Data Management in the Worldwide Sensor Web

2015

system available for monitoring infectious disease outbreaks and other global events. MiTAP focuses on providing timely, multi-lingual, global information access to medical experts and individuals involved in humanitarian assistance and relief work. Multiple information sources in multiple languages are automatically captured, filtered, translated, summarized, and categorized by disease, region, information source, person, and organization. Critical information is automatically extracted and tagged to facilitate browsing, searching, and sorting. The system supports shared situational awareness through collaboration, allowing users to submit other articles for processing, annotate existing documents, post directly to the system, and flag messages for others to see. MiTAP currently stores eight hundred thousand articles and processes an additional 2000 to 10,000 daily, delivering up-to-date information to dozens of regular users.

Public health reports (Washington, D.C. : 1974), 2017

Public health surveillance is the foundation of effective public health practice. Public health surveillance is defined as the ongoing systematic collection, analysis, and interpretation of data, closely integrated with the dissemination of these data to the public health practitioners, clinicians, and policy makers responsible for preventing and controlling disease and injury. 1 Ideally, surveillance systems should support timely, efficient, flexible, scalable, and interoperable data acquisition, analysis, and dissemination. However, many current systems rely on disease-specific approaches that inhibit efficiency and interoperability (eg, manual data entry and data recoding that place a substantial burden on data partners) and use slow, inefficient, out-of-date technologies that no longer meet user needs for data management, analysis, visualization, and dissemination. 2-4 Advances in information technology, data science, analytic methods, and information sharing provide an opportunity to substantially enhance surveillance. As a global leader in public health surveillance, the Centers for Disease Control and Prevention (CDC) is working with public health partners to transform and modernize CDC's surveillance systems and approaches. Here, we describe recent enhancements in surveillance data analysis and visualization, information sharing, and dissemination at CDC and identify the challenges ahead.

Online Journal of Public Health Informatics, 2011

Lack of automated and integrated data collection and management, and poor linkage of clinical, epidemiological and laboratory data during an outbreak can inhibit effective and timely outbreak investigation and response. This paper describes an innovative web-based technology, referred to as Web Data, developed for the rapid setup and provision of interactive and adaptive data management during outbreak situations. We also describe the benefits and limitations of the Web Data technology identified through a questionnaire that was developed to evaluate the use of Web Data implementation and application during the 2009 H1N1 pandemic by Winnipeg Regional Health Authority and Provincial Laboratory for Public Health of Alberta. Some of the main benefits include: improved and secure data access, increased efficiency and reduced error, enhanced electronic collection and transfer of data, rapid creation and modification of the database, conversion of specimen-level to case-level data, and user-defined data extraction and query capabilities. Areas requiring improvement include: better understanding of privacy policies, increased capability for data sharing and linkages between jurisdictions to alleviate data entry duplication.

The novel emergent coronavirus now designated SARS-CoV-2 emerged in Wuhan, Hubei Province, China, in late 2019. Since then, this rapidly spreading pathogen has created one of the most significant pandemics in recent history. Globalisation and international trade have created a unique potential for fast-moving pathogens to spread globally overnight. In this paper, we describe a globally integrated system of data-driven viral surveillance by analogy to the CTBTO's technical infrastructure. Established as a result of the Comprehensive Nuclear Test Ban Treaty signed in 1996, it is backed by a robust, globally distributed monitoring infrastructure that may serve as an inspiration for developing a similar monitoring network for public health. We propose to build on the experiences of the CTBTO's technical infrastructure to conceptualise a Pandemic Advance Warning System, an early warning infrastructure for emerging pathogenic threats.

2012

Health surveillance practices date back to decades ago. Traditionally, such practices to gather health data have been manual; more recently, however, computerized health information systems have been applied to enhance and facilitate health information acquisition for surveillance. The so-called health surveillance systems put in practice the systematic acquisition of health data, which is stored and processed for expert analysis. This chapter makes a survey of health surveillance systems dedicated to syndromic and epidemiological surveillance, identifying the different design and technological strategies adopted in the development of such systems. The aims of such a survey are: (1) to provide practitioners with some information about the collective expertise of health information system architects in the design and implementation of syndromic and epidemiological surveillance systems; and (2) to pave the way for the establishment of software product lines dedicated to such systems.

Journal of the American Medical Informatics Association

Public health faces unprecedented challenges in its efforts to control COVID-19 through a national vaccination campaign. Addressing these challenges will require fundamental changes to public health data systems. For example, of the core data systems for immunization campaigns is the immunization information system (IIS); however, IISs were designed for tracking the vaccinated, not finding the patients who are high risk and need to be vaccinated. Health systems have this data in their electronic health records (EHR) systems and often have a greater capacity for outreach. Clearly, a partnership is needed. However, successful collaborations will require public health to change from its historical hierarchical information supply chain model to an ecosystem model with a peer-to-peer exchange with population health providers. Examples of the types of informatics innovations necessary to support such an ecosystem include a national patient identifier, population-level data exchange for im...

American Journal of Public Health, 2009

IEEE Transactions on Information Technology in Biomedicine, 2000

Motivated by the importance of infectious disease informatics (IDI) and the challenges to IDI system development and data sharing, we design and implement BioPortal, a Web-based IDI system that integrates cross-jurisdictional data to support information sharing, analysis, and visualization in public health. In this paper, we discuss general challenges in IDI, describe BioPortal's architecture and functionalities, and highlight encouraging evaluation results obtained from a controlled experiment that focused on analysis accuracy, task performance efficiency, user information satisfaction, system usability, usefulness, and ease of use.

Efficient Decision Support Systems - Practice and Challenges in Biomedical Related Domain, 2011

Journal of the American Medical Informatics Association, 2003

This report describes the design and implementation of the Real-time Outbreak and Disease Surveillance (RODS) system, a computer-based public health surveillance system for early detection of disease outbreaks. Hospitals send RODS data from clinical encounters over virtual private networks and leased lines using the Health Level 7 (HL7) message protocol. The data are sent in real time. RODS automatically classifies the registration chief complaint from the visit into one of seven syndrome categories using Bayesian classifiers. It stores the data in a relational database, aggregates the data for analysis using data warehousing techniques, applies univariate and multivariate statistical detection algorithms to the data, and alerts users of when the algorithms identify anomalous patterns in the syndrome counts. RODS also has a Web-based user interface that supports temporal and spatial analyses. RODS processes sales of over-the-counter health care products in a similar manner but receives such data in batch mode on a daily basis. RODS was used during the 2002 Winter Olympics and currently operates in two states-Pennsylvania and Utah. It has been and continues to be a resource for implementing, evaluating, and applying new methods of public health surveillance.