as a decision support system and not as a decision making tool. NATURE 06/11/18 Machine-learned epidemiology: real-time detection of foodborne illness at scale. Experimental design FINDER is a machine-learned model for real-time foodborne illness detection.

NATURE 06/11/18 Machine-learned epidemiology: real-time detection of foodborne illness at scale

To determine the ability of FINDER to detect potentially unsafe restaurants, we introduced FINDER into two local health departments in Chicago and Las Vegas. In each city, FINDER-identified restaurants were inspected following the same protocol used in other restaurant inspections. The results of the FINDER inspections were then compared to the overall baseline inspection results, as well as to two subsets of baseline inspections, complaint-based inspections, and routine inspections that are conducted at certain time intervals. Analyses were further stratified by restaurant risk level. Iranian J Publ Health, Vol. 41, No.11, Nov 2012, pp.102-103 Validity of Evaluation Approaches for Outbreak Detection Methods in Syndromic Surveillance Systems. PATENT STORM 18/01/05 US Patent 7447333 - Video and audio monitoring for syndromic surveillance for infectious diseases. Journal of Urban Health: Bulletin of the New York Academy of Medicine Vol. 80, No. 2, Supplement 1 2003 Syndromic Surveillance Using Automated Collection of Computerized Discharge Diagnoses.

Journal of Acute Disease (2012) Syndromic surveillance: A necessary public health tool. EUROSURVEILLANCE Volume 14, Issue 44, 05 November 2009 Syndromic surveillance: the next phase of public health monitoring during the H1N1 influenza pandemic? PLOS 07/12/16 Implementation of Syndromic Surveillance Systems in Two Rural Villages in Senegal . Abstract Infectious diseases still represent a major challenge for humanity.

PLOS 07/12/16 Implementation of Syndromic Surveillance Systems in Two Rural Villages in Senegal .

In this context, their surveillance is critical. KINGSTON UNIVERSITY LONDON - 2014 - A theory-based online health behaviour intervention for new university students (U@Uni): results from a randomised controlled trial. Epton, Tracy, Norman, Paul , Dadzie, Aba-Sah , Harris, Peter R., Webb, Thomas L., Sheeran, Paschal, Julious, Steven A., Ciravegna, Fabio , Brennan, Alan , Meier, Petra S., Naughton, Declan, Petroczi, Andrea, Kruger, Jen and Shah, Iltaf (2014) A theory-based online health behaviour intervention for new university students (U@Uni): results from a randomised controlled trial.

KINGSTON UNIVERSITY LONDON - 2014 - A theory-based online health behaviour intervention for new university students (U@Uni): results from a randomised controlled trial

BMC Public Health, 14(563), ISSN (online) 1471-2458.

Documents ISDS

University of Pittsburgh - 2012 - Thèse en ligne : SYNDROMIC SURVEILLANCE FOR BIOTERRORISM-RELATED INHALATION ANTHRAX IN AN EMER. Le site syndromic surveillance Systems in Europe. J Bioterr Biodef 2012, 3:3 Syndromic Surveillance: Early Warning Systems for Monitoring Emerging Outbreaks of Health Events from. Epidemiol. Infect., - 2012 - Developing a new syndromic surveillance system for the London 2012 Olympic and Paralympic Games. Strategic early warning system. The aim of a Strategic Early Warning System (SEWS) is to assist organizations in dealing with discontinuities or strategic surprises.

Strategic early warning system

By detecting weak signals (Igor Ansoff, 1975), which can be perceived as important discontinuities in an organizational environment, SEWS allows organizations to react strategically ahead of time. Underlying theory[edit] The underlying assumption of SEWS is that discontinuities do not emerge without warning. These warning signs are described as "weak signals" (Ansoff, 1975), a concept aimed at early detection of those signals which could lead to strategic surprises -- events which have the potential to jeopardise an organization’s strategy. UC DAVIS OCT 2009 PREDICT: Building a global early warning system for emerging diseases that move between wildlife and people.

PREVENTION BULLETIN JAN/FEV 2002 Syndromic Disease Surveillance in the Wake of Anthrax Threats and High Profile Public Events. Preventing_Chaos. PREVENTING CHAOS IN A CRISIS - Strategies for prevention, control and damage limitationPATRICK LAGADEC. OMS - THE FUTURE OF HEALTH – HEALTH OF THE FUTURE. Naval Postgraduate School (Monterey) - 2006 - Syndromic SurveillanceAn Article for The Encyclopedia for Quantitative Risk Assess. MISSOURI - 2009 - ESSENCE: Missouri's Syndromic Surveillance Project. Background.

MISSOURI - 2009 - ESSENCE: Missouri's Syndromic Surveillance Project

LOS ANGELES - 2003 - DESCRIPTION OF AN EMERGENCY DEPARTMENT-BASED SYNDROMIC SURVEILLANCE SYSTEM IN LOS ANGELES COUNTY. J Public Health (Oxf). 2009 Dec;31(4):566-72. Epub 2009 May 13. Using encounters versus episodes in syndromic surveillance. Journal of Bioethical Inquiry Volume 6, Number 2 / juin 2009 Syndromic Surveillance and Patients as Victims and Vectors. J Am Med Inform Assoc. 2004 Mar-Apr;11(2):141-50. Epub 2003 Nov 21. Implementing syndromic surveillance: a practical guide infor. Journal of the American Medical Informatics Associationjamia.bmj.com 2004;11:141-150 doi:10.1197/jamia.M1356 The Practice of Informatics Review Paper + Author Affiliations Correspondence and reprints: Kenneth D.

J Am Med Inform Assoc. 2004 Mar-Apr;11(2):141-50. Epub 2003 Nov 21. Implementing syndromic surveillance: a practical guide infor

Mandl, MD, MPH, Division of Emergency Medicine, Children's Hospital Boston, 300 Longwood Avenue, Boston, MA 02115; e-mail: kenneth_mandl@harvard.edu. Abstract Syndromic surveillance refers to methods relying on detection of individual and population health indicators that are discernible before confirmed diagnoses are made. International Journal of Health Geographics 2010, 9:1 Enhancing spatial detection accuracy for syndromic surveillance with stree. Spatial Scan Statistic The spatial detection software used by ESSENCE is adapted from SaTScan, a program developed by Kulldorff [4] which is widely accepted as the de facto standard for spatial-temporal detection of disease clusters.

International Journal of Health Geographics 2010, 9:1 Enhancing spatial detection accuracy for syndromic surveillance with stree

Kulldorff's scan statistics are typically used to detect clusters of disease incidents in both time and space. With ESSENCE, purely spatial methods are used and a non-mathematical description of that statistic is given here. In short, a circular window is scanned across geographic space evaluating the number of observed and expected incidents inside the window at each location. Multiple window sizes are assessed at each location and adjustments are made for the variable density of the background population and the number of cases observed. ESSENCE spatial detection is based on the Poisson model. The Bernoulli model is an alternative scan statistic wherein cases and "non-cases" are analyzed, e.g., patients with ILI symptoms and those without ILI symptoms. Am J Public Health > v.96(3); Mar 2006 Diarrheal Illness Detected Through Syndromic Surveillance After a Massive Power Outage: N.

Ann Emerg Med. 2006 Mar;47(3):265.e1. Epub 2006 Jan 18. Validation of syndromic surveillance for respiratory infections.

Documents BMC

Can J Infect Dis Med Microbiol. 2006 Jul–Aug; 17(4): 235–241. Syndromic Surveillance of Norovirus using Over-the-counter Sales. Documents CDC. Syndromic surveillance: faulty alarm system or useful tool? May 16, 2007 (CIDRAP News) – At first glance, the Web page looks like an overhead shot of a fantastic game board: a map—identifiably Los Angeles—sprinkled with faceted roundels in a half-dozen colors.

Syndromic surveillance: faulty alarm system or useful tool?

But the data graphically displayed at WhoIsSick.org are from the real world. The roundels represent reports of symptoms volunteered by site users: runny nose, cough, fever, headache, muscle aches, and digestive trouble. The site's founder, a California tech entrepreneur named PT Lee, drew on the new Web technology of Google Maps and the Web trend toward user participation to create a 21st-century service that a 19th-century epidemiologist would recognize: geographic surveillance of illness trends.

WhoIsSick, which went live 2 months ago after a year of planning, grew out of Lee's frustration over his wife's holiday bout with a gastrointestinal bug. The site has registered about 200,000 visitors so far. The spike in interest was followed by a vast increase in funding. See also: WhoIsSick. CMAJ 14/04/09 Early detection of disease outbreaks using the Internet. + Author Affiliations Correspondence to: Dr.

CMAJ 14/04/09 Early detection of disease outbreaks using the Internet

Kumanan Wilson, The Ottawa Hospital, Civic Campus, 1053 Carling Ave., Administrative Services Building, Rm. 1009, Box 684, Ottawa ON K1Y 4E9; fax 416 595-5826; kwilson@ohri.ca Rapidly identifying an infectious disease outbreak is critical, both for effective initiation of public health intervention measures and timely alerting of government agencies and the general public. Surveillance capacity for such detection can be costly, and many countries lack the public health infrastructure to identify outbreaks at their earliest stages.

Furthermore, there may be economic incentives for countries to not fully disclose the nature and extent of an outbreak.1 The Internet, however, is revolutionizing how epidemic intelligence is gathered, and it offers solutions to some of these challenges. Advice of the Scientific Committee in relation to EFSA's activities in a crisisThe advice is based on a draft prepared by. Doi:10.2903/j.efsa.2004.14r European Food Safety Authority Type: Technical Report Question number: EFSA-Q-2003-102 Published: 21 September 2004 Last updated: 21 September 2004. This version replaces the previous one/s. AbstractSummary The advice is based on a draft prepared by the Task Force on "Crisis Management" of the EFSA Scientific Committee. Section 3 of Regulation N° 178/2002 addresses the role of EFSA in a crisis. Not only will the Authority need to be prepared to react in a crisis as identified in the Regulation it will also need to have in place systems which identify developing potential crisis situations and be able to be proactive in this respect.

EUREKALERT 23/10/09 UC Davis leads attack on deadly new diseasesUSAID grant of up to $75 million will help prevent pandemics. Public release date: 23-Oct-2009 [ Print | E-mail Share ] [ Close Window ] Contact: Jonna Mazetjkmazet@ucdavis.edu 530-754-9035University of California - Davis In hopes of preventing the next global pandemic and a possible death toll into the millions, UC Davis today launches an unprecedented international effort to find and control diseases that move between wildlife and people. The global early warning system, named PREDICT, will be developed with funding of up to $75 million over five years and is one of five new initiatives of the U.S.

Agency for International Development (USAID) known in combination as the Emerging Pandemic Threats Program. UC Davis' primary PREDICT partners, which have formed a global consortium to implement PREDICT around the world, are: Wildlife Conservation Society, Wildlife Trust, Global Viral Forecasting Inc., and Smithsonian Institution. UC Davis will bring on emerging-disease authority Stephen S. Triple-S – the syndromic surveillance project. EUROSURVEILLANCE 28/04/11 European institutes for disease prevention and control collaborate to improve public health surveillan.

European institutes for disease prevention and control collaborate to improve public health surveillance Citation style for this article: Hulth A, Viso AC. European institutes for disease prevention and control collaborate to improve public health surveillance. Euro Surveill. 2011;16(17):pii=19851. MICROSOFT RESEARCH - 2015 - Ebola data from the Internet: An opportunity for syndromic surveillance or a news event?