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Journal articles on the topic 'Data surveillance'

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Author: Grafiati

Published: 4 June 2021

Last updated: 31 January 2023

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1

Matzner, Tobias. "Beyond data as representation: The performativity of Big Data in surveillance." Surveillance & Society 14, no. 2 (September 21, 2016): 197–210. http://dx.doi.org/10.24908/ss.v14i2.5831.

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The article discusses problems of representative views of data and elaborates a concept of the performativity of data. It shows how data used for surveillance contributes in creating suspect subjectivities. In particular, the article focuses on the inductive or explorative processing of data and on the decoupling of data generation and analysis that characterize current use of data for surveillance. It lines out several challenges this poses to established accounts of surveillance: David Lyon’s concept of surveillance as social sorting and Haggerty and Ericson’s “surveillant assemblage”. These problems are attributed to a representationalist view, which focuses on the veracity of data. This can lead to ignoring problematic consequences of surveillance procedures and the full scope of affected persons. Building on an idea by Rita Raley, an alternative account of data as performative is proposed. Using Judith Butler’s concept of “citationality,” this account shows how surveillance is entangled with the production of subjects through data in general. Surveillance is reformulated as a particular way in which subjects are produced that is parasitical to other forms of subjectivation.

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2

Steeves, Valerie. "Big Data Surveillance." Journal of Intelligence, Conflict, and Warfare 4, no. 2 (November 23, 2021): 95–98. http://dx.doi.org/10.21810/jicw.v4i2.3751.

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On October 21, 2021, Dr. Valerie Steeves presented Big Data Surveillance: Privacy and Trust Implications at the October 2021 CASIS Digital Roundtable event. This presentation was followed by a question and answer period, where CASIS Executives and attendees were given the opportunity to discuss the presentation with Dr. Steeves. The discussion topics included building an understanding of data surveillance for young people and safeguarding their privacy through building trusting relations.

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3

Brayne, Sarah. "The Criminal Law and Law Enforcement Implications of Big Data." Annual Review of Law and Social Science 14, no. 1 (October 13, 2018): 293–308. http://dx.doi.org/10.1146/annurev-lawsocsci-101317-030839.

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Law enforcement agencies increasingly use big data analytics in their daily operations. This review outlines how police departments leverage big data and new surveillant technologies in patrol and investigations. It distinguishes between directed surveillance—which involves the surveillance of individuals and places under suspicion—and dragnet surveillance—which involves suspicionless, unparticularized data collection. Law enforcement's adoption of big data analytics far outpaces legal responses to the new surveillant landscape. Therefore, this review highlights open legal questions about data collection, suspicion requirements, and police discretion. It concludes by offering suggestions for future directions for researchers and practitioners.

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4

Andrejevic, Mark, and Kelly Gates. "Big Data Surveillance: Introduction." Surveillance & Society 12, no. 2 (May 9, 2014): 185–96. http://dx.doi.org/10.24908/ss.v12i2.5242.

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5

Southey, Hugh, and Adam Straw. "Surveillance, Data and Privacy." Judicial Review 18, no. 4 (December 20, 2013): 440–45. http://dx.doi.org/10.1080/10854681.2013.11426812.

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6

Wesseling, Mara, Marieke de Goede, and Louise Amoore. "DATA WARS BEYOND SURVEILLANCE." Journal of Cultural Economy 5, no. 1 (February 2012): 49–66. http://dx.doi.org/10.1080/17530350.2012.640554.

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7

Smith, Gavin J. D. "Surveillance, Data and Embodiment." Body & Society 22, no. 2 (January 21, 2016): 108–39. http://dx.doi.org/10.1177/1357034x15623622.

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8

Boulanger, Virginie, Étienne Poirier, Anne MacLaurin, and Caroline Quach. "Divergences between healthcare-associated infection administrative data and active surveillance data in Canada." Canada Communicable Disease Report 48, no. 1 (January 26, 2022): 4–16. http://dx.doi.org/10.14745/ccdr.v48i01a02.

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Background: Although Canada has both a national active surveillance system and administrative data for the passive surveillance of healthcare-associated infections (HAI), both have identified strengths and weaknesses in their data collection and reporting. Active and passive surveillance work independently, resulting in results that diverge at times. To understand the divergences between administrative health data and active surveillance data, a scoping review was performed. Method: Medline, Embase and Cumulative Index to Nursing and Allied Health Literature along with grey literature were searched for studies in English and French that evaluated the use of administrative data, alone or in comparison with traditional surveillance, in Canada between 1995 and November 2, 2020. After extracting relevant information from selected articles, a descriptive summary of findings was provided with suggestions for the improvement of surveillance systems to optimize the overall data quality. Results: Sixteen articles met the inclusion criteria, including twelve observational studies and four systematic reviews. Studies showed that using a single source of administrative data was not accurate for HAI surveillance when compared with traditional active surveillance; however, combining different sources of data or combining administrative with active surveillance data improved accuracy. Electronic surveillance systems can also enhance surveillance by improving the ability to detect potential HAIs. Conclusion: Although active surveillance of HAIs produced the most accurate results and remains the gold-standard, the integration between active and passive surveillance data can be optimized. Administrative data can be used to enhance traditional active surveillance. Future studies are needed to evaluate the feasibility and benefits of potential solutions presented for the use of administrative data for HAI surveillance and reporting in Canada.

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9

Sell, Randall L. "LGBTQ Health Surveillance: Data = Power." American Journal of Public Health 107, no. 6 (June 2017): 843–44. http://dx.doi.org/10.2105/ajph.2017.303798.

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10

Busch, Thorsten, Christoph Schank, Ulrich Leicht-Deobald, Antoinette Weibel, Simon Daniel Schafheitle, Isabelle Wildhaber, and Gabriel Kasper. "Workplace Surveillance & Big Data." Academy of Management Proceedings 2018, no. 1 (August 2018): 14411. http://dx.doi.org/10.5465/ambpp.2018.14411abstract.

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11

Geer, Daniel E. "Personal Data and Government Surveillance." IEEE Security & Privacy 12, no. 4 (July 2014): 90–96. http://dx.doi.org/10.1109/msp.2014.73.

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12

Low, N. "Caution: chlamydia surveillance data ahead." Sexually Transmitted Infections 84, no. 2 (January 23, 2008): 80–81. http://dx.doi.org/10.1136/sti.2008.030338.

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13

Brumfiel, Geoff. "Military surveillance data: Shared intelligence." Nature 477, no. 7365 (September 2011): 388–89. http://dx.doi.org/10.1038/477388a.

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14

Fricker, Ronald D., and Howard S. Burkom. "Data aggregation in disease surveillance." Journal of Quality Technology 53, no. 1 (July 12, 2019): 38–43. http://dx.doi.org/10.1080/00224065.2019.1611360.

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15

Garfinkel, Simson, and Michael Smith. "Guest Editors' Introduction: Data Surveillance." IEEE Security and Privacy Magazine 4, no. 6 (November 2006): 15–17. http://dx.doi.org/10.1109/msp.2006.154.

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16

Lally, Nick. "Crowdsourced surveillance and networked data." Security Dialogue 48, no. 1 (September 21, 2016): 63–77. http://dx.doi.org/10.1177/0967010616664459.

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Possibilities for crowdsourced surveillance have expanded in recent years as data uploaded to social networks can be mined, distributed, assembled, mapped, and analyzed by anyone with an uncensored internet connection. These data points are necessarily fragmented and partial, open to interpretation, and rely on algorithms for retrieval and sorting. Yet despite these limitations, they have been used to produce complex representations of space, subjects, and power relations as internet users attempt to reconstruct and investigate events while they are developing. In this article, I consider one case of crowdsourced surveillance that emerged following the detonation of two bombs at the 2013 Boston Marathon. I focus on the actions of a particular forum on reddit.com , which would exert a significant influence on the events as they unfolded. The study describes how algorithmic affordances, internet cultures, surveillance imaginaries, and visual epistemologies contributed to the structuring of thought, action, and subjectivity in the moment of the event. I use this case study as a way to examine moments of entangled political complicity and resistance, highlighting the ways in which particular surveillance practices are deployed and feed back into the event amid its unfolding.

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17

Wang, Songwang, Yangfei Li, Qiang Chen, Xiaoyu Feng, Xiju Shi, Ying Huang, Li Mei, et al. "Integration of biosafety surveillance through Biosafety Surveillance Conceptual Data Model." Biosafety and Health 1, no. 2 (September 2019): 98–104. http://dx.doi.org/10.1016/j.bsheal.2019.10.002.

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18

Lee, Seungwon, Paul Ronksley, John Conly, Stephanie Garies, Hude Quan, Peter Faris, Bing Li, and Elizabeth Henderson. "Using data linkage methodologies to augment healthcare-associated infection surveillance data." Infection Control & Hospital Epidemiology 40, no. 10 (July 29, 2019): 1144–50. http://dx.doi.org/10.1017/ice.2019.184.

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AbstractBackground and objectives:The landscape of antimicrobial resistance (AMR) surveillance is changing rapidly. The primary objective of this study was to assess the benefit of linking population-based infection prevention and control surveillance data on methicillin-resistant Staphylococcus aureus (MRSA) to hospital discharge abstract data (DAD). We assessed the value of this novel data linkage for the characterization of hospital-acquired (HA) and community-acquired MRSA (CA-MRSA) cases.Methods:Incident inpatient MRSA surveillance data for all adults (≥18 years) from 4 acute-care facilities in Calgary, Alberta, between April 1, 2011, and March 31, 2017, were linked to DAD. Personal health number (PHN) and gender were used to identify specific individuals, and specimen collection time-points were used to identify specific hospitalization records. A third common variable on admission date between these databases was used to validate the linkage process. Descriptive statistics were used to characterize HA-MRSA and CA-MRSA cases identified through the linkage process.Results:A total of 2,430 surveillance records (94.6%) were successfully linked to the correct hospitalization period. By linking surveillance and administrative data, we were able to identify key differences between patients with HA- and CA-MRSA. These differences are consistent with previously reported findings in the literature. Data linkage to DAD may be a novel tool to enhance and augment the details of base surveillance data.Conclusion and recommendations:This is the first Canadian study linking a frontline healthcare-associated infection AMR surveillance database to an administrative population database. This work represents an important methodological step toward complementing traditional AMR surveillance data practices. Data linkage to other data types, such as primary care, emergency, social, and biological data, may be the basis of achieving more precise data focused around AMR.

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19

Shallenberger, R., S. Khuwaja, and O. G. M. Ibrahim. "Integrating Surveillance Data: Does Emergency Room (ER) Syndromic Data add to the Ecologic Surveillance Picture?" American Journal of Epidemiology 163, suppl_11 (June 1, 2006): S188. http://dx.doi.org/10.1093/aje/163.suppl_11.s188-c.

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20

Boulanger, Virginie, Étienne Poirier, Anne MacLaurin, and Caroline Quach. "Divergences entre les données administratives sur les infections associées aux soins de santé et les données issues de la surveillance active au Canada." Relevé des maladies transmissibles au Canada 48, no. 1 (January 26, 2022): 4–18. http://dx.doi.org/10.14745/ccdr.v48i01a02f.

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Contexte : Bien que le Canada dispose à la fois d’un système national de surveillance active et de données administratives pour la surveillance passive des infections associées aux soins de santé (IASS), chacun des deux systèmes présente des forces et des faiblesses au niveau de la collecte et de la communication des données. La surveillance active et la surveillance passive fonctionnent de manière indépendante, ce qui donne parfois des résultats divergents. Pour comprendre les divergences qui existent entre les données administratives sur la santé et les données issues de la surveillance active, un examen de la portée a été réalisé. Méthodes : Des recherches ont été menées dans les bases de données Medline, Embase et Cumulative Index to Nursing and Allied Health Literature, ainsi que dans la littérature grise pour trouver des études en anglais et en français évaluant l’utilisation de données administratives, seules ou en comparaison avec les données issues de la surveillance traditionnelle et réalisées au Canada entre 1995 et le 2 novembre 2020. Après avoir extrait les renseignements pertinents des articles sélectionnés, un résumé descriptif des résultats a été présenté, accompagné de suggestions pour améliorer les systèmes de surveillance, dans le but d’optimiser la qualité globale des données. Résultats : Seize articles répondaient aux critères d’inclusion, dont douze études observationnelles et quatre revues systématiques. Ces études ont démontré que l’utilisation d’une seule source de données administratives n’était pas assez précise pour la surveillance des IASS, par rapport à la surveillance active traditionnelle. Cependant, la combinaison de différentes sources de données ou la combinaison de données administratives et de données issues de la surveillance active en améliore la précision. Les systèmes de surveillance électronique peuvent également renforcer la surveillance en augmentant la capacité à détecter les IASS potentielles. Conclusion : Bien que la surveillance active des IASS ait produit les résultats les plus précis et demeure la norme d’excellence en la matière, l’intégration des données issues de la surveillance active et passive peut être optimisée. Les données administratives peuvent être utilisées pour renforcer la surveillance active traditionnelle. De futures études sont nécessaires pour évaluer la faisabilité et les avantages des solutions potentielles proposées pour l’utilisation des données administratives pour la surveillance et la déclaration des IASS au Canada.

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21

Orlando, Vincent A., Ronald G. Sandholm, and Douglas W. Burgess. "TCAS II Use of ADS-B Surveillance Data through Hybrid Surveillance." Air Traffic Control Quarterly 7, no. 2 (April 1999): 109–21. http://dx.doi.org/10.2514/atcq.7.2.109.

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22

TODD, EWEN C. D., JOHN J. GUZEWICH, and FRANK L. BRYAN. "Surveillance of Foodborne Disease IV. Dissemination and Uses of Surveillance Data." Journal of Food Protection 60, no. 6 (June 1, 1997): 715–23. http://dx.doi.org/10.4315/0362-028x-60.6.715.

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Comparisons of etiologic agents, vehicles, significant ingredients, place of mishandling, and method of food processing or preparation with specific contributory factors are particularly useful in identifying specific hazards, specifying operations that are candidates for designation as critical control points, and assessing risks. After foodborne disease surveillance data have been received, tabulated, and appropriately interpreted, summary information needs to be disseminated in a timely fashion to those who can use it for preventing foodborne diseases. This action should be taken at all levels of the surveillance network. Surveillance information is used to determine the need for food safety actions, which involves planning and implementing programs and assessing the effectiveness of the actions taken. Uses of the data include (a) developing new policies and procedures and revising priorities, (b) evaluating effectiveness of programs, (c) justifying food safety program budgets based on estimated costs offoodborne illness, (d) modifying regulations so that they relate to contemporary foodborne disease issues, (e) conducting hazard analyses and risk assessments and instituting programs oriented to hazard analysis critical control points (HACCP), (f) starting or improving a public information campaign and educating the public, (g) notifying and training food industry personnel, (h) training agency staff and public health students and professionals, and (i) identifying new problems and research needs from the data. Implementing these approaches will necessitate changes in traditional food safety activities. This four-part series of articles concludes with recommendations to be considered by local, state/provincial, national, and international agencies responsible for foodborne disease surveillance.

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23

Raman, Shanti, John Rooney, Tania Sorrell, Sharon Chen, Elizabeth Griggs, and Christine Roberts. "Using Australian cryptococcal surveillance data to improve AIDS surveillance in NSW." New South Wales Public Health Bulletin 7, no. 6 (1996): 60. http://dx.doi.org/10.1071/nb96017.

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24

Kumar, B. "Automated Surveillance System and Data Communication." IOSR Journal of Computer Engineering 12, no. 2 (2013): 31–38. http://dx.doi.org/10.9790/0661-1223138.

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25

Eze, Monday, Charles Okunbor, Ruth Amanze, and Deborah Aleburu. "PROGRAMMING TRIGGERS FOR CRITICAL DATA SURVEILLANCE." Indian Journal of Computer Science and Engineering 12, no. 2 (April 20, 2021): 518–25. http://dx.doi.org/10.21817/indjcse/2021/v12i2/211202162.

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26

Hwang, Gyu-Hwan, Moon-Ki Kim, and Do-Keun Kang. "Displaying Multiple Maritime Surveillance Radar Data." Transactions of The Korean Institute of Electrical Engineers 61, no. 7 (July 1, 2012): 1041–48. http://dx.doi.org/10.5370/kiee.2012.61.7.1041.

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27

Rushworth, R. Louise, S. M. Bell, Stephen Morrell, Peter W. Robertson, and C. C. Fisher. "Validation of hepatitis B surveillance data." Australian and New Zealand Journal of Public Health 21, no. 2 (April 1997): 217. http://dx.doi.org/10.1111/j.1467-842x.1997.tb01688.x.

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28

Brillman, Judith C. "Syndromic Surveillance with Emergency Department Data." Emergency Medicine News 25, no. 9 (September 2003): 30–31. http://dx.doi.org/10.1097/00132981-200309000-00019.

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29

Castro, K. G. "Tuberculosis Surveillance: Data for Decision-Making." Clinical Infectious Diseases 44, no. 10 (May 15, 2007): 1268–70. http://dx.doi.org/10.1086/514351.

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30

Koh, C. H., and Busyairah Syd Ali. "Multi-Sensor Surveillance Data Correlation Algorithm." Advanced Science Letters 24, no. 2 (February 1, 2018): 971–76. http://dx.doi.org/10.1166/asl.2018.10669.

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31

Shrier, Ian. "Proper Analyses of Injury Surveillance Data." Medicine & Science in Sports & Exercise 41 (May 2009): 46. http://dx.doi.org/10.1249/01.mss.0000353029.83930.7c.

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32

Bourhis, Y., T. Gottwald, and F. van den Bosch. "Translating surveillance data into incidence estimates." Philosophical Transactions of the Royal Society B: Biological Sciences 374, no. 1776 (May 20, 2019): 20180262. http://dx.doi.org/10.1098/rstb.2018.0262.

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Monitoring a population for a disease requires the hosts to be sampled and tested for the pathogen. This results in sampling series from which we may estimate the disease incidence, i.e. the proportion of hosts infected. Existing estimation methods assume that disease incidence does not change between monitoring rounds, resulting in an underestimation of the disease incidence. In this paper, we develop an incidence estimation model accounting for epidemic growth with monitoring rounds that sample varying incidence. We also show how to accommodate the asymptomatic period that is the characteristic of most diseases. For practical use, we produce an approximation of the model, which is subsequently shown to be accurate for relevant epidemic and sampling parameters. Both the approximation and the full model are applied to stochastic spatial simulations of epidemics. The results prove their consistency for a very wide range of situations. The estimation model is made available as an online application. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.

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33

Moody, Barbara. "Practical Application of Sharing Surveillance Data." Critical Care Nursing Clinics of North America 7, no. 4 (December 1995): 719–25. http://dx.doi.org/10.1016/s0899-5885(18)30365-4.

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34

Dombkowski, Kevin J., Elizabeth A. Wasilevich, Sarah Lyon-Callo, Trang Q. Nguyen, Michael G. Medvesky, and Mary Alice Lee. "Asthma Surveillance Using Medicaid Administrative Data." Journal of Public Health Management and Practice 15, no. 6 (November 2009): 485–93. http://dx.doi.org/10.1097/phh.0b013e3181a8c334.

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35

Ismadi, D., P. Suthichoti, and C. S. Kabir. "Understanding well performance with surveillance data." Journal of Petroleum Science and Engineering 74, no. 1-2 (October 2010): 99–106. http://dx.doi.org/10.1016/j.petrol.2010.08.015.

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36

Arthur, Paul Longley. "Data Portraits: Identity, Privacy, and Surveillance." a/b: Auto/Biography Studies 32, no. 2 (April 25, 2017): 371–73. http://dx.doi.org/10.1080/08989575.2017.1289007.

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37

Subudhi, Badri Narayan, Deepak Kumar Rout, and Ashish Ghosh. "Big data analytics for video surveillance." Multimedia Tools and Applications 78, no. 18 (June 5, 2019): 26129–62. http://dx.doi.org/10.1007/s11042-019-07793-w.

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38

Van Landuyt, Herman W., Johan Boelaert, Bart Glibert, Bart Gordts, and Anne-Marie Verbruggen. "Surveillance of aminoglycoside resistance: European data." American Journal of Medicine 80, no. 6 (June 1986): 76–81. http://dx.doi.org/10.1016/0002-9343(86)90483-3.

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39

Bertrand, M. "Provider Perceptions and Epidemiological Surveillance Data." American Journal of Infection Control 32, no. 3 (May 2004): E118—E119. http://dx.doi.org/10.1016/j.ajic.2004.04.175.

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40

Muldoon, J. T., L. A. Wintermeyer, J. A. Eure, L. Fuortes, J. A. Merchant, S. F. Van Lier, and T. B. Richards. "Occupational disease surveillance data sources, 1985." American Journal of Public Health 77, no. 8 (August 1987): 1006–8. http://dx.doi.org/10.2105/ajph.77.8.1006.

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41

Trevathan, E. ""Flying blind" without epilepsy surveillance data." Neurology 76, no. 1 (December 27, 2010): 10–11. http://dx.doi.org/10.1212/wnl.0b013e318203e9b0.

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42

Cowling, Benjamin J., Irene O. L. Wong, Lai-Ming Ho, Steven Riley, and Gabriel M. Leung. "Methods for monitoring influenza surveillance data." International Journal of Epidemiology 35, no. 5 (August 22, 2006): 1314–21. http://dx.doi.org/10.1093/ije/dyl162.

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43

Williams, J. "Commentary: Public access to surveillance data." BMJ 313, no. 7069 (November 30, 1996): 1383. http://dx.doi.org/10.1136/bmj.313.7069.1383.

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44

Rubel, Alan, and Kyle M. L. Jones. "The temptation of data-enabled surveillance." Communications of the ACM 63, no. 4 (March 20, 2020): 22–24. http://dx.doi.org/10.1145/3382741.

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45

Hier, Sean P. "Probing the Surveillant Assemblage: on the dialectics of surveillance practices as processes of social control." Surveillance & Society 1, no. 3 (September 1, 2002): 399–411. http://dx.doi.org/10.24908/ss.v1i3.3347.

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Recent dialogue on the contemporary nature of information and data gathering techniques has incorporated the notion of assemblages to denote an increasing convergence of once discrete systems of surveillance. The rhizomatic expansion of late modern ‘surveillant assemblages’ is purported not only to enable important transformations in the purpose and intention of surveillance practices, but to facilitate a partial democratization of surveillance hierarchies. Seeking to account for the forces and desires which give rise to, and sustain, surveillant assemblages, this paper explicates the workings of a dialectic embedded in many surveillance practices to reveal a polarization effect involving the simultaneous leveling and solidification of hierarchies. Empirical data from the intensification of welfare monitoring are presented to illustrate the dialectics of surveillance practices as processes of social control.

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46

Garnett, G. P. "Behavioural data as an adjunct to HIV surveillance data." Sexually Transmitted Infections 82, suppl_1 (April 1, 2006): i57—i62. http://dx.doi.org/10.1136/sti.2005.016543.

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47

Woeltje, Keith F., Michael Y. Lin, Michael Klompas, Marc Oliver Wright, Gianna Zuccotti, and William E. Trick. "Data Requirements for Electronic Surveillance of Healthcare-Associated Infections." Infection Control & Hospital Epidemiology 35, no. 9 (September 1, 2014): 1083–91. http://dx.doi.org/10.1086/677623.

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Electronic surveillance for healthcare-associated infections (HAIs) is increasingly widespread. This is driven by multiple factors: a greater burden on hospitals to provide surveillance data to state and national agencies, financial pressures to be more efficient with HAI surveillance, the desire for more objective comparisons between healthcare facilities, and the increasing amount of patient data available electronically. Optimal implementation of electronic surveillance requires that specific information be available to the surveillance systems. This white paper reviews different approaches to electronic surveillance, discusses the specific data elements required for performing surveillance, and considers important issues of data validation.Infect Control Hosp Epidemiol 2014;35(9):1083-1091

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48

Ternhag, A., A. Tegnell, B. Lesko, K. Skaerlund, and P. Penttinen. "Basic Surveillance Network, a European database for surveillance data on infectious diseases." Eurosurveillance 9, no. 7 (July 1, 2004): 1–2. http://dx.doi.org/10.2807/esm.09.07.00472-en.

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The Basic Surveillance Network was started in 2000 and is one of the networks on infectious diseases funded by the European Commission. The network collects and makes readily available basic surveillance data on infectious diseases from all the 'old' (pre-2004) European Union member states. The aim is to provide easy access to descriptive data that already exist in national databases, so that it is possible to monitor and compare incidence trends for infectious diseases in the EU member states. The list of diseases covered by the network has recently been expanded from 10 initial 'pilot' diseases to over 40 diseases listed by the EU to be under surveillance. In the near future, the new member states will be invited to participate in the network. Data are case-based and comprise date of onset of disease, age and sex. Only a very short list of disease specific additional variables, such as country of infection or immunisation status, is collected. Classification of cases ( possible, probable, confirmed) is specified according to EU case definitions. The participants of the network have access to an internal web site were all the data is presented in tables and graphs. An open website is available for the public at https://www.eubsn.org./BSN/

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49

Gardner, Bennett R., Matthew J. Strickland, and Adolfo Correa. "Application of the automated spatial surveillance program to birth defects surveillance data." Birth Defects Research Part A: Clinical and Molecular Teratology 79, no. 7 (July 2007): 559–64. http://dx.doi.org/10.1002/bdra.20363.

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50

Brayne, Sarah. "Big Data Surveillance: The Case of Policing." American Sociological Review 82, no. 5 (August 29, 2017): 977–1008. http://dx.doi.org/10.1177/0003122417725865.

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Abstract:

This article examines the intersection of two structural developments: the growth of surveillance and the rise of “big data.” Drawing on observations and interviews conducted within the Los Angeles Police Department, I offer an empirical account of how the adoption of big data analytics does—and does not—transform police surveillance practices. I argue that the adoption of big data analytics facilitates amplifications of prior surveillance practices and fundamental transformations in surveillance activities. First, discretionary assessments of risk are supplemented and quantified using risk scores. Second, data are used for predictive, rather than reactive or explanatory, purposes. Third, the proliferation of automatic alert systems makes it possible to systematically surveil an unprecedentedly large number of people. Fourth, the threshold for inclusion in law enforcement databases is lower, now including individuals who have not had direct police contact. Fifth, previously separate data systems are merged, facilitating the spread of surveillance into a wide range of institutions. Based on these findings, I develop a theoretical model of big data surveillance that can be applied to institutional domains beyond the criminal justice system. Finally, I highlight the social consequences of big data surveillance for law and social inequality.

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