@article{Bragazzi2020, abstract = {SARS-CoV2 is a novel coronavirus, responsible for the COVID-19 pandemic declared by the World Health Organization. Thanks to the latest advancements in the field of molecular and computational techniques and information and communication technologies (ICTs), artificial intelligence (AI) and Big Data can help in handling the huge, unprecedented amount of data derived from public health surveillance, real-time epidemic outbreaks monitoring, trend now-casting/forecasting, regular situation briefing and updating from governmental institutions and organisms, and health facility utilization information. The present review is aimed at overviewing the potential applications of AI and Big Data in the global effort to manage the pandemic. {\textcopyright} 2020 by the authors.}, annote = {Cited By :20 Export Date: 20 January 2021}, author = {Bragazzi, N L and Dai, H and Damiani, G and Behzadifar, M and Martini, M and Wu, J}, doi = {10.3390/ijerph17093176}, journal = {International Journal of Environmental Research and Public Health}, number = {9}, title = {{How big data and artificial intelligence can help better manage the covid-19 pandemic}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084327140{\&}doi=10.3390{\%}2Fijerph17093176{\&}partnerID=40{\&}md5=dc9ff3b44147deeb080df92f04898561}, volume = {17}, year = {2020} } @article{Bu2020, abstract = {Several population health big data projects have been initiated in the USA recently. These include the County Health Rankings {\&} Roadmaps (CHR) initiated in 2010, the 500 Cities Project initiated in 2016, and the City Health Dashboard project initiated in 2017. Such projects provide data on a range of factors that determine health—such as socioeconomic factors, behavioral factors, health care access, and environmental factors—either at the county or city level. They provided state-of-the-art data visualization and interaction tools so that clinicians, public health practitioners, and policymakers can easily understand population health data at the local level. However, these recent initiatives were all built from data collected using long-standing and extant public health surveillance systems from organizations such as the Centers for Disease Control and Prevention and the U.S. Census Bureau. This resulted in a large extent of similarity among different datasets and a potential waste of resources. This perspective article aims to elaborate on the diminishing returns of creating more population health datasets and propose potential ways to integrate with clinical care and research, driving insights bidirectionally, and utilizing advanced analytical tools to improve value in population health big data. {\textcopyright} 2020, Society of General Internal Medicine.}, annote = {Export Date: 20 January 2021}, author = {Bu, D D and Liu, S H and Liu, B and Li, Y}, doi = {10.1007/s11606-020-05869-0}, journal = {Journal of General Internal Medicine}, number = {11}, pages = {3342--3345}, title = {{Achieving Value in Population Health Big Data}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084464115{\&}doi=10.1007{\%}2Fs11606-020-05869-0{\&}partnerID=40{\&}md5=2b48d6fe48756bca7aa996f26a2767d9}, volume = {35}, year = {2020} } @article{Buckee2020, annote = {Cited By :9 Export Date: 20 January 2021}, author = {Buckee, C}, doi = {10.1016/S2589-7500(20)30059-5}, journal = {The Lancet Digital Health}, number = {5}, pages = {e218--e220}, title = {{Improving epidemic surveillance and response: big data is dead, long live big data}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082178682{\&}doi=10.1016{\%}2FS2589-7500{\%}2820{\%}2930059-5{\&}partnerID=40{\&}md5=1f7e519dcb5d95c0c6d51bf5fc7b8209}, volume = {2}, year = {2020} } @article{Chiolero2020, abstract = {Public health surveillance is the ongoing systematic collection, analysis and interpretation of data, closely integrated with the timely dissemination of the resulting information to those responsible for preventing and controlling disease and injury. With the rapid development of data science, encompassing big data and artificial intelligence, and with the exponential growth of accessible and highly heterogeneous health-related data, from healthcare providers to user-generated online content, the field of surveillance and health monitoring is changing rapidly. It is, therefore, the right time for a short glossary of key terms in public health surveillance, with an emphasis on new data-science developments in the field. {\textcopyright} Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.}, annote = {Cited By :3 Export Date: 20 January 2021}, author = {Chiolero, A and Chiolero, A and Chiolero, A and Chiolero, A and Buckeridge, D}, doi = {10.1136/jech-2018-211654}, journal = {Journal of Epidemiology and Community Health}, number = {7}, pages = {612--616}, title = {{Glossary for public health surveillance in the age of data science}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084155775{\&}doi=10.1136{\%}2Fjech-2018-211654{\&}partnerID=40{\&}md5=abe224c3c31badc38a841b20ae9cb50d}, volume = {74}, year = {2020} } @article{Degeling2020, abstract = {Background: Outbreaks of infectious disease cause serious and costly health and social problems. Two new technologies - pathogen whole genome sequencing (WGS) and Big Data analytics - promise to improve our capacity to detect and control outbreaks earlier, saving lives and resources. However, routinely using these technologies to capture more detailed and specific personal information could be perceived as intrusive and a threat to privacy. Method: Four community juries were convened in two demographically different Sydney municipalities and two regional cities in New South Wales, Australia (western Sydney, Wollongong, Tamworth, eastern Sydney) to elicit the views of well-informed community members on the acceptability and legitimacy of: making pathogen WGS and linked administrative data available for public health research using this information in concert with data linkage and machine learning to enhance communicable disease surveillance systems Fifty participants of diverse backgrounds, mixed genders and ages were recruited by random-digit-dialling and topic-blinded social-media advertising. Each jury was presented with balanced factual evidence supporting different expert perspectives on the potential benefits and costs of technologically enhanced public health research and communicable disease surveillance and given the opportunity to question experts. Results: Almost all jurors supported data linkage and WGS on routinely collected patient isolates for the purposes of public health research, provided standard de-identification practices were applied. However, allowing this information to be operationalised as a syndromic surveillance system was highly contentious with three juries voting in favour, and one against by narrow margins. For those in favour, support depended on several conditions related to system oversight and security being met. Those against were concerned about loss of privacy and did not trust Australian governments to run secure and effective systems. Conclusions: Participants across all four events strongly supported the introduction of data linkage and pathogenomics to public health research under current research governance structures. Combining pathogen WGS with event-based data surveillance systems, however, is likely to be controversial because of a lack of public trust, even when the potential public health benefits are clear. Any suggestion of private sector involvement or commercialisation of WGS or surveillance data was unanimously rejected. {\textcopyright} 2020 The Author(s).}, annote = {Cited By :3 Export Date: 20 January 2021}, author = {Degeling, C and Carter, S M and {Van Oijen}, A M and McAnulty, J and Sintchenko, V and Braunack-Mayer, A and Yarwood, T and Johnson, J and Gilbert, G L}, doi = {10.1186/s12910-020-00474-6}, journal = {BMC Medical Ethics}, number = {1}, title = {{Community perspectives on the benefits and risks of technologically enhanced communicable disease surveillance systems: A report on four community juries}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084030797{\&}doi=10.1186{\%}2Fs12910-020-00474-6{\&}partnerID=40{\&}md5=b479039e2785e0dee2d75ead4788da11}, volume = {21}, year = {2020} } @article{Mavragani2020, abstract = {Background: Web-based sources are increasingly employed in the analysis, detection, and forecasting of diseases and epidemics, and in predicting human behavior toward several health topics. This use of the internet has come to be known as infodemiology, a concept introduced by Gunther Eysenbach. Infodemiology and infoveillance studies use web-based data and have become an integral part of health informatics research over the past decade. Objective: The aim of this paper is to provide a scoping review of the state-of-the-art in infodemiology along with the background and history of the concept, to identify sources and health categories and topics, to elaborate on the validity of the employed methods, and to discuss the gaps identified in current research. Methods: The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed to extract the publications that fall under the umbrella of infodemiology and infoveillance from the JMIR, PubMed, and Scopus databases. A total of 338 documents were extracted for assessment. Results: Of the 338 studies, the vast majority (n=282, 83.4{\%}) were published with JMIR Publications. The Journal of Medical Internet Research features almost half of the publications (n=168, 49.7{\%}), and JMIR Public Health and Surveillance has more than one-fifth of the examined studies (n=74, 21.9{\%}). The interest in the subject has been increasing every year, with 2018 featuring more than one-fourth of the total publications (n=89, 26.3{\%}), and the publications in 2017 and 2018 combined accounted for more than half (n=171, 50.6{\%}) of the total number of publications in the last decade. The most popular source was Twitter with 45.0{\%} (n=152), followed by Google with 24.6{\%} (n=83), websites and platforms with 13.9{\%} (n=47), blogs and forums with 10.1{\%} (n=34), Facebook with 8.9{\%} (n=30), and other search engines with 5.6{\%} (n=19). As for the subjects examined, conditions and diseases with 17.2{\%} (n=58) and epidemics and outbreaks with 15.7{\%} (n=53) were the most popular categories identified in this review, followed by health care (n=39, 11.5{\%}), drugs (n=40, 10.4{\%}), and smoking and alcohol (n=29, 8.6{\%}). Conclusions: The field of infodemiology is becoming increasingly popular, employing innovative methods and approaches for health assessment. The use of web-based sources, which provide us with information that would not be accessible otherwise and tackles the issues arising from the time-consuming traditional methods, shows that infodemiology plays an important role in health informatics research. {\textcopyright} 2020 Journal of Medical Internet Research. All rights reserved.}, annote = {Cited By :12 Export Date: 20 January 2021}, author = {Mavragani, A}, doi = {10.2196/16206}, journal = {Journal of Medical Internet Research}, number = {4}, title = {{Infodemiology and infoveillance: Scoping review}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084168060{\&}doi=10.2196{\%}2F16206{\&}partnerID=40{\&}md5=f098a1e37c4450c279faf6fe0077973a}, volume = {22}, year = {2020} } @misc{Aiello2019, abstract = {Disease surveillance systems are a cornerstone of public health tracking and prevention. This review addresses the use, promise, perils, and ethics of social media- and Internet-based data collection for public health surveillance. Our review highlights untapped opportunities for integrating digital surveillance in public health and current applications that could be improved through better integration, validation, and clarity on rules surrounding ethical considerations. Promising developments include hybrid systems that couple traditional surveillance data with data from search queries, social media posts, and crowdsourcing. In the future, it will be important to identify opportunities for public and private partnerships, train public health experts in data science, reduce biases related to digital data (gathered from Internet use, wearable devices, etc.), and address privacy. We are on the precipice of an unprecedented opportunity to track, predict, and prevent global disease burdens in the population using digital data. Copyright {\textcopyright} 2020 by Annual Reviews.}, annote = {Cited By :11 Export Date: 20 January 2021}, author = {Aiello, A E and Renson, A and Zivich, P N}, booktitle = {Annual Review of Public Health}, doi = {10.1146/annurev-publhealth-040119-094402}, pages = {101--118}, title = {{Social media- and internet-based disease surveillance for public health}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082941553{\&}doi=10.1146{\%}2Fannurev-publhealth-040119-094402{\&}partnerID=40{\&}md5=3526fd22f50bf7d9b15148904b4bd505}, volume = {41}, year = {2019} } @article{Antoine-Moussiaux2019, abstract = {The economic evaluation of health surveillance systems and of health information is a methodological challenge, as for information systems in general. Main present threads are considering cost-effectiveness solutions, minimizing costs for a given technically required output, or cost-benefit analysis, balancing costs with economic benefits of duly informed public interventions. The latter option, following a linear command-and-control perspective, implies considering a main causal link between information, decision, action, and health benefits. Yet, valuing information, taking into account its nature and multiple sources, the modalities of its processing cycle, from production to diffusion, decentralized use and gradual building of a shared information capital, constitutes a promising challenge. This work proposes an interdisciplinary insight on the value of health surveillance to get a renewed theoretical framework integrating information and informatics theory and information economics. The reflection is based on a typological approach of value, basically distinguishing between use and non-use values. Through this structured discussion, the main idea is to expand the boundaries of surveillance evaluation, to focus on changes and trends, on the dynamic and networked structure of information systems, on the contribution of diverse data, and on the added value of combining qualitative and quantitative information. Distancing itself from the command-and-control model, this reflection considers the behavioral fundaments of many health risks, as well as the decentralized, progressive and deliberative dimension of decision-making in risk management. The framework also draws on lessons learnt from recent applications within and outside of health sector, as in surveillance of antimicrobial resistance, inter-laboratory networks, the use of big data or web sources, the diffusion of technological products and large-scale financial risks. Finally, the paper poses the bases to think the challenge of a workable approach to economic evaluation of health surveillance through a better understanding of health information value. It aims to avoid over-simplifying the range of health information benefits across society while keeping evaluation within the boundaries of what may be ascribed to the assessed information system. {\textcopyright} 2019 Antoine-Moussiaux, Vandenberg, Kozlakidis, Aenishaenslin, Peyre, Roche, Bonnet and Ravel.}, annote = {Cited By :1 Export Date: 20 January 2021}, author = {Antoine-Moussiaux, N and Vandenberg, O and Kozlakidis, Z and Aenishaenslin, C and Peyre, M and Roche, M and Bonnet, P and Ravel, A}, doi = {10.3389/fpubh.2019.00138}, journal = {Frontiers in Public Health}, number = {JUN}, title = {{Valuing health surveillance as an information system: Interdisciplinary insights}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068775320{\&}doi=10.3389{\%}2Ffpubh.2019.00138{\&}partnerID=40{\&}md5=403afa93e8761c6caa0abeceacd9d7de}, volume = {7}, year = {2019} } @incollection{Blazes2019, abstract = {Monitoring trends in disease is a core function of any public health system. There are numerous ways to accomplish disease surveillance, with key goals being rapid detection, being representative of the population, and cost-effectiveness. As surveillance methods improve in terms of timeliness and accuracy, precision public health is realized. {\textcopyright} 2019 Elsevier Inc. All rights reserved.}, annote = {Export Date: 20 January 2021}, author = {Blazes, D L and Dowell, S F}, booktitle = {Genomic and Precision Medicine: Infectious and Inflammatory Disease}, doi = {10.1016/B978-0-12-801496-7.00015-0}, pages = {257--265}, title = {{The role of disease surveillance in precision public health}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080815891{\&}doi=10.1016{\%}2FB978-0-12-801496-7.00015-0{\&}partnerID=40{\&}md5=609d2625474c711244ecfa98f3277b20}, year = {2019} } @article{Eckmanns2019, abstract = {Contemporary infectious disease surveillance systems aim to employ the speed and scope of big data in an attempt to provide global health security. Both shifts - the perception of health problems through the framework of global health security and the corresponding technological approaches - imply epistemological changes, methodological ambivalences as well as manifold societal effects. Bringing current findings from social sciences and public health praxis into a dialogue, this conversation style contribution points out several broader implications of changing disease surveillance. The conversation covers epidemiological issues such as the shift from expert knowledge to algorithmic knowledge, the securitization of global health, and the construction of new kinds of threats. Those developments are detailed and discussed in their impacts for health provision in a broader sense. {\textcopyright} 2019 The Author(s).}, annote = {Cited By :5 Export Date: 20 January 2021}, author = {Eckmanns, T and F{\"{u}}ller, H and Roberts, S L}, doi = {10.1186/s40504-019-0091-8}, journal = {Life Sciences, Society and Policy}, number = {1}, title = {{Digital epidemiology and global health security; An interdisciplinary conversation Tim Eckmanns, Leon Hempel, Kate Polin, Klaus Scheuermann, Edward Velasco}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063131587{\&}doi=10.1186{\%}2Fs40504-019-0091-8{\&}partnerID=40{\&}md5=d279c6b3bf47b5e56a21246274f42e34}, volume = {15}, year = {2019} } @article{Garattini2019, abstract = {The exponential accumulation, processing and accrual of big data in healthcare are only possible through an equally rapidly evolving field of big data analytics. The latter offers the capacity to rationalize, understand and use big data to serve many different purposes, from improved services modelling to prediction of treatment outcomes, to greater patient and disease stratification. In the area of infectious diseases, the application of big data analytics has introduced a number of changes in the information accumulation models. These are discussed by comparing the traditional and new models of data accumulation. Big data analytics is fast becoming a crucial component for the modelling of transmission—aiding infection control measures and policies—emergency response analyses required during local or international outbreaks. However, the application of big data analytics in infectious diseases is coupled with a number of ethical impacts. Four key areas are discussed in this paper: (i) automation and algorithmic reliance impacting freedom of choice, (ii) big data analytics complexity impacting informed consent, (iii) reliance on profiling impacting individual and group identities and justice/fair access and (iv) increased surveillance and population intervention capabilities impacting behavioural norms and practices. Furthermore, the extension of big data analytics to include information derived from personal devices, such as mobile phones and wearables as part of infectious disease frameworks in the near future and their potential ethical impacts are discussed. Considered together, the need for a constructive and transparent inclusion of ethical questioning in this rapidly evolving field becomes an increasing necessity in order to provide a moral foundation for the societal acceptance and responsible development of the technological advancement. {\textcopyright} 2017, The Author(s).}, annote = {Cited By :14 Export Date: 20 January 2021}, author = {Garattini, C and Raffle, J and Aisyah, D N and Sartain, F and Kozlakidis, Z}, doi = {10.1007/s13347-017-0278-y}, journal = {Philosophy and Technology}, number = {1}, pages = {69--85}, title = {{Big Data Analytics, Infectious Diseases and Associated Ethical Impacts}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064277708{\&}doi=10.1007{\%}2Fs13347-017-0278-y{\&}partnerID=40{\&}md5=6a4bc44b710e94b587a9e3e9ce5bae78}, volume = {32}, year = {2019} } @article{Khoury2019, abstract = {The field of public health genomics has matured in the past two decades and is beginning to deliver genomic-based interventions for health and health care. In the past few years, the terms precision medicine and precision public health have been used to include information from multiple fields measuring biomarkers as well as environmental and other variables to provide tailored interventions. In the context of public health, "precision" implies delivering the right intervention to the right population at the right time, with the goal of improving health for all. In addition to genomics, precision public health can be driven by "big data" as identified by volume, variety, and variability in biomedical, sociodemographic, environmental, geographic, and other information. Most current big data applications in health are in elucidating pathobiology and tailored drug discovery. We explore how big data and predictive analytics can contribute to precision public health by improving public health surveillance and assessment, and efforts to promote uptake of evidence-based interventions, by including more extensive information related to place, person, and time. We use selected examples drawn from child health, cardiovascular disease, and cancer to illustrate the promises of precision public health, as well as current methodologic and analytic challenges to big data to fulfill these promises. {\textcopyright} 2019 S. Karger AG, Basel. All rights reserved.}, annote = {Cited By :4 Export Date: 20 January 2021}, author = {Khoury, M J and Engelgau, M and Chambers, D A and Mensah, G A}, doi = {10.1159/000501465}, journal = {Public Health Genomics}, number = {5-6}, pages = {244--249}, title = {{Beyond Public Health Genomics: Can Big Data and Predictive Analytics Deliver Precision Public Health?}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069543514{\&}doi=10.1159{\%}2F000501465{\&}partnerID=40{\&}md5=0c45859daa027f80a595b73b4b2da1a1}, volume = {21}, year = {2019} } @article{Ouyang2019, abstract = {Research in big data, informatics, and bioinformatics has grown dramatically (Andreu-Perez J, et al.2015, IEEE Journal of Biomedical and Health Informatics 19, 1193-1208). Advances in gene sequencing technologies, surveillance systems, and electronic medical records have increased the amount of health data available. Unconventional data sources such as social media, wearable sensors, and internet search engine activity have also contributed to the influx of health data. The purpose of this study was to describe how 'big data', 'informatics', and 'bioinformatics' have been used in the animal health and veterinary medical literature and to map and chart publications using these terms through time. A scoping review methodology was used. A literature search of the terms 'big data', 'informatics', and 'bioinformatics' was conducted in the context of animal health and veterinary medicine. Relevance screening on abstract and full-text was conducted sequentially. In order for articles to be relevant, they must have used the words 'big data', 'informatics', or 'bioinformatics' in the title or abstract and full-text and have dealt with one of the major animal species encountered in veterinary medicine. Data items collected for all relevant articles included species, geographic region, first author affiliation, and journal of publication. The study level, study type, and data sources were collected for primary studies. After relevance screening, 1093 were classified. While there was a steady increase in 'bioinformatics' articles between 1995 and the end of the study period, 'informatics' articles reached their peak in 2012, then declined. The first 'big data' publication in animal health and veterinary medicine was in 2012. While few articles used the term 'big data' (n = 14), recent growth in 'big data' articles was observed. All geographic regions produced publications in 'informatics' and 'bioinformatics' while only North America, Europe, Asia, and Australia/Oceania produced publications about 'big data'. 'Bioinformatics' primary studies tended to use genetic data and tended to be conducted at the genetic level. In contrast, 'informatics' primary studies tended to use non-genetic data sources and conducted at an organismal level. The rapidly evolving definition of 'big data' may lead to avoidance of the term. Copyright {\textcopyright} Cambridge University Press 2019.}, annote = {Cited By :1 Export Date: 20 January 2021}, author = {Ouyang, Z and Sargeant, J and Thomas, A and Wycherley, K and Ma, R and Esmaeilbeigi, R and Versluis, A and Stacey, D and Stone, E and Poljak, Z and Bernardo, T M}, doi = {10.1017/S1466252319000136}, journal = {Animal Health Research Reviews}, pages = {1--18}, title = {{A scoping review of 'big data', 'informatics', and 'bioinformatics' in the animal health and veterinary medical literature}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076791066{\&}doi=10.1017{\%}2FS1466252319000136{\&}partnerID=40{\&}md5=4e2ca1f1125018b23772470f085a4c8a}, year = {2019} } @article{Roberts2019, abstract = {This article investigates the rise of algorithmic disease surveillance systems as novel technologies of risk analysis utilised to regulate pandemic outbreaks in an era of big data. Critically, the article demonstrates how intensified efforts towards harnessing big data and the application of algorithmic processing techniques to enhance the real-time surveillance and regulation infectious disease outbreaks significantly transform practices of global infectious disease surveillance; observed through the advent of novel risk rationalities which underpin the deployment of intensifying algorithmic practices to increasingly colonise and patrol emergent topographies of data in order to identify and govern the emergence of exceptional pathogenic risks. Conceptually, this article asserts further howthe rise of these novel risk regulating technologies within a context of big data transforms the government and forecasting of epidemics and pandemics: Illustrated by the rise of emergent algorithmic governmentalties of risk within contemporary contexts of big data, disease surveillance and the regulation of pandemic. {\textcopyright} 2019 Cambridge University Press.}, annote = {Cited By :3 Export Date: 20 January 2021}, author = {Roberts, S L}, doi = {10.1017/err.2019.6}, journal = {European Journal of Risk Regulation}, number = {1}, pages = {94--115}, title = {{Big data, algorithmic governmentality and the regulation of pandemic risk}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066078688{\&}doi=10.1017{\%}2Ferr.2019.6{\&}partnerID=40{\&}md5=6787f44ba3633baded7feb9976fe03f0}, volume = {10}, year = {2019} } @article{Wong2019, abstract = {Background: Since the beginning of the 21st century, the amount of data obtained from public health surveillance has increased dramatically due to the advancement of information and communications technology and the data collection systems now in place. Methods: This paper aims to highlight the opportunities gained through the use of Artificial Intelligence (AI) methods to enable reliable disease-oriented monitoring and projection in this information age. Results and Conclusion: It is foreseeable that together with reliable data management platforms AI methods will enable analysis of massive infectious disease and surveillance data effectively to support government agencies, healthcare service providers, and medical professionals to response to disease in the future. {\textcopyright} 2018 Australasian College for Infection Prevention and Control}, annote = {Cited By :27 Export Date: 20 January 2021}, author = {Wong, Z S Y and Zhou, J and Zhang, Q}, doi = {10.1016/j.idh.2018.10.002}, journal = {Infection, Disease and Health}, number = {1}, pages = {44--48}, title = {{Artificial Intelligence for infectious disease Big Data Analytics}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055753611{\&}doi=10.1016{\%}2Fj.idh.2018.10.002{\&}partnerID=40{\&}md5=b4d87d859d05aaabbc7d2b8a1925435e}, volume = {24}, year = {2019} } @article{Smith2019, abstract = {Syndromic surveillance is a form of surveillance that generates information for public health action by collecting, analysing and interpreting routine health-related data on symptoms and clinical signs reported by patients and clinicians rather than being based on microbiologically or clinically confirmed cases. In England, a suite of national real-time syndromic surveillance systems (SSS) have been developed over the last 20 years, utilising data from a variety of health care settings (a telehealth triage system, general practice and emergency departments). The real-time systems in England have been used for early detection (e.g. seasonal influenza), for situational awareness (e.g. describing the size and demographics of the impact of a heatwave) and for reassurance of lack of impact on population health of mass gatherings (e.g. the London 2012 Olympic and Paralympic Games).We highlight the lessons learnt from running SSS, for nearly two decades, and propose questions and issues still to be addressed. We feel that syndromic surveillance is an example of the use of ‘big data', but contend that the focus for sustainable and useful systems should be on the added value of such systems and the importance of people working together to maximise the value for the public health of syndromic surveillance services. {\textcopyright} The Author(s) 2019.}, annote = {Export Date: 3 August 2020}, author = {Smith, G E and Elliot, A J and Lake, I and Edeghere, O and Morbey, R and Catchpole, M and Heymann, D L and Hawker, J and Ibbotson, S and McCloskey, B and Pebody, R and Bains, A and Harcourt, S and Hughes, H and Lee, W and Loveridge, P and Smith, S and Soriano, A}, doi = {10.1017/S0950268819000074}, journal = {Epidemiology and Infection}, title = {{Syndromic surveillance: Two decades experience of sustainable systems – Its people not just data!}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062834073{\&}doi=10.1017{\%}2FS0950268819000074{\&}partnerID=40{\&}md5=e8a6aed251cb73ddc2c9a5d9760e32a1}, volume = {147}, year = {2019} } @article{Balicer2018, annote = {Cited By :3 Export Date: 20 January 2021}, author = {Balicer, R D and Luengo-Oroz, M and Cohen-Stavi, C and Loyola, E and Mantingh, F and Romanoff, L and Galea, G}, doi = {10.1016/S2213-8587(17)30372-8}, journal = {The Lancet Diabetes and Endocrinology}, number = {8}, pages = {595--598}, title = {{Using big data for non-communicable disease surveillance}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85035119967{\&}doi=10.1016{\%}2FS2213-8587{\%}2817{\%}2930372-8{\&}partnerID=40{\&}md5=0862c94fc5e260a183595f3d97cd9a3b}, volume = {6}, year = {2018} } @article{Bate2018, annote = {Cited By :14 Export Date: 20 January 2021}, author = {Bate, A and Reynolds, R F and Caubel, P}, doi = {10.1177/2042098617736422}, journal = {Therapeutic Advances in Drug Safety}, number = {1}, pages = {5--11}, title = {{The hope, hype and reality of Big Data for pharmacovigilance}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85039739342{\&}doi=10.1177{\%}2F2042098617736422{\&}partnerID=40{\&}md5=3d0d21cdb9f81f05293c9318c46727ca}, volume = {9}, year = {2018} } @article{Dolley2018, abstract = {Precision public health is an emerging practice to more granularly predict and understand public health risks and customize treatments for more specific and homogeneous subpopulations, often using new data, technologies, and methods. Big data is one element that has consistently helped to achieve these goals, through its ability to deliver to practitioners a volume and variety of structured or unstructured data not previously possible. Big data has enabled more widespread and specific research and trials of stratifying and segmenting populations at risk for a variety of health problems. Examples of success using big data are surveyed in surveillance and signal detection, predicting future risk, targeted interventions, and understanding disease. Using novel big data or big data approaches has risks that remain to be resolved. The continued growth in volume and variety of available data, decreased costs of data capture, and emerging computational methods mean big data success will likely be a required pillar of precision public health into the future. This review article aims to identify the precision public health use cases where big data has added value, identify classes of value that big data may bring, and outline the risks inherent in using big data in precision public health efforts. {\textcopyright} 2018 Dolley.}, annote = {Cited By :44 Export Date: 20 January 2021}, author = {Dolley, S}, doi = {10.3389/fpubh.2018.00068}, journal = {Frontiers in Public Health}, title = {{Big data's role in precision public health}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053111688{\&}doi=10.3389{\%}2Ffpubh.2018.00068{\&}partnerID=40{\&}md5=5f7fd92bc40f9ed161016c1a56e1d7ab}, volume = {6}, year = {2018} } @article{Gamache2018, abstract = {OBJECTIVE:  To summarize the recent public and population health informatics literature with a focus on the synergistic "bridging" of electronic data to benefit communities and other populations. METHODS:  The review was primarily driven by a search of the literature from July 1, 2016 to September 30, 2017. The search included articles indexed in PubMed using subject headings with (MeSH) keywords "public health informatics" and "social determinants of health". The "social determinants of health" search was refined to include articles that contained the keywords "public health", "population health" or "surveillance". RESULTS:  Several categories were observed in the review focusing on public health's socio-technical infrastructure: evaluation of surveillance practices, surveillance methods, interoperable health information infrastructure, mobile health, social media, and population health. Common trends discussing socio-technical infrastructure included big data platforms, social determinants of health, geographical information systems, novel data sources, and new visualization techniques. A common thread connected these categories of workforce, governance, and sustainability: using clinical resources and data to bridge public and population health. CONCLUSIONS:  Both medical care providers and public health agencies are increasingly using informatics and big data tools to create and share digital information. The intent of this "bridging" is to proactively identify, monitor, and improve a range of medical, environmental, and social factors relevant to the health of communities. These efforts show a significant growth in a range of population health-centric information exchange and analytics activities. Georg Thieme Verlag KG Stuttgart.}, annote = {Cited By :20 Export Date: 20 January 2021}, author = {Gamache, R and Kharrazi, H and Weiner, J P}, doi = {10.1055/s-0038-1667081}, journal = {Yearbook of medical informatics}, number = {1}, pages = {199--206}, title = {{Public and Population Health Informatics: The Bridging of Big Data to Benefit Communities}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052996969{\&}doi=10.1055{\%}2Fs-0038-1667081{\&}partnerID=40{\&}md5=77ad3f80fce8e8d1ac27a3e1725b02d7}, volume = {27}, year = {2018} } @misc{Mooney2018, abstract = {The digital world is generating data at a staggering and still increasing rate. While these 'big data' have unlocked novel opportunities to understand public health, they hold still greater potential for research and practice. This review explores several key issues that have arisen around big data. First, we propose a taxonomy of sources of big data to clarify terminology and identify threads common across some subtypes of big data. Next, we consider common public health research and practice uses for big data, including surveillance, hypothesis-generating research, and causal inference, while exploring the role that machine learning may play in each use. We then consider the ethical implications of the big data revolution with particular emphasis on maintaining appropriate care for privacy in a world in which technology is rapidly changing social norms regarding the need for (and even the meaning of ) privacy. Finally, we make suggestions regarding structuring teams and training to succeed in working with big data in research and practice. {\textcopyright} 2018 Annual Reviews Inc. All rights reserved.}, annote = {Cited By :59 Export Date: 20 January 2021}, author = {Mooney, S J and Pejaver, V}, booktitle = {Annual Review of Public Health}, doi = {10.1146/annurev-publhealth-040617-014208}, pages = {95--112}, title = {{Big Data in Public Health: Terminology, Machine Learning, and Privacy}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044872006{\&}doi=10.1146{\%}2Fannurev-publhealth-040617-014208{\&}partnerID=40{\&}md5=eadb791aa24648a0d54e54492637ffe4}, volume = {39}, year = {2018} } @article{Trifiro2018, abstract = {In the last decade ‘big data' has become a buzzword used in several industrial sectors, including but not limited to telephony, finance and healthcare. Despite its popularity, it is not always clear what big data refers to exactly. Big data has become a very popular topic in healthcare, where the term primarily refers to the vast and growing volumes of computerized medical information available in the form of electronic health records, administrative or health claims data, disease and drug monitoring registries and so on. This kind of data is generally collected routinely during administrative processes and clinical practice by different healthcare professionals: from doctors recording their patients' medical history, drug prescriptions or medical claims to pharmacists registering dispensed prescriptions. For a long time, this data accumulated without its value being fully recognized and leveraged. Today big data has an important place in healthcare, including in pharmacovigilance. The expanding role of big data in pharmacovigilance includes signal detection, substantiation and validation of drug or vaccine safety signals, and increasingly new sources of information such as social media are also being considered. The aim of the present paper is to discuss the uses of big data for drug safety post-marketing assessment. {\textcopyright} 2017, Springer International Publishing AG.}, annote = {Cited By :24 Export Date: 20 January 2021}, author = {Trifir{\`{o}}, G and Sultana, J and Bate, A}, doi = {10.1007/s40264-017-0592-4}, journal = {Drug Safety}, number = {2}, pages = {143--149}, title = {{From Big Data to Smart Data for Pharmacovigilance: The Role of Healthcare Databases and Other Emerging Sources}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028353382{\&}doi=10.1007{\%}2Fs40264-017-0592-4{\&}partnerID=40{\&}md5=850eca975d52e2f1419c5d691835bb6d}, volume = {41}, year = {2018} } @article{Barrett2017, annote = {Cited By :3 Export Date: 20 January 2021}, author = {Barrett, D}, doi = {10.3389/fvets.2017.00150}, journal = {Frontiers in Veterinary Science}, number = {OCT}, title = {{The potential for big data in animal disease surveillance in Ireland}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85038846809{\&}doi=10.3389{\%}2Ffvets.2017.00150{\&}partnerID=40{\&}md5=0a251f09a02319591386e861318ca452}, volume = {4}, year = {2017} } @article{Pollett2017, abstract = {Internet-based surveillance methods for vector-borne diseases (VBDs) using “big data” sources such as Google, Twitter, and internet newswire scraping have recently been developed, yet reviews on such “digital disease detection” methods have focused on respiratory pathogens, particularly in high-income regions. Here, we present a narrative review of the literature that has examined the performance of internet-based biosurveillance for diseases caused by vector-borne viruses, parasites, and other pathogens, including Zika, dengue, other arthropod-borne viruses, malaria, leishmaniasis, and Lyme disease across a range of settings, including low- and middle-income countries. The fundamental features, advantages, and drawbacks of each internet big data source are presented for those with varying familiarity of “digital epidemiology.” We conclude with some of the challenges and future directions in using internet-based biosurveillance for the surveillance and control of VBD. {\textcopyright} 2017 Public Library of Science. All Rights Reserved.}, annote = {Cited By :13 Export Date: 20 January 2021}, author = {Pollett, S and Althouse, B M and Forshey, B and Rutherford, G W and Jarman, R G}, doi = {10.1371/journal.pntd.0005871}, journal = {PLoS Neglected Tropical Diseases}, number = {11}, title = {{Internet-based biosurveillance methods for vector-borne diseases: Are they novel public health tools or just novelties?}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85036623681{\&}doi=10.1371{\%}2Fjournal.pntd.0005871{\&}partnerID=40{\&}md5=fefb14714f88a9a13d98f8a1f85fd84b}, volume = {11}, year = {2017} } @article{Manogaran2017, abstract = {{\textcopyright} 2017, IGI Global.Ambient intelligence is an emerging platform that provides advances in sensors and sensor networks, pervasive computing, and artificial intelligence to capture the real time climate data. This result continuously generates several exabytes of unstructured sensor data and so it is often called big climate data. Nowadays, researchers are trying to use big climate data to monitor and predict the climate change and possible diseases. Traditional data processing techniques and tools are not capable of handling such huge amount of climate data. Hence, there is a need to develop advanced big data architecture for processing the real time climate data. The purpose of this paper is to propose a big data based surveillance system that analyzes spatial climate big data and performs continuous monitoring of correlation between climate change and Dengue. Proposed disease surveillance system has been implemented with the help of Apache Hadoop MapReduce and its supporting tools.}, author = {Manogaran, Gunasekaran and Lopez, Daphne}, doi = {10.4018/IJACI.2017040106}, file = {:C$\backslash$:/Users/fernanda.dorea/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Manogaran, Lopez - 2017 - Disease Surveillance System for Big Climate Data Processing and Dengue Transmission.pdf:pdf}, issn = {1941-6237}, journal = {International Journal of Ambient Computing and Intelligence}, keywords = {big data}, mendeley-tags = {big data}, month = {apr}, number = {2}, pages = {88--105}, title = {{Disease Surveillance System for Big Climate Data Processing and Dengue Transmission}}, url = {http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJACI.2017040106}, volume = {8}, year = {2017} } @article{Oshea2017, abstract = {{\textcopyright} 2017 Elsevier B.V.Background Internet access and usage has changed how people seek and report health information. Meanwhile,infectious diseases continue to threaten humanity. The analysis of Big Data, or vast digital data, presents an opportunity to improve disease surveillance and epidemic intelligence. Epidemic intelligence contains two components: indicator based and event-based. A relatively new surveillance type has emerged called event-based Internet biosurveillance systems. These systems use information on events impacting health from Internet sources, such as social media or news aggregates. These systems circumvent the limitations of traditional reporting systems by being inexpensive, transparent, and flexible. Yet, innovations and the functionality of these systems can change rapidly. Aim To update the current state of knowledge on event-based Internet biosurveillance systems by identifying all systems, including current functionality, with hopes to aid decision makers with whether to incorporate new methods into comprehensive programmes of surveillance. Methods A systematic review was performed through PubMed, Scopus, and Google Scholar databases, while also including grey literature and other publication types. Results 50 event-based Internet systems were identified, including an extraction of 15 attributes for each system, described in 99 articles. Each system uses different innovative technology and data sources to gather data, process, and disseminate data to detect infectious disease outbreaks. Conclusions The review emphasises the importance of using both formal and informal sources for timely and accurate infectious disease outbreak surveillance, cataloguing all event-based Internet biosurveillance systems. By doing so, future researchers will be able to use this review as a library for referencing systems, with hopes of learning, building, and expanding Internet-based surveillance systems. Event-based Internet biosurveillance should act as an extension of traditional systems, to be utilised as an additional, supplemental data source to have a more comprehensive estimate of disease burden.}, author = {O'Shea, Jesse and {O 'shea}, Jesse and O'Shea, Jesse}, doi = {10.1016/j.ijmedinf.2017.01.019}, file = {:C$\backslash$:/Users/fernanda.dorea/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/O 'shea, O'Shea - 2017 - Digital disease detection A systematic review of event-based internet biosurveillance systems.pdf:pdf}, issn = {13865056}, journal = {International Journal of Medical Informatics}, keywords = {Biosurveillance,Disease surveillance,Public health,webcrawl}, mendeley-tags = {webcrawl}, month = {may}, pages = {15--22}, title = {{Digital disease detection: A systematic review of event-based internet biosurveillance systems}}, url = {http://linkinghub.elsevier.com/retrieve/pii/S1386505617300308 http://ac.els-cdn.com/S1386505617300308/1-s2.0-S1386505617300308-main.pdf?{\_}tid=9f3eb2fe-397b-11e7-a63c-00000aab0f27{\&}acdnat=1494859057{\_}fbabbd3631cf0a877cfa3dda69e731ab}, volume = {101}, year = {2017} } @article{Santillana2017, author = {Santillana, Mauricio}, doi = {10.1093/cid/ciw660}, issn = {1058-4838}, journal = {Clinical Infectious Diseases}, month = {jan}, number = {1}, pages = {42--43}, title = {{Editorial Commentary : Perspectives on the Future of Internet Search Engines and Biosurveillance Systems}}, url = {https://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciw660}, volume = {64}, year = {2017} } @article{Flahault2016, abstract = {OBJECTIVES: The aim of this manuscript is to provide a brief overview of the scientific challenges that should be addressed in order to unlock the full potential of using data from a general point of view, as well as to present some ideas that could help answer specific needs for data understanding in the field of health sciences and epidemiology. METHODS: A survey of uses and challenges of big data analyses for medicine and public health was conducted. The first part of the paper focuses on big data techniques, algorithms, and statistical approaches to identify patterns in data. The second part describes some cutting-edge applications of analyses and predictive modeling in public health. RESULTS: In recent years, we witnessed a revolution regarding the nature, collection, and availability of data in general. This was especially striking in the health sector and particularly in the field of epidemiology. Data derives from a large variety of sources, e.g. clinical settings, billing claims, care scheduling, drug usage, web based search queries, and Tweets. CONCLUSION: The exploitation of the information (data mining, artificial intelligence) relevant to these data has become one of the most promising as well challenging tasks from societal and scientific viewpoints in order to leverage the information available and making public health more efficient.}, annote = {Cited By :1 Export Date: 20 January 2021}, author = {Flahault, A and Bar-Hen, A and Paragios, N}, doi = {10.15265/iy-2016-021}, journal = {Yearbook of medical informatics}, number = {1}, pages = {240--246}, title = {{Public Health and Epidemiology Informatics}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021859654{\&}doi=10.15265{\%}2Fiy-2016-021{\&}partnerID=40{\&}md5=c0a45a7df41826d604a4384531401c39}, year = {2016} } @inproceedings{Zhang2016, abstract = {Asthma is a chronic disease that affects people of all ages, and is a serious health and economic concern worldwide. However, accurate and timely surveillance and predicting hospital visits could allow for targeted interventions and reduce the societal burden of asthma. Current national asthma disease surveillance systems can have data availability lags of up to months and years. Rapid progress has been made in gathering social media data to perform disease surveillance and prediction. We introduce novel methods for extracting signals from social media data to assist in accurate and timely asthma surveillance. Our empirical analyses show that our methods are very effective for surveillance of asthma prevalence at both state and municipal levels. They are also useful for predicting the number of hospital visits based on near-real-Time social media data for specific geographic areas. Our results can be used for public health surveillance, ED preparedness, and targeted patient interventions.}, author = {Zhang, W and Ram, S and Burkart, M and Pengetnze, Y}, booktitle = {DH 2016 - Proceedings of the 2016 Digital Health Conference}, doi = {10.1145/2896338.2896340}, isbn = {9781450342247}, title = {{Extracting signals from social media for chronic disease surveillance}}, url = {http://dl.acm.org/citation.cfm?id=2897728}, year = {2016} } @article{Simonsen2016, abstract = {{\textcopyright} The Author 2016.While big data have proven immensely useful in fields such as marketing and earth sciences, public health is still relying on more traditional surveillance systems and awaiting the fruits of a big data revolution. A new generation of big data surveillance systems is needed to achieve rapid, flexible, and local tracking of infectious diseases, especially for emerging pathogens. In this opinion piece, we reflect on the long and distinguished history of disease surveillance and discuss recent developments related to use of big data. We start with a brief review of traditional systems relying on clinical and laboratory reports.We then examine how large-volume medical claims data can, with great spatiotemporal resolution, help elucidate local disease patterns. Finally, we review efforts to develop surveillance systems based on digital and social data streams, including the recent rise and fall of Google Flu Trends. We conclude by advocating for increased use of hybrid systems combining information from traditional surveillance and big data sources, which seems the most promising option moving forward. Throughout the article, we use influenza as an exemplar of an emerging and reemerging infection which has traditionally been considered a model system for surveillance and modeling.}, author = {Simonsen, Lone and Gog, Julia R and Olson, Don and Viboud, C{\'{e}}cile}, doi = {10.1093/infdis/jiw376}, file = {:C$\backslash$:/Users/fernanda.dorea/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Simonsen et al. - 2016 - Infectious Disease Surveillance in the Big Data Era Towards Faster and Locally Relevant Systems.pdf:pdf}, issn = {0022-1899}, journal = {Journal of Infectious Diseases}, keywords = {big data,ddds}, mendeley-tags = {big data,ddds}, month = {dec}, number = {suppl 4}, pages = {S380--S385}, title = {{Infectious Disease Surveillance in the Big Data Era: Towards Faster and Locally Relevant Systems}}, url = {https://academic.oup.com/jid/article-lookup/doi/10.1093/infdis/jiw376}, volume = {214}, year = {2016} } @inproceedings{Othman2016, abstract = {{\textcopyright} 2016 IEEE.This paper introduces Dengue Active Surveillance System (DASS) framework for an early warning system of the outbreak. Dengue and dengue hemorrhagic fever are emerging as major public health problems in most Asian countries such as Malaysia. Effective prevention and control programs will depend on improved surveillance. A new approach to active surveillance outlined with emphasis on the inter-epidemic period. The objective is to develop an early warning surveillance system (framework) that can predict epidemic dengue to improve current passive surveillance system available in Malaysia. Basically, the framework introduced data harvesting process from multiple sources as input, data pre-processing using data aggregator and filtering engine, storing large data in repository, analytic engine for analysis and processing the large data, and presentation of the information to the users. The data harvested from two major sources such as weather or flood information, and social media such as build development and dengue symptom using system API, SOAP and others. The data aggregator will aggregate the data from three different types of data such as structured, semi-structured and unstructured data to be stored into the semi-structured database such as MongoDB and NoSQL. The data parse to the filtering engine for filtering and cleaning the data sources using suitable keywords prior to store it in the large data repository. After that, the large data will be processed and analyzed using algorithm or mathematical calculation to determine the expected dengue cases. Then, the processed information will be presented to the users in a form of web or mobile application and other method, for example, short message service (SMS). Finally, the system accuracy will be evaluated based on the comparison study with the traditional passive system.}, author = {Othman, Mohd Khalit and Danuri, M.S.N.M. Mohd Shahrul Nizam Mohd}, booktitle = {2016 International Conference on Information and Communication Technology (ICICTM)}, doi = {10.1109/ICICTM.2016.7890783}, file = {:C$\backslash$:/Users/fernanda.dorea/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Othman, Danuri - 2016 - Proposed conceptual framework of Dengue Active Surveillance System (DASS) in Malaysia.pdf:pdf}, isbn = {978-1-5090-0412-6}, keywords = {biosurveillance example}, mendeley-tags = {biosurveillance example}, pages = {90--96}, publisher = {IEEE}, title = {{Proposed conceptual framework of Dengue Active Surveillance System (DASS) in Malaysia}}, url = {http://ieeexplore.ieee.org/document/7890783/}, year = {2016} } @article{Bansal2016, abstract = {{\textcopyright} The Author 2016.We devote a special issue of the Journal of Infectious Diseases to review the recent advances of big data in strengthening disease surveillance, monitoring medical adverse events, informing transmission models, and tracking patient sentiments and mobility. We consider a broad definition of big data for public health, one encompassing patient information gathered from high-volume electronic health records and participatory surveillance systems, as well as mining of digital traces such as social media, Internet searches, and cell-phone logs. We introduce nine independent contributions to this special issue and highlight several cross-cutting areas that require further research, including representativeness, biases, volatility, and validation, and the need for robust statistical and hypotheses-driven analyses. Overall, we are optimistic that the big-data revolution will vastly improve the granularity and timeliness of available epidemiological information, with hybrid systems augmenting rather than supplanting traditional surveillance systems, and better prospects for accurate infectious diseases models and forecasts.}, author = {Bansal, S and Chowell, G and Simonsen, L and Vespignani, A and Viboud, C}, doi = {10.1093/infdis/jiw400}, file = {:C$\backslash$:/Users/fernanda.dorea/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Bansal et al. - 2016 - Big data for infectious disease surveillance and modeling.pdf:pdf}, journal = {Journal of Infectious Diseases}, keywords = {big data,modeling,surveillance}, mendeley-tags = {big data,modeling,surveillance}, title = {{Big data for infectious disease surveillance and modeling}}, volume = {214}, year = {2016} } @article{Vallmuur2016, abstract = {{\textcopyright} 2016, BMJ Publishing Group. All rights reserved.Objective Vast amounts of injury narratives are collected daily and are available electronically in real time and have great potential for use in injury surveillance and evaluation. Machine learning algorithms have been developed to assist in identifying cases and classifying mechanisms leading to injury in a much timelier manner than is possible when relying on manual coding of narratives. The aim of this paper is to describe the background, growth, value, challenges and future directions of machine learning as applied to injury surveillance. Methods This paper reviews key aspects of machine learning using injury narratives, providing a case study to demonstrate an application to an established humanmachine learning approach. Results The range of applications and utility of narrative text has increased greatly with advancements in computing techniques over time. Practical and feasible methods exist for semiautomatic classification of injury narratives which are accurate, efficient and meaningful. The human-machine learning approach described in the case study achieved high sensitivity and PPV and reduced the need for human coding to less than a third of cases in one large occupational injury database. Conclusions The last 20 years have seen a dramatic change in the potential for technological advancements in injury surveillance. Machine learning of ‘big injury narrative data' opens up many possibilities for expanded sources of data which can provide more comprehensive, ongoing and timely surveillance to inform future injury prevention policy and practice.}, author = {Vallmuur, Kirsten and Marucci-Wellman, Helen R and Taylor, Jennifer A and Lehto, Mark and Corns, Helen L and Smith, Gordon S}, doi = {10.1136/injuryprev-2015-041813}, file = {:C$\backslash$:/Users/fernanda.dorea/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Vallmuur et al. - 2016 - Harnessing information from injury narratives in the ‘big data' era understanding and applying machine learning.pdf:pdf}, issn = {1353-8047}, journal = {Injury Prevention}, keywords = {,classification,ddds,nlp}, mendeley-tags = {classification,ddds,nlp}, month = {apr}, number = {Suppl 1}, pages = {i34--i42}, title = {{Harnessing information from injury narratives in the ‘big data' era: understanding and applying machine learning for injury surveillance}}, url = {http://injuryprevention.bmj.com/lookup/doi/10.1136/injuryprev-2015-041813}, volume = {22}, year = {2016} } @article{Huang2015, abstract = {{\textcopyright} 2015 Elsevier Inc.With the development of smart devices and cloud computing, more and more public health data can be collected from various sources and can be analyzed in an unprecedented way. The huge social and academic impact of such developments caused a worldwide buzz for big data. In this review article, we summarized the latest applications of Big Data in health sciences, including the recommendation systems in healthcare, Internet-based epidemic surveillance, sensor-based health conditions and food safety monitoring, Genome-Wide Association Studies (GWAS) and expression Quantitative Trait Loci (eQTL), inferring air quality using big data and metabolomics and ionomics for nutritionists. We also reviewed the latest technologies of big data collection, storage, transferring, and the state-of-the-art analytical methods, such as Hadoop distributed file system, MapReduce, recommendation system, deep learning and network Analysis. At last, we discussed the future perspectives of health sciences in the era of Big Data.}, author = {Huang, Tao and Lan, Liang and Fang, Xuexian and An, Peng and Min, Junxia and Wang, Fudi}, doi = {10.1016/j.bdr.2015.02.002}, file = {:C$\backslash$:/Users/fernanda.dorea/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Huang et al. - 2015 - Promises and Challenges of Big Data Computing in Health Sciences.pdf:pdf}, issn = {22145796}, journal = {Big Data Research}, keywords = {big data}, mendeley-tags = {big data}, month = {mar}, number = {1}, pages = {2--11}, title = {{Promises and Challenges of Big Data Computing in Health Sciences}}, url = {http://linkinghub.elsevier.com/retrieve/pii/S2214579615000118}, volume = {2}, year = {2015} } @article{Milinovich2015, author = {Milinovich, Gabriel J and Magalh{\~{a}}es, Ricardo J Soares and Hu, Wenbiao}, doi = {10.1016/S2214-109X(14)70356-0}, file = {:C$\backslash$:/Users/fernanda.dorea/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Milinovich, Magalh{\~{a}}es, Hu - 2015 - Role of big data in the early detection of Ebola and other emerging infectious diseases.pdf:pdf}, issn = {2214109X}, journal = {The Lancet Global Health}, keywords = {webcrawl}, mendeley-tags = {webcrawl}, month = {jan}, number = {1}, pages = {e20--e21}, title = {{Role of big data in the early detection of Ebola and other emerging infectious diseases}}, url = {http://linkinghub.elsevier.com/retrieve/pii/S2214109X14703560}, volume = {3}, year = {2015} } @article{Davidson2015, abstract = {Seasonal influenza infects approximately 5-20{\%} of the U.S. population every year, resulting in over 200,000 hospitalizations. The ability to more accurately assess infection levels and predict which regions have higher infection risk in future time periods can instruct targeted prevention and treatment efforts, especially during epidemics. Google Flu Trends (GFT) has generated significant hope that'big data' can be an effective tool for estimating disease burden and spread. The estimates generated by GFT come in real-time-two weeks earlier than traditional surveillance data collected by the U.S. Centers for Disease Control and Prevention (CDC). However, GFT had some infamous errors and is significantly less accurate at tracking laboratory-confirmed cases than syndromic influenza-like illness (ILI) cases. We construct an empirical network using CDC data and combine this with GFT to substantially improve its performance. This improved model predicts infections one week into the future as well as GFT predicts the present and does particularly well in regions that are most likely to facilitate influenza spread and during epidemics.}, author = {Davidson, Michael W. and Haim, Dotan A. and Radin, Jennifer M.}, doi = {10.1038/srep08154}, file = {:C$\backslash$:/Users/fernanda.dorea/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Davidson, Haim, Radin - 2015 - Using Networks to Combine “Big Data” and Traditional Surveillance to Improve Influenza Predictions.pdf:pdf}, issn = {2045-2322}, journal = {Scientific Reports}, keywords = {webcrawl}, mendeley-tags = {webcrawl}, month = {jul}, number = {Region 3}, pages = {8154}, title = {{Using Networks to Combine “Big Data” and Traditional Surveillance to Improve Influenza Predictions}}, url = {http://www.nature.com/articles/srep08154}, volume = {5}, year = {2015} } @article{Charles-Smith2015, abstract = {OBJECTIVE: Research studies show that social media may be valuable tools in the disease surveillance toolkit used for improving public health professionals' ability to detect disease outbreaks faster than traditional methods and to enhance outbreak response. A social media work group, consisting of surveillance practitioners, academic researchers, and other subject matter experts convened by the International Society for Disease Surveillance, conducted a systematic primary literature review using the PRISMA framework to identify research, published through February 2013, answering either of the following questions: Can social media be integrated into disease surveillance practice and outbreak management to support and improve public health?Can social media be used to effectively target populations, specifically vulnerable populations, to test an intervention and interact with a community to improve health outcomes?Examples of social media included are Facebook, MySpace, microblogs (e.g., Twitter), blogs, and discussion forums. For Question 1, 33 manuscripts were identified, starting in 2009 with topics on Influenza-like Illnesses (n = 15), Infectious Diseases (n = 6), Non-infectious Diseases (n = 4), Medication and Vaccines (n = 3), and Other (n = 5). For Question 2, 32 manuscripts were identified, the first in 2000 with topics on Health Risk Behaviors (n = 10), Infectious Diseases (n = 3), Non-infectious Diseases (n = 9), and Other (n = 10).$\backslash$n$\backslash$nCONCLUSIONS: The literature on the use of social media to support public health practice has identified many gaps and biases in current knowledge. Despite the potential for success identified in exploratory studies, there are limited studies on interventions and little use of social media in practice. However, information gleaned from the articles demonstrates the effectiveness of social media in supporting and improving public health and in identifying target populations for intervention. A primary recommendation resulting from the review is to identify opportunities that enable public health professionals to integrate social media analytics into disease surveillance and outbreak management practice.}, archivePrefix = {arXiv}, arxivId = {1401.1032}, author = {Charles-Smith, Lauren E. and Reynolds, Tera L. and Cameron, Mark A. and Conway, Mike and Lau, Eric H Y and Olsen, Jennifer M. and Pavlin, Julie A. and Shigematsu, Mika and Streichert, Laura C. and Suda, Katie J. and Corley, Courtney D.}, doi = {10.1371/journal.pone.0139701}, eprint = {1401.1032}, file = {:C$\backslash$:/Users/fernanda.dorea/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Charles-Smith et al. - 2015 - Using social media for actionable disease surveillance and outbreak management A systematic literature rev.pdf:pdf}, isbn = {1932-6203}, issn = {19326203}, journal = {PLoS ONE}, keywords = {social media}, mendeley-tags = {social media}, number = {10}, pages = {1--20}, pmid = {26437454}, title = {{Using social media for actionable disease surveillance and outbreak management: A systematic literature review}}, volume = {10}, year = {2015} } @inproceedings{Kumar2015, abstract = {Rapid growth of the Internet has paved the way for millions of people across the globe to access social media platforms such as Facebook and Twitter. These social media platforms enable people to share information instantaneously. The large volume of information shared on these platforms can be leveraged to identify outbreaks of various epidemics. This will help health professionals to provide timely intervention, which in return could help save lives and millions of dollars. Analysis of information shared on social media is complicated due to its sheer volume, varied formats and velocity of collection. We have addressed this potential problem by making use of a big data analytics platform capable of handling large quantities of streaming data. In this paper we demonstrate how data from social media can be effectively used in the surveillance of disease conditions.}, author = {Kumar, A T K and Asamoah, D and Sharda, R}, booktitle = {2015 Americas Conference on Information Systems, AMCIS 2015}, file = {:C$\backslash$:/Users/fernanda.dorea/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Kumar, Asamoah, Sharda - 2015 - Can social media support public health Demonstrating disease surveillance using big data analytics.pdf:pdf}, isbn = {9780996683104}, keywords = {,social media}, mendeley-tags = {social media}, title = {{Can social media support public health? Demonstrating disease surveillance using big data analytics}}, year = {2015} } @article{Gittelman2015, abstract = {{\textcopyright}Steven Gittelman, Victor Lange, Carol A Gotway Crawford, Catherine A Okoro, Eugene Lieb, Satvinder S Dhingra, Elaine Trimarchi.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.}, author = {Gittelman, Steven and Lange, Victor and {Gotway Crawford}, Carol A and Okoro, Catherine A and Lieb, Eugene and Dhingra, Satvinder S and Trimarchi, Elaine}, doi = {10.2196/jmir.3970}, file = {:C$\backslash$:/Users/fernanda.dorea/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Gittelman et al. - 2015 - A New Source of Data for Public Health Surveillance Facebook Likes.pdf:pdf}, issn = {1438-8871}, journal = {Journal of Medical Internet Research}, keywords = {,social media}, mendeley-tags = {social media}, month = {apr}, number = {4}, pages = {e98}, title = {{A New Source of Data for Public Health Surveillance: Facebook Likes}}, url = {http://www.jmir.org/2015/4/e98/}, volume = {17}, year = {2015} } @article{Asokan2015, abstract = {{\textcopyright} 2015 Ministry of Health, Saudi Arabia.Zoonoses constitute 61{\%} of all known infectious diseases. The major obstacles to control zoonoses include insensitive systems and unreliable data. Intelligent handling of the cost effective big data can accomplish the goals of one health to detect disease trends, outbreaks, pathogens and causes of emergence in human and animals.}, author = {Asokan, G.V. and Asokan, Vanitha}, doi = {10.1016/j.jegh.2015.02.001}, file = {:C$\backslash$:/Users/fernanda.dorea/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Asokan, Asokan - 2015 - Leveraging “big data” to enhance the effectiveness of “one health” in an era of health informatics.pdf:pdf}, issn = {22106006}, journal = {Journal of Epidemiology and Global Health}, keywords = {Big data,Health informatics,One health,Zoonoses,big data,one health}, mendeley-tags = {big data,one health}, month = {dec}, number = {4}, pages = {311--314}, pmid = {25747185}, title = {{Leveraging “big data” to enhance the effectiveness of “one health” in an era of health informatics}}, url = {http://linkinghub.elsevier.com/retrieve/pii/S2210600615000283 http://www.ncbi.nlm.nih.gov/pubmed/25747185}, volume = {5}, year = {2015} } @incollection{Pyne2015, abstract = {There is growing concern about our preparedness for controlling the spread of pandemics such as H1N1 Influenza. The dynamics of epidemic spread in large-scale populations are very complex. Further, human behavior, social contact networks, and pandemics are closely intertwined and evolve as the epidemic spread. Individuals' changing behaviors in response to public policies and their evolving perception of how an infectious disease outbreak is unfolding can dramatically alter normal social interactions. Effective planning and response strategies must take these complicated interactions into account. Mathematical models are key to understanding the spread of epidemics. In this chapter, we discuss a recent approach of diffusion in network models for studying the complex dynamics of epidemics in large-scale populations. Analyzing these models leads to very challenging computational problems. Further, using these models for forecasting epidemic spread and developing public health policies leads to issues that are characteristic of big data applications. The chapter describes the state of the art in computational and big data epidemiology. {\textcopyright} 2015 Elsevier B.V.}, author = {Pyne, Saumyadipta and Vullikanti, Anile Kumar S. and Marathe, Madhav V.}, booktitle = {Handbook of Statistics}, doi = {10.1016/B978-0-444-63492-4.00008-3}, isbn = {9780444634924}, pages = {171--202}, title = {{Big Data Applications in Health Sciences and Epidemiology}}, url = {http://linkinghub.elsevier.com/retrieve/pii/B9780444634924000083}, volume = {33}, year = {2015} } @article{Hay2013, author = {Hay, Simon I. and George, Dylan B. and Moyes, Catherine L. and Brownstein, John S. and Flaxman, AD}, doi = {10.1371/journal.pmed.1001413}, file = {:C$\backslash$:/Users/fernanda.dorea/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Hay et al. - 2013 - Big Data Opportunities for Global Infectious Disease Surveillance.pdf:pdf}, issn = {1549-1676}, journal = {PLoS Medicine}, keywords = {big data,biosurveillance,ddds}, mendeley-tags = {big data,biosurveillance,ddds}, month = {apr}, number = {4}, pages = {e1001413}, publisher = {Public Library of Science}, title = {{Big Data Opportunities for Global Infectious Disease Surveillance}}, url = {http://dx.plos.org/10.1371/journal.pmed.1001413}, volume = {10}, year = {2013} } @article{Hoffman2013, abstract = {The accelerating adoption of electronic health record (EHR) systems will have far-reaching implications for public health research and surveillance, which in turn could lead to changes in public policy, statutes, and regulations. The public health benefits of EHR use can be significant. However, researchers and analysts who rely on EHR data must proceed with caution and understand the potential limitations of EHRs. Because of clinicians' workloads, poor user-interface design, and other factors, EHR data can be erroneous, miscoded, fragmented, and incomplete. In addition, public health findings can be tainted by the problems of selection bias, confounding bias, and measurement bias. These flaws may become all the more troubling and important in an era of electronic "big data," in which a massive amount of information is processed automatically, without human checks. Thus, we conclude the paper by outlining several regulatory and other interventions to address data analysis difficulties that could result in invalid conclusions and unsound public health policies. {\textcopyright} 2013 American Society of Law, Medicine {\&} Ethics, Inc.}, author = {Hoffman, Sharona and Podgurski, Andy}, doi = {10.1111/jlme.12040}, file = {:C$\backslash$:/Users/fernanda.dorea/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Hoffman, Podgurski - 2013 - Big Bad Data Law, Public Health, and Biomedical Databases.pdf:pdf}, issn = {10731105}, journal = {The Journal of Law, Medicine {\&} Ethics}, keywords = {big data,policy}, mendeley-tags = {big data,policy}, month = {mar}, number = {SUPPL. 1}, pages = {56--60}, title = {{Big Bad Data: Law, Public Health, and Biomedical Databases}}, url = {http://doi.wiley.com/10.1111/jlme.12040}, volume = {41}, year = {2013} } @article{Larson2013, author = {Larson, Eric B}, doi = {10.1001/jama.2013.5914}, issn = {0098-7484}, journal = {JAMA}, month = {jun}, number = {23}, pages = {2443}, title = {{Building Trust in the Power of “Big Data” Research to Serve the Public Good}}, url = {http://jama.jamanetwork.com/article.aspx?doi=10.1001/jama.2013.5914}, volume = {309}, year = {2013} }