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Alahmari AA, Almuzaini Y, Alamri F, Alenzi R, Khan AA. Strengthening global health security through health early warning systems: A literature review and case study. J Infect Public Health 2024; 17 Suppl 1:85-95. [PMID: 38368245 DOI: 10.1016/j.jiph.2024.01.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 01/29/2024] [Accepted: 01/31/2024] [Indexed: 02/19/2024] Open
Abstract
Disease transmission is dependent on a variety of factors, including the characteristics of an event, such as crowding and shared accommodations, the potential of participants having prolonged exposure and close contact with infectious individuals, the type of activities, and the characteristics of the participants, such as their age and immunity to infectious agents [1-3]. Effective control of outbreaks of infectious diseases requires rapid diagnosis and intervention in high-risk settings. As a result, syndromic and event-based surveillance may be used to enhance the responsiveness of the surveillance system [1]. In public health, surveillance is collecting, analyzing, and interpreting data across time to inform decision-making and aid policy implementation [1]. In this review article we aimed to provide an overview of the principles, types, uses, advantages, and limitations of surveillance systems and to highlight the importance of early warning systems in response to the information received by disease surveillance. The study conducted a comprehensive literature search using several databases, selecting, and reviewing 78 articles that covered different types of surveillance systems, their applications, and their impact on controlling infectious diseases. The article also presents a case study from the Hajj gathering, which highlighted the development, evaluation, and impact of early warning systems on response to the information received by disease surveillance. The study concludes that ongoing disease surveillance should be accompanied by well-designed early warning and response systems, and continuous efforts should be invested in evaluating and validating these systems to minimize the risk of reporting delays and reducing the risk of outbreaks.
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Affiliation(s)
- Ahmed A Alahmari
- Global Center of Mass Gatherings Medicine, Ministry of Health, Riyadh, Saudi Arabia.
| | - Yasir Almuzaini
- Global Center of Mass Gatherings Medicine, Ministry of Health, Riyadh, Saudi Arabia
| | - Fahad Alamri
- Global Center of Mass Gatherings Medicine, Ministry of Health, Riyadh, Saudi Arabia
| | | | - Anas A Khan
- Global Center of Mass Gatherings Medicine, Ministry of Health, Riyadh, Saudi Arabia; Department of Emergency Medicine, College of Medicine, King Saud University, Riyadh, Saudi Arabia
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Mudumbai SC, Gabriel RA, Howell S, Tan JM, Freundlich RE, O’Reilly Shah V, Kendale S, Poterack K, Rothman BS. Public Health Informatics and the Perioperative Physician: Looking to the Future. Anesth Analg 2024; 138:253-272. [PMID: 38215706 PMCID: PMC10825795 DOI: 10.1213/ane.0000000000006649] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2024]
Abstract
The role of informatics in public health has increased over the past few decades, and the coronavirus disease 2019 (COVID-19) pandemic has underscored the critical importance of aggregated, multicenter, high-quality, near-real-time data to inform decision-making by physicians, hospital systems, and governments. Given the impact of the pandemic on perioperative and critical care services (eg, elective procedure delays; information sharing related to interventions in critically ill patients; regional bed-management under crisis conditions), anesthesiologists must recognize and advocate for improved informatic frameworks in their local environments. Most anesthesiologists receive little formal training in public health informatics (PHI) during clinical residency or through continuing medical education. The COVID-19 pandemic demonstrated that this knowledge gap represents a missed opportunity for our specialty to participate in informatics-related, public health-oriented clinical care and policy decision-making. This article briefly outlines the background of PHI, its relevance to perioperative care, and conceives intersections with PHI that could evolve over the next quarter century.
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Affiliation(s)
- Seshadri C. Mudumbai
- Anesthesiology and Perioperative Care Service, Veterans Affairs Palo Alto Health Care System
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine
| | - Rodney A. Gabriel
- Department of Anesthesiology, University of California, San Diego, California
| | | | - Jonathan M. Tan
- Department of Anesthesiology Critical Care Medicine, Children’s Hospital Los Angeles
- Department of Anesthesiology, Keck School of Medicine at the University of Southern California
- Spatial Sciences Institute at the University of Southern California
| | - Robert E. Freundlich
- Department of Anesthesiology, Surgery, and Biomedical Informatics, Vanderbilt University Medical Center
| | | | - Samir Kendale
- Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center
| | - Karl Poterack
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic
| | - Brian S. Rothman
- Department of Anesthesiology, Surgery, and Biomedical Informatics, Vanderbilt University Medical Center
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MacIntyre CR, Chen X, Kunasekaran M, Quigley A, Lim S, Stone H, Paik HY, Yao L, Heslop D, Wei W, Sarmiento I, Gurdasani D. Artificial intelligence in public health: the potential of epidemic early warning systems. J Int Med Res 2023; 51:3000605231159335. [PMID: 36967669 PMCID: PMC10052500 DOI: 10.1177/03000605231159335] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023] Open
Abstract
The use of artificial intelligence (AI) to generate automated early warnings in epidemic surveillance by harnessing vast open-source data with minimal human intervention has the potential to be both revolutionary and highly sustainable. AI can overcome the challenges faced by weak health systems by detecting epidemic signals much earlier than traditional surveillance. AI-based digital surveillance is an adjunct to-not a replacement of-traditional surveillance and can trigger early investigation, diagnostics and responses at the regional level. This narrative review focuses on the role of AI in epidemic surveillance and summarises several current epidemic intelligence systems including ProMED-mail, HealthMap, Epidemic Intelligence from Open Sources, BlueDot, Metabiota, the Global Biosurveillance Portal, Epitweetr and EPIWATCH. Not all of these systems are AI-based, and some are only accessible to paid users. Most systems have large volumes of unfiltered data; only a few can sort and filter data to provide users with curated intelligence. However, uptake of these systems by public health authorities, who have been slower to embrace AI than their clinical counterparts, is low. The widespread adoption of digital open-source surveillance and AI technology is needed for the prevention of serious epidemics.
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Affiliation(s)
- Chandini Raina MacIntyre
- Biosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia
- College of Public Service & Community Solutions, Arizona State University, Tempe, United States
| | - Xin Chen
- Biosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Mohana Kunasekaran
- Biosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Ashley Quigley
- Biosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Samsung Lim
- Biosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia
- School of Civil and Environmental Engineering, University of New South Wales, Sydney, Australia
| | - Haley Stone
- Biosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Hye-Young Paik
- School of Computer Science and Engineering, Faulty of Engineering, University of New South Wales, Sydney, Australia
| | - Lina Yao
- School of Computer Science and Engineering, Faulty of Engineering, University of New South Wales, Sydney, Australia
| | - David Heslop
- School of Population Health, Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Wenzhao Wei
- Biosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Ines Sarmiento
- Biosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Deepti Gurdasani
- William Harvey Research Institute, Queen Mary University of London, United Kingdom
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Wearable body sensor network: SDGs panacea for an holistic SARS-CoV-2 mitigation, diagnostic, therapeutic, and health informatics interventions. COVID-19 AND THE SUSTAINABLE DEVELOPMENT GOALS 2022. [PMCID: PMC9335063 DOI: 10.1016/b978-0-323-91307-2.00010-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
The COVID-19 pandemic offers enormous potential for digital health solutions, with social distancing being the only current approach that has thus far been established to reduce the risk. Wearable Activity Tracker (WATs) is an appealing, successful, and inexpensive choice in this context. This technology is of vital significance in ensuring the well-being of both patients and healthcare professionals following sustainable development goals. The application of technological interventions will in no doubt help in tackling the debilitating effect of health emergencies in the world population. However, there is a shortage of literature explaining the programming resources needed to effectively treat this novel infection which is briefly addressed in this review. This study prominently addressed the potential of wearable Activity Tracker as a technological solution to healthcare disaster management.
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Valdivia-Granda WA. Known and Unknown Transboundary Infectious Diseases as Hybrid Threats. Front Public Health 2021; 9:668062. [PMID: 34336765 PMCID: PMC8316594 DOI: 10.3389/fpubh.2021.668062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 06/07/2021] [Indexed: 11/13/2022] Open
Abstract
The pathogenicity, transmissibility, environmental stability, and potential for genetic manipulation make microbes hybrid threats that could blur the distinction between peace and war. These agents can fall below the detection, attribution, and response capabilities of a nation and seriously affect their health, trade, and security. A framework that could enhance horizon scanning regarding the potential risk of microbes used as hybrid threats requires not only accurately discriminating known and unknown pathogens but building novel scenarios to deploy mitigation strategies. This demands the transition of analyst-based biosurveillance tracking a narrow set of pathogens toward an autonomous biosurveillance enterprise capable of processing vast data streams beyond human cognitive capabilities. Autonomous surveillance systems must gather, integrate, analyze, and visualize billions of data points from different and unrelated sources. Machine learning and artificial intelligence algorithms can contextualize capability information for different stakeholders at different levels of resolution: strategic and tactical. This document provides a discussion of the use of microorganisms as hybrid threats and considerations to quantitatively estimate their risk to ensure societal awareness, preparedness, mitigation, and resilience.
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Feldman J, Thomas-Bachli A, Forsyth J, Patel ZH, Khan K. Development of a global infectious disease activity database using natural language processing, machine learning, and human expertise. J Am Med Inform Assoc 2021; 26:1355-1359. [PMID: 31361300 DOI: 10.1093/jamia/ocz112] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Revised: 05/28/2019] [Accepted: 06/04/2019] [Indexed: 01/25/2023] Open
Abstract
OBJECTIVE We assessed whether machine learning can be utilized to allow efficient extraction of infectious disease activity information from online media reports. MATERIALS AND METHODS We curated a data set of labeled media reports (n = 8322) indicating which articles contain updates about disease activity. We trained a classifier on this data set. To validate our system, we used a held out test set and compared our articles to the World Health Organization Disease Outbreak News reports. RESULTS Our classifier achieved a recall and precision of 88.8% and 86.1%, respectively. The overall surveillance system detected 94% of the outbreaks identified by the WHO covered by online media (89%) and did so 43.4 (IQR: 9.5-61) days earlier on average. DISCUSSION We constructed a global real-time disease activity database surveilling 114 illnesses and syndromes. We must further assess our system for bias, representativeness, granularity, and accuracy. CONCLUSION Machine learning, natural language processing, and human expertise can be used to efficiently identify disease activity from digital media reports.
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Affiliation(s)
- Joshua Feldman
- Harvard University, School of Engineering and Applied Sciences, Cambridge, Massachusetts, USA
| | - Andrea Thomas-Bachli
- Li Ka Shing Knowledge Institute, St. Michaels Hospital, Toronto, Ontario, Canada.,BlueDot, Toronto, Ontario, Canada
| | - Jack Forsyth
- Li Ka Shing Knowledge Institute, St. Michaels Hospital, Toronto, Ontario, Canada.,BlueDot, Toronto, Ontario, Canada
| | - Zaki Hasnain Patel
- Li Ka Shing Knowledge Institute, St. Michaels Hospital, Toronto, Ontario, Canada.,BlueDot, Toronto, Ontario, Canada
| | - Kamran Khan
- Li Ka Shing Knowledge Institute, St. Michaels Hospital, Toronto, Ontario, Canada.,BlueDot, Toronto, Ontario, Canada.,Division of Infectious Diseases, Department of Medicine, University of Toronto, Toronto, Ontario, Canada
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What Can COVID-19 Teach Us about Using AI in Pandemics? Healthcare (Basel) 2020; 8:healthcare8040527. [PMID: 33271960 PMCID: PMC7711608 DOI: 10.3390/healthcare8040527] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 11/23/2020] [Accepted: 11/24/2020] [Indexed: 12/17/2022] Open
Abstract
The COVID-19 pandemic put significant strain on societies and their resources, with the healthcare system and workers being particularly affected. Artificial Intelligence (AI) offers the unique possibility of improving the response to a pandemic as it emerges and evolves. Here, we utilize the WHO framework of a pandemic evolution to analyze the various AI applications. Specifically, we analyzed AI from the perspective of all five domains of the WHO pandemic response. To effectively review the current scattered literature, we organized a sample of relevant literature from various professional and popular resources. The article concludes with a consideration of AI’s weaknesses as key factors affecting AI in future pandemic preparedness and response.
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Adaptive Choropleth Mapper: An Open-Source Web-Based Tool for Synchronous Exploration of Multiple Variables at Multiple Spatial Extents. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2019. [DOI: 10.3390/ijgi8110509] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Choropleth mapping is an essential visualization technique for exploratory spatial data analysis. Visualizing multiple choropleth maps is a technique that spatial analysts use to reveal spatiotemporal patterns of one variable or to compare the geographical distributions of multiple variables. Critical features for effective exploration of multiple choropleth maps are (1) automated computation of the same class intervals for shading different choropleth maps, (2) dynamic visualization of local variation in a variable, and (3) linking for synchronous exploration of multiple choropleth maps. Since the 1990s, these features have been developed and are now included in many commercial geographic information system (GIS) software packages. However, many choropleth mapping tools include only one or two of the three features described above. On the other hand, freely available mapping tools that support side-by-side multiple choropleth map visualizations are usually desktop software only. As a result, most existing tools supporting multiple choropleth-map visualizations cannot be easily integrated with Web-based and open-source data visualization libraries, which have become mainstream in visual analytics and geovisualization. To fill this gap, we introduce an open-source Web-based choropleth mapping tool called the Adaptive Choropleth Mapper (ACM), which combines the three critical features for flexible choropleth mapping.
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Schwab-Reese LM, Hovdestad W, Tonmyr L, Fluke J. The potential use of social media and other internet-related data and communications for child maltreatment surveillance and epidemiological research: Scoping review and recommendations. CHILD ABUSE & NEGLECT 2018; 85:187-201. [PMID: 29366596 PMCID: PMC7112406 DOI: 10.1016/j.chiabu.2018.01.014] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Revised: 12/06/2017] [Accepted: 01/12/2018] [Indexed: 05/12/2023]
Abstract
Collecting child maltreatment data is a complicated undertaking for many reasons. As a result, there is an interest by child maltreatment researchers to develop methodologies that allow for the triangulation of data sources. To better understand how social media and internet-based technologies could contribute to these approaches, we conducted a scoping review to provide an overview of social media and internet-based methodologies for health research, to report results of evaluation and validation research on these methods, and to highlight studies with potential relevance to child maltreatment research and surveillance. Many approaches were identified in the broad health literature; however, there has been limited application of these approaches to child maltreatment. The most common use was recruiting participants or engaging existing participants using online methods. From the broad health literature, social media and internet-based approaches to surveillance and epidemiologic research appear promising. Many of the approaches are relatively low cost and easy to implement without extensive infrastructure, but there are also a range of limitations for each method. Several methods have a mixed record of validation and sources of error in estimation are not yet understood or predictable. In addition to the problems relevant to other health outcomes, child maltreatment researchers face additional challenges, including the complex ethical issues associated with both internet-based and child maltreatment research. If these issues are adequately addressed, social media and internet-based technologies may be a promising approach to reducing some of the limitations in existing child maltreatment data.
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Affiliation(s)
- Laura M Schwab-Reese
- The Kempe Center for The Prevention and Treatment of Child Abuse and Neglect, University of Colorado, Anschutz Medical Campus, 13123 E 16th Ave., Aurora, CO 80045, USA.
| | - Wendy Hovdestad
- Public Health Agency of Canada, 785 Carling Ave., Ottawa, ON, K1A 0K9, Canada
| | - Lil Tonmyr
- Public Health Agency of Canada, 785 Carling Ave., Ottawa, ON, K1A 0K9, Canada
| | - John Fluke
- The Kempe Center for The Prevention and Treatment of Child Abuse and Neglect, University of Colorado, Anschutz Medical Campus, 13123 E 16th Ave., Aurora, CO 80045, USA
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Beard R, Wentz E, Scotch M. A systematic review of spatial decision support systems in public health informatics supporting the identification of high risk areas for zoonotic disease outbreaks. Int J Health Geogr 2018; 17:38. [PMID: 30376842 PMCID: PMC6208014 DOI: 10.1186/s12942-018-0157-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Accepted: 10/19/2018] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Zoonotic diseases account for a substantial portion of infectious disease outbreaks and burden on public health programs to maintain surveillance and preventative measures. Taking advantage of new modeling approaches and data sources have become necessary in an interconnected global community. To facilitate data collection, analysis, and decision-making, the number of spatial decision support systems reported in the last 10 years has increased. This systematic review aims to describe characteristics of spatial decision support systems developed to assist public health officials in the management of zoonotic disease outbreaks. METHODS A systematic search of the Google Scholar database was undertaken for published articles written between 2008 and 2018, with no language restriction. A manual search of titles and abstracts using Boolean logic and keyword search terms was undertaken using predefined inclusion and exclusion criteria. Data extraction included items such as spatial database management, visualizations, and report generation. RESULTS For this review we screened 34 full text articles. Design and reporting quality were assessed, resulting in a final set of 12 articles which were evaluated on proposed interventions and identifying characteristics were described. Multisource data integration, and user centered design were inconsistently applied, though indicated diverse utilization of modeling techniques. CONCLUSIONS The characteristics, data sources, development and modeling techniques implemented in the design of recent SDSS that target zoonotic disease outbreak were described. There are still many challenges to address during the design process to effectively utilize the value of emerging data sources and modeling methods. In the future, development should adhere to comparable standards for functionality and system development such as user input for system requirements, and flexible interfaces to visualize data that exist on different scales. PROSPERO registration number: CRD42018110466.
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Affiliation(s)
- Rachel Beard
- College of Health Solutions, Arizona State University, Phoenix, AZ USA
- Center for Environmental Health Engineering, Biodesign Institute, Arizona State University, Tempe, AZ USA
| | - Elizabeth Wentz
- School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ USA
| | - Matthew Scotch
- College of Health Solutions, Arizona State University, Phoenix, AZ USA
- Center for Environmental Health Engineering, Biodesign Institute, Arizona State University, Tempe, AZ USA
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User rating activity within KIWI: A technology for public health event monitoring and early warning signal detection. Online J Public Health Inform 2018; 10:e205. [PMID: 30349623 PMCID: PMC6194104 DOI: 10.5210/ojphi.v10i2.8547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Objectives To review user signal rating activity within the Canadian Network for Public Health Intelligence's (CNPHI's) Knowledge Integration using Web-based Intelligence (KIWI) technology by answering the following questions: (1) who is rating, (2) how are users rating, and (3) how well are users rating? Methods KIWI rating data was extracted from the CNPHI platform. Zoonotic & Emerging program signals with first rating occurring between January 1, 2016 and December 31, 2017 were included. Krippendorff's alpha was used to estimate inter-rater reliability between users. A z-test was used to identify whether users tended to rate within 95% confidence interval (versus outside) the average community rating. Results The 37 users who rated signals represented 20 organizations. 27.0% (n = 10) of users rated ≥10% of all rated signals, and their inter-rater reliability estimate was 72.4% (95% CI: 66.5-77.9%). Five users tended to rate significantly outside of the average community rating. An average user rated 58.4% of the time within the signal's 95% CI. All users who significantly rated within the average community rating rated outside the 95% CI at least once. Discussion A diverse community of raters participated in rating the signals. Krippendorff's Alpha estimate revealed moderate reliability for users who rated ≥10% of signals. It was observed that inter-rater reliability increased for users with more experience rating signals. Conclusions Diversity was observed between user ratings. It is hypothesized that rating diversity is influenced by differences in user expertise and experience, and that the number of times a user rates within and outside of a signal's 95% CI can be used as a proxy for user expertise. The introduction of a weighted rating algorithm within KIWI that takes this into consideration could be beneficial.
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12
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Utility and potential of rapid epidemic intelligence from internet-based sources. Int J Infect Dis 2017; 63:77-87. [PMID: 28765076 DOI: 10.1016/j.ijid.2017.07.020] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2017] [Revised: 07/19/2017] [Accepted: 07/21/2017] [Indexed: 12/15/2022] Open
Abstract
OBJECTIVES Rapid epidemic detection is an important objective of surveillance to enable timely intervention, but traditional validated surveillance data may not be available in the required timeframe for acute epidemic control. Increasing volumes of data on the Internet have prompted interest in methods that could use unstructured sources to enhance traditional disease surveillance and gain rapid epidemic intelligence. We aimed to summarise Internet-based methods that use freely-accessible, unstructured data for epidemic surveillance and explore their timeliness and accuracy outcomes. METHODS Steps outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist were used to guide a systematic review of research related to the use of informal or unstructured data by Internet-based intelligence methods for surveillance. RESULTS We identified 84 articles published between 2006-2016 relating to Internet-based public health surveillance methods. Studies used search queries, social media posts and approaches derived from existing Internet-based systems for early epidemic alerts and real-time monitoring. Most studies noted improved timeliness compared to official reporting, such as in the 2014 Ebola epidemic where epidemic alerts were generated first from ProMED-mail. Internet-based methods showed variable correlation strength with official datasets, with some methods showing reasonable accuracy. CONCLUSION The proliferation of publicly available information on the Internet provided a new avenue for epidemic intelligence. Methodologies have been developed to collect Internet data and some systems are already used to enhance the timeliness of traditional surveillance systems. To improve the utility of Internet-based systems, the key attributes of timeliness and data accuracy should be included in future evaluations of surveillance systems.
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13
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Polgreen PM, Polgreen EL. Emerging and Re-emerging Pathogens and Diseases, and Health Consequences of a Changing Climate. Infect Dis (Lond) 2017. [PMCID: PMC7149782 DOI: 10.1016/b978-0-7020-6285-8.00004-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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Mukhi SN. KIWI: A technology for public health event monitoring and early warning signal detection. Online J Public Health Inform 2016; 8:e208. [PMID: 28210429 PMCID: PMC5302468 DOI: 10.5210/ojphi.v8i3.6937] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
OBJECTIVES To introduce the Canadian Network for Public Health Intelligence's new Knowledge Integration using Web-based Intelligence (KIWI) technology, and to pefrom preliminary evaluation of the KIWI technology using a case study. The purpose of this new technology is to support surveillance activities by monitoring unstructured data sources for the early detection and awareness of potential public health threats. METHODS A prototype of the KIWI technology, adapted for zoonotic and emerging diseases, was piloted by end-users with expertise in the field of public health and zoonotic/emerging disease surveillance. The technology was assessed using variables such as geographic coverage, user participation, and others; categorized by high-level attributes from evaluation guidelines for internet based surveillance systems. Special attention was given to the evaluation of the system's automated sense-making algorithm, which used variables such as sensitivity, specificity, and predictive values. Event-based surveillance evaluation was not applied to its full capacity as such an evaluation is beyond the scope of this paper. RESULTS KIWI was piloted with user participation = 85.0% and geographic coverage within monitored sources = 83.9% of countries. The pilots, which focused on zoonotic and emerging diseases, lasted a combined total of 65 days and resulted in the collection of 3243 individual information pieces (IIP) and 2 community reported events (CRE) for processing. Ten sources were monitored during the second phase of the pilot, which resulted in 545 anticipatory intelligence signals (AIS). KIWI's automated sense-making algorithm (SMA) had sensitivity = 63.9% (95% CI: 60.2-67.5%), specificity = 88.6% (95% CI: 87.3-89.8%), positive predictive value = 59.8% (95% CI: 56.1-63.4%), and negative predictive value = 90.3% (95% CI: 89.0-91.4%). DISCUSSION Literature suggests the need for internet based monitoring and surveillance systems that are customizable, integrated into collaborative networks of public health professionals, and incorporated into national surveillance activities. Results show that the KIWI technology is well posied to address some of the suggested challenges. A limitation of this study is that sample size for pilot participation was small for capturing overall readiness of integrating KIWI into regular surveillance activities. CONCLUSIONS KIWI is a customizable technology developed within an already thriving collaborative platform used by public health professionals, and performs well as a tool for discipline-specific event monitoring and early warning signal detection.
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Affiliation(s)
- Shamir N Mukhi
- Canadian Network for Public Health Intelligence, Public Health Agency of Canada
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15
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Ao TT, Rahman M, Haque F, Chakraborty A, Hossain MJ, Haider S, Alamgir ASM, Sobel J, Luby SP, Gurley ES. Low-Cost National Media-Based Surveillance System for Public Health Events, Bangladesh. Emerg Infect Dis 2016; 22:720-2. [PMID: 26981877 PMCID: PMC4806969 DOI: 10.3201/eid2204.150330] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
We assessed a media-based public health surveillance system in Bangladesh during 2010-2011. The system is a highly effective, low-cost, locally appropriate, and sustainable outbreak detection tool that could be used in other low-income, resource-poor settings to meet the capacity for surveillance outlined in the International Health Regulations 2005.
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Choi J, Cho Y, Shim E, Woo H. Web-based infectious disease surveillance systems and public health perspectives: a systematic review. BMC Public Health 2016; 16:1238. [PMID: 27931204 PMCID: PMC5146908 DOI: 10.1186/s12889-016-3893-0] [Citation(s) in RCA: 68] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2016] [Accepted: 11/30/2016] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND Emerging and re-emerging infectious diseases are a significant public health concern, and early detection and immediate response is crucial for disease control. These challenges have led to the need for new approaches and technologies to reinforce the capacity of traditional surveillance systems for detecting emerging infectious diseases. In the last few years, the availability of novel web-based data sources has contributed substantially to infectious disease surveillance. This study explores the burgeoning field of web-based infectious disease surveillance systems by examining their current status, importance, and potential challenges. METHODS A systematic review framework was applied to the search, screening, and analysis of web-based infectious disease surveillance systems. We searched PubMed, Web of Science, and Embase databases to extensively review the English literature published between 2000 and 2015. Eleven surveillance systems were chosen for evaluation according to their high frequency of application. Relevant terms, including newly coined terms, development and classification of the surveillance systems, and various characteristics associated with the systems were studied. RESULTS Based on a detailed and informative review of the 11 web-based infectious disease surveillance systems, it was evident that these systems exhibited clear strengths, as compared to traditional surveillance systems, but with some limitations yet to be overcome. The major strengths of the newly emerging surveillance systems are that they are intuitive, adaptable, low-cost, and operated in real-time, all of which are necessary features of an effective public health tool. The most apparent potential challenges of the web-based systems are those of inaccurate interpretation and prediction of health status, and privacy issues, based on an individual's internet activity. CONCLUSION Despite being in a nascent stage with further modification needed, web-based surveillance systems have evolved to complement traditional national surveillance systems. This review highlights ways in which the strengths of existing systems can be maintained and weaknesses alleviated to implement optimal web surveillance systems.
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Affiliation(s)
- Jihye Choi
- Department of Public Health Science, School of Public Health, Seoul National University, 1 Kwanak-ro, Kwanak-gu, Seoul, South Korea
| | - Youngtae Cho
- Department of Public Health Science, School of Public Health, Seoul National University, 1 Kwanak-ro, Kwanak-gu, Seoul, South Korea
| | - Eunyoung Shim
- Department of Public Health Science, School of Public Health, Seoul National University, 1 Kwanak-ro, Kwanak-gu, Seoul, South Korea
- Department of New Business, Samsung Fire and Marine Insurance, 14 Seocho-daero 74-gil, Seocho-gu, Seoul, South Korea
| | - Hyekyung Woo
- Department of Public Health Science, School of Public Health, Seoul National University, 1 Kwanak-ro, Kwanak-gu, Seoul, South Korea
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Using Technology in Biosurveillance and Epidemic Management. Comput Inform Nurs 2016; 34:485-489. [DOI: 10.1097/cin.0000000000000300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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18
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Abat C, Chaudet H, Rolain JM, Colson P, Raoult D. Traditional and syndromic surveillance of infectious diseases and pathogens. Int J Infect Dis 2016; 48:22-8. [PMID: 27143522 PMCID: PMC7110877 DOI: 10.1016/j.ijid.2016.04.021] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2016] [Revised: 04/25/2016] [Accepted: 04/26/2016] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Infectious diseases remain a major public health problem worldwide. Hence, their surveillance is critical. Currently, many surveillance strategies and systems are in use around the world. An inventory of the data, surveillance strategies, and surveillance systems developed worldwide for the surveillance of infectious diseases is presented herein, with emphasis on the role of the microbiology laboratory in surveillance. METHODS The data, strategies, and systems used around the world for the surveillance of infectious diseases and pathogens, along with current issues and trends, were reviewed. RESULTS Twelve major classes of data were identified on the basis of their timing relative to infection, resources available, and type of surveillance. Two primary strategies were compared: disease-specific surveillance and syndromic surveillance. Finally, 262 systems implemented worldwide for the surveillance of infections were registered and briefly described, with a focus on those based on microbiological data from laboratories. CONCLUSIONS There is currently a wealth of available data on infections, which has been growing with the recent emergence of new technologies. Concurrently with the expansion of computer resources and networks, these data will allow the optimization of real-time detection and notification of infections.
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Affiliation(s)
- Cédric Abat
- Aix-Marseille Université, URMITE UM 63 CNRS 7278 IRD 198 INSERM U1905, Facultés de Médecine et de Pharmacie, 27 boulevard Jean Moulin, 13385 Marseille Cedex 05, France
| | - Hervé Chaudet
- Aix Marseille Université, SESSTIM UMR 912 INSERM, Marseille, France
| | - Jean-Marc Rolain
- Aix-Marseille Université, URMITE UM 63 CNRS 7278 IRD 198 INSERM U1905, Facultés de Médecine et de Pharmacie, 27 boulevard Jean Moulin, 13385 Marseille Cedex 05, France
| | - Philippe Colson
- Aix-Marseille Université, URMITE UM 63 CNRS 7278 IRD 198 INSERM U1905, Facultés de Médecine et de Pharmacie, 27 boulevard Jean Moulin, 13385 Marseille Cedex 05, France
| | - Didier Raoult
- Aix-Marseille Université, URMITE UM 63 CNRS 7278 IRD 198 INSERM U1905, Facultés de Médecine et de Pharmacie, 27 boulevard Jean Moulin, 13385 Marseille Cedex 05, France.
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Huang DC, Wang JF, Huang JX, Sui DZ, Zhang HY, Hu MG, Xu CD. Towards Identifying and Reducing the Bias of Disease Information Extracted from Search Engine Data. PLoS Comput Biol 2016; 12:e1004876. [PMID: 27271698 PMCID: PMC4894584 DOI: 10.1371/journal.pcbi.1004876] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2015] [Accepted: 03/17/2016] [Indexed: 11/19/2022] Open
Abstract
The estimation of disease prevalence in online search engine data (e.g., Google Flu Trends (GFT)) has received a considerable amount of scholarly and public attention in recent years. While the utility of search engine data for disease surveillance has been demonstrated, the scientific community still seeks ways to identify and reduce biases that are embedded in search engine data. The primary goal of this study is to explore new ways of improving the accuracy of disease prevalence estimations by combining traditional disease data with search engine data. A novel method, Biased Sentinel Hospital-based Area Disease Estimation (B-SHADE), is introduced to reduce search engine data bias from a geographical perspective. To monitor search trends on Hand, Foot and Mouth Disease (HFMD) in Guangdong Province, China, we tested our approach by selecting 11 keywords from the Baidu index platform, a Chinese big data analyst similar to GFT. The correlation between the number of real cases and the composite index was 0.8. After decomposing the composite index at the city level, we found that only 10 cities presented a correlation of close to 0.8 or higher. These cities were found to be more stable with respect to search volume, and they were selected as sample cities in order to estimate the search volume of the entire province. After the estimation, the correlation improved from 0.8 to 0.864. After fitting the revised search volume with historical cases, the mean absolute error was 11.19% lower than it was when the original search volume and historical cases were combined. To our knowledge, this is the first study to reduce search engine data bias levels through the use of rigorous spatial sampling strategies.
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Affiliation(s)
- Da-Cang Huang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resource Research, Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Surveillance and Early Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jin-Feng Wang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resource Research, Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Surveillance and Early Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China
- * E-mail:
| | - Ji-Xia Huang
- College of Forestry, Beijing Forestry University, Beijing, China
| | - Daniel Z. Sui
- Department of Geography, The Ohio State University, Columbus, Ohio, United States of America
| | - Hong-Yan Zhang
- School of Geographical Science, Northeast Normal University, Changchun, China
| | - Mao-Gui Hu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resource Research, Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Surveillance and Early Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Cheng-Dong Xu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resource Research, Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Surveillance and Early Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China
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Bello-Orgaz G, Jung JJ, Camacho D. Social big data: Recent achievements and new challenges. AN INTERNATIONAL JOURNAL ON INFORMATION FUSION 2016; 28:45-59. [PMID: 32288689 PMCID: PMC7106299 DOI: 10.1016/j.inffus.2015.08.005] [Citation(s) in RCA: 105] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2015] [Revised: 08/07/2015] [Accepted: 08/17/2015] [Indexed: 05/04/2023]
Abstract
Big data has become an important issue for a large number of research areas such as data mining, machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The rise of different big data frameworks such as Apache Hadoop and, more recently, Spark, for massive data processing based on the MapReduce paradigm has allowed for the efficient utilisation of data mining methods and machine learning algorithms in different domains. A number of libraries such as Mahout and SparkMLib have been designed to develop new efficient applications based on machine learning algorithms. The combination of big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas as social media and social networks. These new challenges are focused mainly on problems such as data processing, data storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and tracking data, among others. In this paper, we present a revision of the new methodologies that is designed to allow for efficient data mining and information fusion from social media and of the new applications and frameworks that are currently appearing under the "umbrella" of the social networks, social media and big data paradigms.
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Affiliation(s)
- Gema Bello-Orgaz
- Computer Science Department, Universidad Autónoma de Madrid, Spain
| | - Jason J. Jung
- Department of Computer Engineering, Chung-Ang University, Seoul, Republic of Korea
| | - David Camacho
- Computer Science Department, Universidad Autónoma de Madrid, Spain
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Chorianopoulos K, Talvis K. Flutrack.org: Open-source and linked data for epidemiology. Health Informatics J 2015; 22:962-974. [PMID: 26351261 DOI: 10.1177/1460458215599822] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Epidemiology has made advances, thanks to the availability of real-time surveillance data and by leveraging the geographic analysis of incidents. There are many health information systems that visualize the symptoms of influenza-like illness on a digital map, which is suitable for end-users, but it does not afford further processing and analysis. Existing systems have emphasized the collection, analysis, and visualization of surveillance data, but they have neglected a modular and interoperable design that integrates high-resolution geo-location with real-time data. As a remedy, we have built an open-source project and we have been operating an open service that detects flu-related symptoms and shares the data in real-time with anyone who wants to built upon this system. An analysis of a small number of precisely geo-located status updates (e.g. Twitter) correlates closely with the Google Flu Trends and the Centers for Disease Control and Prevention flu-positive reports. We suggest that public health information systems should embrace an open-source approach and offer linked data, in order to facilitate the development of an ecosystem of applications and services, and in order to be transparent to the general public interest.
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22
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Christaki E. New technologies in predicting, preventing and controlling emerging infectious diseases. Virulence 2015; 6:558-65. [PMID: 26068569 DOI: 10.1080/21505594.2015.1040975] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Surveillance of emerging infectious diseases is vital for the early identification of public health threats. Emergence of novel infections is linked to human factors such as population density, travel and trade and ecological factors like climate change and agricultural practices. A wealth of new technologies is becoming increasingly available for the rapid molecular identification of pathogens but also for the more accurate monitoring of infectious disease activity. Web-based surveillance tools and epidemic intelligence methods, used by all major public health institutions, are intended to facilitate risk assessment and timely outbreak detection. In this review, we present new methods for regional and global infectious disease surveillance and advances in epidemic modeling aimed to predict and prevent future infectious diseases threats.
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Affiliation(s)
- Eirini Christaki
- a Hellenic Center for Disease Control and Prevention; First Department of Internal Medicine; AHEPA University Hospital ; Thessaloniki , Greece.,b Infectious Diseases Division; Alpert School of Medicine of Brown University ; Providence , RI USA
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Gittelman S, Lange V, Gotway Crawford CA, Okoro CA, Lieb E, Dhingra SS, Trimarchi E. A new source of data for public health surveillance: Facebook likes. J Med Internet Res 2015; 17:e98. [PMID: 25895907 PMCID: PMC4419195 DOI: 10.2196/jmir.3970] [Citation(s) in RCA: 77] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2014] [Revised: 02/23/2015] [Accepted: 03/02/2015] [Indexed: 12/05/2022] Open
Abstract
Background Investigation into personal health has become focused on conditions at an increasingly local level, while response rates have declined and complicated the process of collecting data at an individual level. Simultaneously, social media data have exploded in availability and have been shown to correlate with the prevalence of certain health conditions. Objective Facebook likes may be a source of digital data that can complement traditional public health surveillance systems and provide data at a local level. We explored the use of Facebook likes as potential predictors of health outcomes and their behavioral determinants. Methods We performed principal components and regression analyses to examine the predictive qualities of Facebook likes with regard to mortality, diseases, and lifestyle behaviors in 214 counties across the United States and 61 of 67 counties in Florida. These results were compared with those obtainable from a demographic model. Health data were obtained from both the 2010 and 2011 Behavioral Risk Factor Surveillance System (BRFSS) and mortality data were obtained from the National Vital Statistics System. Results Facebook likes added significant value in predicting most examined health outcomes and behaviors even when controlling for age, race, and socioeconomic status, with model fit improvements (adjusted R2) of an average of 58% across models for 13 different health-related metrics over basic sociodemographic models. Small area data were not available in sufficient abundance to test the accuracy of the model in estimating health conditions in less populated markets, but initial analysis using data from Florida showed a strong model fit for obesity data (adjusted R2=.77). Conclusions Facebook likes provide estimates for examined health outcomes and health behaviors that are comparable to those obtained from the BRFSS. Online sources may provide more reliable, timely, and cost-effective county-level data than that obtainable from traditional public health surveillance systems as well as serve as an adjunct to those systems.
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Stevens KB, Pfeiffer DU. Sources of spatial animal and human health data: Casting the net wide to deal more effectively with increasingly complex disease problems. Spat Spatiotemporal Epidemiol 2015; 13:15-29. [PMID: 26046634 PMCID: PMC7102771 DOI: 10.1016/j.sste.2015.04.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2015] [Accepted: 04/28/2015] [Indexed: 12/29/2022]
Abstract
During the last 30years it has become commonplace for epidemiological studies to collect locational attributes of disease data. Although this advancement was driven largely by the introduction of handheld global positioning systems (GPS), and more recently, smartphones and tablets with built-in GPS, the collection of georeferenced disease data has moved beyond the use of handheld GPS devices and there now exist numerous sources of crowdsourced georeferenced disease data such as that available from georeferencing of Google search queries or Twitter messages. In addition, cartography has moved beyond the realm of professionals to crowdsourced mapping projects that play a crucial role in disease control and surveillance of outbreaks such as the 2014 West Africa Ebola epidemic. This paper provides a comprehensive review of a range of innovative sources of spatial animal and human health data including data warehouses, mHealth, Google Earth, volunteered geographic information and mining of internet-based big data sources such as Google and Twitter. We discuss the advantages, limitations and applications of each, and highlight studies where they have been used effectively.
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Affiliation(s)
- Kim B Stevens
- Veterinary Epidemiology, Economics and Public Health Group, Dept. of Production & Population Health, Royal Veterinary College, London, United Kingdom.
| | - Dirk U Pfeiffer
- Veterinary Epidemiology, Economics and Public Health Group, Dept. of Production & Population Health, Royal Veterinary College, London, United Kingdom.
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Gajewski KN, Peterson AE, Chitale RA, Pavlin JA, Russell KL, Chretien JP. A review of evaluations of electronic event-based biosurveillance systems. PLoS One 2014; 9:e111222. [PMID: 25329886 PMCID: PMC4203831 DOI: 10.1371/journal.pone.0111222] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2014] [Accepted: 09/12/2014] [Indexed: 11/18/2022] Open
Abstract
Electronic event-based biosurveillance systems (EEBS’s) that use near real-time information from the internet are an increasingly important source of epidemiologic intelligence. However, there has not been a systematic assessment of EEBS evaluations, which could identify key uncertainties about current systems and guide EEBS development to most effectively exploit web-based information for biosurveillance. To conduct this assessment, we searched PubMed and Google Scholar to identify peer-reviewed evaluations of EEBS’s. We included EEBS’s that use publicly available internet information sources, cover events that are relevant to human health, and have global scope. To assess the publications using a common framework, we constructed a list of 17 EEBS attributes from published guidelines for evaluating health surveillance systems. We identified 11 EEBS’s and 20 evaluations of these EEBS’s. The number of published evaluations per EEBS ranged from 1 (Gen-Db, GODsN, MiTAP) to 8 (GPHIN, HealthMap). The median number of evaluation variables assessed per EEBS was 8 (range, 3–15). Ten published evaluations contained quantitative assessments of at least one key variable. No evaluations examined usefulness by identifying specific public health decisions, actions, or outcomes resulting from EEBS outputs. Future EEBS assessments should identify and discuss critical indicators of public health utility, especially the impact of EEBS’s on public health response.
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Affiliation(s)
- Kimberly N. Gajewski
- Response Directorate, Federal Emergency Management Agency, Washington, DC, United States of America
| | - Amy E. Peterson
- Division of Integrated Biosurveillance, Armed Forces Health Surveillance Center, Silver Spring, MD, United States of America
| | - Rohit A. Chitale
- Division of Integrated Biosurveillance, Armed Forces Health Surveillance Center, Silver Spring, MD, United States of America
| | - Julie A. Pavlin
- Headquarters, Armed Forces Health Surveillance Center, Silver Spring, MD, United States of America
| | - Kevin L. Russell
- Headquarters, Armed Forces Health Surveillance Center, Silver Spring, MD, United States of America
| | - Jean-Paul Chretien
- Division of Integrated Biosurveillance, Armed Forces Health Surveillance Center, Silver Spring, MD, United States of America
- * E-mail:
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Mao C, Wu XY, Fu XH, Di MY, Yu YY, Yuan JQ, Yang ZY, Tang JL. An internet-based epidemiological investigation of the outbreak of H7N9 Avian influenza A in China since early 2013. J Med Internet Res 2014; 16:e221. [PMID: 25257217 PMCID: PMC4211021 DOI: 10.2196/jmir.3763] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2014] [Accepted: 09/06/2014] [Indexed: 11/30/2022] Open
Abstract
Background In early 2013, a new type of avian influenza, H7N9, emerged in China. It quickly became an issue of great public concern and a widely discussed topic on the Internet. A considerable volume of relevant information was made publicly available on the Internet through various sources. Objective This study aimed to describe the outbreak of H7N9 in China based on data openly available on the Internet and to validate our investigation by comparing our findings with a well-conducted conventional field epidemiologic study. Methods We searched publicly accessible Internet data on the H7N9 outbreak primarily from government and major mass media websites in China up to February 10, 2014. Two researchers independently extracted, compared, and confirmed the information of each confirmed H7N9 case using a self-designed data extraction form. We summarized the epidemiological and clinical characteristics of confirmed H7N9 cases and compared them with those from the field study. Results According to our data updated until February 10, 2014, 334 confirmed H7N9 cases were identified. The median age was 58 years and 67.0% (219/327) were males. Cases were reported in 15 regions in China. Five family clusters were found. Of the 16.8% (56/334) of the cases with relevant data, 69.6% (39/56) reported a history of exposure to animals. Of the 1751 persons with a close contact with a confirmed case, 0.6% (11/1751) of them developed respiratory symptoms during the 7-day surveillance period. In the 97.9% (327/334) of the cases with relevant data, 21.7% (71/327) died, 20.8% (68/327) were discharged from a hospital, and 57.5% (188/327) were of uncertain status. We compared our findings before February 10, 2014 and those before December 1, 2013 with those from the conventional field study, which had the latter cutoff date of ours in data collection. Our study showed most epidemiological and clinical characteristics were similar to those in the field study, except for case fatality (71/327, 21.7% for our data before February 10; 45/138, 32.6% for our data before December 1; 47/139, 33.8% for the field study), time from illness onset to first medical care (4 days, 3 days, and 1 day), and time from illness onset to death (16.5 days, 17 days, and 21 days). Conclusions Findings from our Internet-based investigation were similar to those from the conventional field study in most epidemiological and clinical aspects of the outbreak. Importantly, publicly available Internet data are open to any interested researchers and can thus greatly facilitate the investigation and control of such outbreaks. With improved efforts for Internet data provision, Internet-based investigation has a great potential to become a quick, economical, novel approach to investigating sudden issues of great public concern that involve a relatively small number of cases like this H7N9 outbreak.
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Affiliation(s)
- Chen Mao
- School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, Hong Kong, China (Hong Kong)
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Liyanage H, de Lusignan S, Liaw ST, Kuziemsky CE, Mold F, Krause P, Fleming D, Jones S. Big Data Usage Patterns in the Health Care Domain: A Use Case Driven Approach Applied to the Assessment of Vaccination Benefits and Risks. Contribution of the IMIA Primary Healthcare Working Group. Yearb Med Inform 2014; 9:27-35. [PMID: 25123718 PMCID: PMC4287086 DOI: 10.15265/iy-2014-0016] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Generally benefits and risks of vaccines can be determined from studies carried out as part of regulatory compliance, followed by surveillance of routine data; however there are some rarer and more long term events that require new methods. Big data generated by increasingly affordable personalised computing, and from pervasive computing devices is rapidly growing and low cost, high volume, cloud computing makes the processing of these data inexpensive. OBJECTIVE To describe how big data and related analytical methods might be applied to assess the benefits and risks of vaccines. METHOD We reviewed the literature on the use of big data to improve health, applied to generic vaccine use cases, that illustrate benefits and risks of vaccination. We defined a use case as the interaction between a user and an information system to achieve a goal. We used flu vaccination and pre-school childhood immunisation as exemplars. RESULTS We reviewed three big data use cases relevant to assessing vaccine benefits and risks: (i) Big data processing using crowdsourcing, distributed big data processing, and predictive analytics, (ii) Data integration from heterogeneous big data sources, e.g. the increasing range of devices in the "internet of things", and (iii) Real-time monitoring for the direct monitoring of epidemics as well as vaccine effects via social media and other data sources. CONCLUSIONS Big data raises new ethical dilemmas, though its analysis methods can bring complementary real-time capabilities for monitoring epidemics and assessing vaccine benefit-risk balance.
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Affiliation(s)
| | - S de Lusignan
- Simon de Lusignan, Clinical Informatics & Health Outcomes research group, Department of Health Care Policy and Management, University of Surrey, GUILDFORD, Surrey GU2 7XH, UK, E-mail:
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Yom-Tov E, Borsa D, Cox IJ, McKendry RA. Detecting disease outbreaks in mass gatherings using Internet data. J Med Internet Res 2014; 16:e154. [PMID: 24943128 PMCID: PMC4090384 DOI: 10.2196/jmir.3156] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2013] [Revised: 04/14/2014] [Accepted: 05/18/2014] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Mass gatherings, such as music festivals and religious events, pose a health care challenge because of the risk of transmission of communicable diseases. This is exacerbated by the fact that participants disperse soon after the gathering, potentially spreading disease within their communities. The dispersion of participants also poses a challenge for traditional surveillance methods. The ubiquitous use of the Internet may enable the detection of disease outbreaks through analysis of data generated by users during events and shortly thereafter. OBJECTIVE The intent of the study was to develop algorithms that can alert to possible outbreaks of communicable diseases from Internet data, specifically Twitter and search engine queries. METHODS We extracted all Twitter postings and queries made to the Bing search engine by users who repeatedly mentioned one of nine major music festivals held in the United Kingdom and one religious event (the Hajj in Mecca) during 2012, for a period of 30 days and after each festival. We analyzed these data using three methods, two of which compared words associated with disease symptoms before and after the time of the festival, and one that compared the frequency of these words with those of other users in the United Kingdom in the days following the festivals. RESULTS The data comprised, on average, 7.5 million tweets made by 12,163 users, and 32,143 queries made by 1756 users from each festival. Our methods indicated the statistically significant appearance of a disease symptom in two of the nine festivals. For example, cough was detected at higher than expected levels following the Wakestock festival. Statistically significant agreement (chi-square test, P<.01) between methods and across data sources was found where a statistically significant symptom was detected. Anecdotal evidence suggests that symptoms detected are indeed indicative of a disease that some users attributed to being at the festival. CONCLUSIONS Our work shows the feasibility of creating a public health surveillance system for mass gatherings based on Internet data. The use of multiple data sources and analysis methods was found to be advantageous for rejecting false positives. Further studies are required in order to validate our findings with data from public health authorities.
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Abstract
Background Social media platforms encourage people to share diverse aspects of their daily life. Among these, shared health related information might be used to infer health status and incidence rates for specific conditions or symptoms. In this work, we present an infodemiology study that evaluates the use of Twitter messages and search engine query logs to estimate and predict the incidence rate of influenza like illness in Portugal. Results Based on a manually classified dataset of 2704 tweets from Portugal, we selected a set of 650 textual features to train a Naïve Bayes classifier to identify tweets mentioning flu or flu-like illness or symptoms. We obtained a precision of 0.78 and an F-measure of 0.83, based on cross validation over the complete annotated set. Furthermore, we trained a multiple linear regression model to estimate the health-monitoring data from the Influenzanet project, using as predictors the relative frequencies obtained from the tweet classification results and from query logs, and achieved a correlation ratio of 0.89 (p < 0.001). These classification and regression models were also applied to estimate the flu incidence in the following flu season, achieving a correlation of 0.72. Conclusions Previous studies addressing the estimation of disease incidence based on user-generated content have mostly focused on the english language. Our results further validate those studies and show that by changing the initial steps of data preprocessing and feature extraction and selection, the proposed approaches can be adapted to other languages. Additionally, we investigated whether the predictive model created can be applied to data from the subsequent flu season. In this case, although the prediction result was good, an initial phase to adapt the regression model could be necessary to achieve more robust results.
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Bauer DC, Gaff C, Dinger ME, Caramins M, Buske FA, Fenech M, Hansen D, Cobiac L. Genomics and personalised whole-of-life healthcare. Trends Mol Med 2014; 20:479-86. [PMID: 24801560 DOI: 10.1016/j.molmed.2014.04.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2014] [Revised: 04/01/2014] [Accepted: 04/03/2014] [Indexed: 11/30/2022]
Abstract
Genome sequencing has the potential for stratified cancer treatment and improved diagnostics for rare disorders. However, sequencing needs to be utilised in risk stratification on a population scale to deepen the impact on the health system by addressing common diseases, where individual genomic variants have variable penetrance and minor impact. As the accuracy of genomic risk predictors is bounded by heritability, environmental factors such as diet, lifestyle, and microbiome have to be considered. Large-scale, longitudinal research programmes need to study the intrinsic properties between both genetics and environment to unravel their risk contribution. During this discovery process, frameworks need to be established to counteract unrealistic expectations. Sufficient scientific evidence is needed to interpret sources of uncertainty and inform decision making for clinical management and personal health.
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Affiliation(s)
- Denis C Bauer
- CSIRO Preventative Health Flagship and CSIRO Computational Informatics, Sydney, NSW 2113, Australia.
| | - Clara Gaff
- The Walter and Eliza Hall Institute, Parkville, VIC 3052, Australia
| | - Marcel E Dinger
- Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia
| | - Melody Caramins
- School of Medical Sciences, University of New South Wales, Sydney, Australia; Laverty/SDS Pathology, 60 Waterloo Road, North Ryde, NSW 2113, Australia
| | - Fabian A Buske
- Cancer Research Division, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia
| | - Michael Fenech
- CSIRO Preventative Health National Research Flagship, Adelaide, SA 5000, Australia; CSIRO Animal, Food, and Health Sciences, Gate 13 Kintore Avenue, Adelaide, SA 5000, Australia
| | - David Hansen
- Australian E-Health Research Centre and CSIRO Computational Informatics, Herston, QLD 4029, Australia
| | - Lynne Cobiac
- CSIRO Preventative Health National Research Flagship, Adelaide, SA 5000, Australia
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Barboza P, Vaillant L, Le Strat Y, Hartley DM, Nelson NP, Mawudeku A, Madoff LC, Linge JP, Collier N, Brownstein JS, Astagneau P. Factors influencing performance of internet-based biosurveillance systems used in epidemic intelligence for early detection of infectious diseases outbreaks. PLoS One 2014; 9:e90536. [PMID: 24599062 PMCID: PMC3944226 DOI: 10.1371/journal.pone.0090536] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2013] [Accepted: 02/01/2014] [Indexed: 11/19/2022] Open
Abstract
Background Internet-based biosurveillance systems have been developed to detect health threats using information available on the Internet, but system performance has not been assessed relative to end-user needs and perspectives. Method and Findings Infectious disease events from the French Institute for Public Health Surveillance (InVS) weekly international epidemiological bulletin published in 2010 were used to construct the gold-standard official dataset. Data from six biosurveillance systems were used to detect raw signals (infectious disease events from informal Internet sources): Argus, BioCaster, GPHIN, HealthMap, MedISys and ProMED-mail. Crude detection rates (C-DR), crude sensitivity rates (C-Se) and intrinsic sensitivity rates (I-Se) were calculated from multivariable regressions to evaluate the systems’ performance (events detected compared to the gold-standard) 472 raw signals (Internet disease reports) related to the 86 events included in the gold-standard data set were retrieved from the six systems. 84 events were detected before their publication in the gold-standard. The type of sources utilised by the systems varied significantly (p<0001). I-Se varied significantly from 43% to 71% (p = 0001) whereas other indicators were similar (C-DR: p = 020; C-Se, p = 013). I-Se was significantly associated with individual systems, types of system, languages, regions of occurrence, and types of infectious disease. Conversely, no statistical difference of C-DR was observed after adjustment for other variables. Conclusion Although differences could result from a biosurveillance system's conceptual design, findings suggest that the combined expertise amongst systems enhances early detection performance for detection of infectious diseases. While all systems showed similar early detection performance, systems including human moderation were found to have a 53% higher I-Se (p = 00001) after adjustment for other variables. Overall, the use of moderation, sources, languages, regions of occurrence, and types of cases were found to influence system performance.
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Affiliation(s)
- Philippe Barboza
- International Department, French Institute for Public Health Surveillance (Institut de Veille Sanitaire), Saint Maurice, France
- * E-mail:
| | - Laetitia Vaillant
- International Department, French Institute for Public Health Surveillance (Institut de Veille Sanitaire), Saint Maurice, France
| | - Yann Le Strat
- Infectious Department, French Institute for Public Health Surveillance (Institut de Veille Sanitaire), Saint Maurice, France
| | - David M. Hartley
- Department of Microbiology and Immunology, Georgetown University Medical Center, Washington, D.C, United States of America
- Imaging Science and Information Systems Center, Georgetown University School of Medicine, Washington, D.C, United States of America
| | - Noele P. Nelson
- Department of Pediatrics, Georgetown University Medical Center, Washington, D.C, United States of America
| | - Abla Mawudeku
- Centre for Emergency Preparedness and Response, Public Health Agency of Canada, Ottawa, Canada
| | - Lawrence C. Madoff
- ProMED-mail, International Society for Infectious Diseases, Boston, Massachusetts, United States of America
| | - Jens P. Linge
- Joint Research Centre of the European Commission, Ispra, Italy
| | - Nigel Collier
- National Institute of Informatics, Tokyo, Japan
- The European Bioinformatics Institute, Cambridge, United Kingdom
| | - John S. Brownstein
- Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Pascal Astagneau
- École des Hautes Études en Santé Publique (EHESP), University school of public Health, PRES Sorbonne Cité, Paris, France
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Prieto VM, Matos S, Álvarez M, Cacheda F, Oliveira JL. Twitter: a good place to detect health conditions. PLoS One 2014; 9:e86191. [PMID: 24489699 PMCID: PMC3906034 DOI: 10.1371/journal.pone.0086191] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2013] [Accepted: 12/10/2013] [Indexed: 11/19/2022] Open
Abstract
With the proliferation of social networks and blogs, the Internet is increasingly being used to disseminate personal health information rather than just as a source of information. In this paper we exploit the wealth of user-generated data, available through the micro-blogging service Twitter, to estimate and track the incidence of health conditions in society. The method is based on two stages: we start by extracting possibly relevant tweets using a set of specially crafted regular expressions, and then classify these initial messages using machine learning methods. Furthermore, we selected relevant features to improve the results and the execution times. To test the method, we considered four health states or conditions, namely flu, depression, pregnancy and eating disorders, and two locations, Portugal and Spain. We present the results obtained and demonstrate that the detection results and the performance of the method are improved after feature selection. The results are promising, with areas under the receiver operating characteristic curve between 0.7 and 0.9, and f-measure values around 0.8 and 0.9. This fact indicates that such approach provides a feasible solution for measuring and tracking the evolution of health states within the society.
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Affiliation(s)
- Víctor M. Prieto
- Department of Information and Communication Technologies, Campus Elviña s/n, Coruña, Spain
- * E-mail:
| | - Sérgio Matos
- University of Aveiro, DETI/IEETA, Campus Universitariá de Santiago, Aveiro, Portugal
| | - Manuel Álvarez
- Department of Information and Communication Technologies, Campus Elviña s/n, Coruña, Spain
| | - Fidel Cacheda
- Department of Information and Communication Technologies, Campus Elviña s/n, Coruña, Spain
| | - José Luís Oliveira
- University of Aveiro, DETI/IEETA, Campus Universitariá de Santiago, Aveiro, Portugal
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Margevicius KJ, Generous N, Taylor-McCabe KJ, Brown M, Daniel WB, Castro L, Hengartner A, Deshpande A. Advancing a framework to enable characterization and evaluation of data streams useful for biosurveillance. PLoS One 2014; 9:e83730. [PMID: 24392093 PMCID: PMC3879288 DOI: 10.1371/journal.pone.0083730] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2013] [Accepted: 11/15/2013] [Indexed: 11/26/2022] Open
Abstract
In recent years, biosurveillance has become the buzzword under which a diverse set of ideas and activities regarding detecting and mitigating biological threats are incorporated depending on context and perspective. Increasingly, biosurveillance practice has become global and interdisciplinary, requiring information and resources across public health, One Health, and biothreat domains. Even within the scope of infectious disease surveillance, multiple systems, data sources, and tools are used with varying and often unknown effectiveness. Evaluating the impact and utility of state-of-the-art biosurveillance is, in part, confounded by the complexity of the systems and the information derived from them. We present a novel approach conceptualizing biosurveillance from the perspective of the fundamental data streams that have been or could be used for biosurveillance and to systematically structure a framework that can be universally applicable for use in evaluating and understanding a wide range of biosurveillance activities. Moreover, the Biosurveillance Data Stream Framework and associated definitions are proposed as a starting point to facilitate the development of a standardized lexicon for biosurveillance and characterization of currently used and newly emerging data streams. Criteria for building the data stream framework were developed from an examination of the literature, analysis of information on operational infectious disease biosurveillance systems, and consultation with experts in the area of biosurveillance. To demonstrate utility, the framework and definitions were used as the basis for a schema of a relational database for biosurveillance resources and in the development and use of a decision support tool for data stream evaluation.
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Affiliation(s)
- Kristen J. Margevicius
- Defense Systems and Analysis Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Nicholas Generous
- Defense Systems and Analysis Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Kirsten J. Taylor-McCabe
- Biosciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Mac Brown
- Defense Systems and Analysis Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - W. Brent Daniel
- Defense Systems and Analysis Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Lauren Castro
- Defense Systems and Analysis Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Andrea Hengartner
- Defense Systems and Analysis Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Alina Deshpande
- Defense Systems and Analysis Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
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Scales D, Zelenev A, Brownstein JS. Quantifying the effect of media limitations on outbreak data in a global online web-crawling epidemic intelligence system, 2008-2011. EMERGING HEALTH THREATS JOURNAL 2013; 6:21621. [PMID: 24206612 PMCID: PMC3822088 DOI: 10.3402/ehtj.v6i0.21621] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/04/2013] [Revised: 09/16/2013] [Accepted: 09/19/2013] [Indexed: 11/14/2022]
Abstract
Background This is the first study quantitatively evaluating the effect that media-related limitations have on data from an automated epidemic intelligence system. Methods We modeled time series of HealthMap's two main data feeds, Google News and Moreover, to test for evidence of two potential limitations: first, human resources constraints, and second, high-profile outbreaks “crowding out” coverage of other infectious diseases. Results Google News events declined by 58.3%, 65.9%, and 14.7% on Saturday, Sunday and Monday, respectively, relative to other weekdays. Events were reduced by 27.4% during Christmas/New Years weeks and 33.6% lower during American Thanksgiving week than during an average week for Google News. Moreover data yielded similar results with the addition of Memorial Day (US) being associated with a 36.2% reduction in events. Other holiday effects were not statistically significant. We found evidence for a crowd out phenomenon for influenza/H1N1, where a 50% increase in influenza events corresponded with a 4% decline in other disease events for Google News only. Other prominent diseases in this database – avian influenza (H5N1), cholera, or foodborne illness – were not associated with a crowd out phenomenon. Conclusions These results provide quantitative evidence for the limited impact of editorial biases on HealthMap's web-crawling epidemic intelligence.
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Affiliation(s)
- David Scales
- Children's Hospital Informatics Program, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Center for Biomedical Informatics, Boston Children's Hospital, Harvard University, Boston, MA, USA;
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Williams SA, Terras M, Warwick C. How Twitter Is Studied in the Medical Professions: A Classification of Twitter Papers Indexed in PubMed. MEDICINE 2.0 2013; 2:e2. [PMID: 25075237 PMCID: PMC4084770 DOI: 10.2196/med20.2269] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2012] [Revised: 01/27/2013] [Accepted: 05/12/2013] [Indexed: 11/18/2022]
Abstract
Background Since their inception, Twitter and related microblogging systems have provided a rich source of information for researchers and have attracted interest in their affordances and use. Since 2009 PubMed has included 123 journal articles on medicine and Twitter, but no overview exists as to how the field uses Twitter in research. Objective This paper aims to identify published work relating to Twitter within the fields indexed by PubMed, and then to classify it. This classification will provide a framework in which future researchers will be able to position their work, and to provide an understanding of the current reach of research using Twitter in medical disciplines. Methods Papers on Twitter and related topics were identified and reviewed. The papers were then qualitatively classified based on the paper’s title and abstract to determine their focus. The work that was Twitter focused was studied in detail to determine what data, if any, it was based on, and from this a categorization of the data set size used in the studies was developed. Using open coded content analysis additional important categories were also identified, relating to the primary methodology, domain, and aspect. Results As of 2012, PubMed comprises more than 21 million citations from biomedical literature, and from these a corpus of 134 potentially Twitter related papers were identified, eleven of which were subsequently found not to be relevant. There were no papers prior to 2009 relating to microblogging, a term first used in 2006. Of the remaining 123 papers which mentioned Twitter, thirty were focused on Twitter (the others referring to it tangentially). The early Twitter focused papers introduced the topic and highlighted the potential, not carrying out any form of data analysis. The majority of published papers used analytic techniques to sort through thousands, if not millions, of individual tweets, often depending on automated tools to do so. Our analysis demonstrates that researchers are starting to use knowledge discovery methods and data mining techniques to understand vast quantities of tweets: the study of Twitter is becoming quantitative research. Conclusions This work is to the best of our knowledge the first overview study of medical related research based on Twitter and related microblogging. We have used 5 dimensions to categorize published medical related research on Twitter. This classification provides a framework within which researchers studying development and use of Twitter within medical related research, and those undertaking comparative studies of research, relating to Twitter in the area of medicine and beyond, can position and ground their work.
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Affiliation(s)
| | - Melissa Terras
- Department of Information Studies University College London London United Kingdom
| | - Claire Warwick
- Department of Information Studies University College London London United Kingdom
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Lyon A, Grossel G, Burgman M, Nunn M. Using internet intelligence to manage biosecurity risks: a case study for aquatic animal health. DIVERS DISTRIB 2013. [DOI: 10.1111/ddi.12057] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Affiliation(s)
- Aidan Lyon
- Department of Philosophy; University of Maryland; College Park; MD; USA
| | - Geoff Grossel
- Department of Agriculture, Fisheries and Forestry; Animal Biosecurity; Canberra; ACT; Australia
| | - Mark Burgman
- School of Botany; University of Melbourne; Parkville; Vic; Australia
| | - Mike Nunn
- Department of Agriculture, Fisheries and Forestry; Animal Biosecurity; Canberra; ACT; Australia
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37
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Barboza P, Vaillant L, Mawudeku A, Nelson NP, Hartley DM, Madoff LC, Linge JP, Collier N, Brownstein JS, Yangarber R, Astagneau P. Evaluation of epidemic intelligence systems integrated in the early alerting and reporting project for the detection of A/H5N1 influenza events. PLoS One 2013; 8:e57252. [PMID: 23472077 PMCID: PMC3589479 DOI: 10.1371/journal.pone.0057252] [Citation(s) in RCA: 58] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2012] [Accepted: 01/18/2013] [Indexed: 11/18/2022] Open
Abstract
The objective of Web-based expert epidemic intelligence systems is to detect health threats. The Global Health Security Initiative (GHSI) Early Alerting and Reporting (EAR) project was launched to assess the feasibility and opportunity for pooling epidemic intelligence data from seven expert systems. EAR participants completed a qualitative survey to document epidemic intelligence strategies and to assess perceptions regarding the systems performance. Timeliness and sensitivity were rated highly illustrating the value of the systems for epidemic intelligence. Weaknesses identified included representativeness, completeness and flexibility. These findings were corroborated by the quantitative analysis performed on signals potentially related to influenza A/H5N1 events occurring in March 2010. For the six systems for which this information was available, the detection rate ranged from 31% to 38%, and increased to 72% when considering the virtual combined system. The effective positive predictive values ranged from 3% to 24% and F1-scores ranged from 6% to 27%. System sensitivity ranged from 38% to 72%. An average difference of 23% was observed between the sensitivities calculated for human cases and epizootics, underlining the difficulties in developing an efficient algorithm for a single pathology. However, the sensitivity increased to 93% when the virtual combined system was considered, clearly illustrating complementarities between individual systems. The average delay between the detection of A/H5N1 events by the systems and their official reporting by WHO or OIE was 10.2 days (95% CI: 6.7-13.8). This work illustrates the diversity in implemented epidemic intelligence activities, differences in system's designs, and the potential added values and opportunities for synergy between systems, between users and between systems and users.
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Affiliation(s)
- Philippe Barboza
- International Department, French Institute for Public Health Surveillance (Institut de Veille Sanitaire), Saint Maurice, France.
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Hay SI, Battle KE, Pigott DM, Smith DL, Moyes CL, Bhatt S, Brownstein JS, Collier N, Myers MF, George DB, Gething PW. Global mapping of infectious disease. Philos Trans R Soc Lond B Biol Sci 2013; 368:20120250. [PMID: 23382431 PMCID: PMC3679597 DOI: 10.1098/rstb.2012.0250] [Citation(s) in RCA: 153] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The primary aim of this review was to evaluate the state of knowledge of the geographical distribution of all infectious diseases of clinical significance to humans. A systematic review was conducted to enumerate cartographic progress, with respect to the data available for mapping and the methods currently applied. The results helped define the minimum information requirements for mapping infectious disease occurrence, and a quantitative framework for assessing the mapping opportunities for all infectious diseases. This revealed that of 355 infectious diseases identified, 174 (49%) have a strong rationale for mapping and of these only 7 (4%) had been comprehensively mapped. A variety of ambitions, such as the quantification of the global burden of infectious disease, international biosurveillance, assessing the likelihood of infectious disease outbreaks and exploring the propensity for infectious disease evolution and emergence, are limited by these omissions. An overview of the factors hindering progress in disease cartography is provided. It is argued that rapid improvement in the landscape of infectious diseases mapping can be made by embracing non-conventional data sources, automation of geo-positioning and mapping procedures enabled by machine learning and information technology, respectively, in addition to harnessing labour of the volunteer ‘cognitive surplus’ through crowdsourcing.
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Affiliation(s)
- Simon I Hay
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, UK.
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Abstract
Simon Hay and colleagues discuss the potential and challenges of producing continually updated infectious disease risk maps using diverse and large volume data sources such as social media.
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Affiliation(s)
- Simon I Hay
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom.
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Collier N. Uncovering text mining: a survey of current work on web-based epidemic intelligence. Glob Public Health 2012; 7:731-49. [PMID: 22783909 PMCID: PMC3438486 DOI: 10.1080/17441692.2012.699975] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2011] [Revised: 03/06/2012] [Indexed: 12/01/2022]
Abstract
Real world pandemics such as SARS 2002 as well as popular fiction like the movie Contagion graphically depict the health threat of a global pandemic and the key role of epidemic intelligence (EI). While EI relies heavily on established indicator sources a new class of methods based on event alerting from unstructured digital Internet media is rapidly becoming acknowledged within the public health community. At the heart of automated information gathering systems is a technology called text mining. My contribution here is to provide an overview of the role that text mining technology plays in detecting epidemics and to synthesise my existing research on the BioCaster project.
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Abstract
UNLABELLED We present a novel public health database (GENI-DB) in which news events on the topic of over 176 infectious diseases and chemicals affecting human and animal health are compiled from surveillance of the global online news media in 10 languages. News event frequency data were gathered systematically through the BioCaster public health surveillance system from July 2009 to the present and is available to download by the research community for purposes of analyzing trends in the global burden of infectious diseases. Database search can be conducted by year, country, disease and language. AVAILABILITY The GENI-DB is freely available via a web portal at http://born.nii.ac.jp/.
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Affiliation(s)
- Nigel Collier
- National Institute of Informatics, ROIS, Tokyo, Japan.
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