IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i10p3414-d171954.html
   My bibliography  Save this article

Global Research on Syndromic Surveillance from 1993 to 2017: Bibliometric Analysis and Visualization

Author

Listed:
  • Ibrahim Musa

    (Database/Bioinformatics Laboratory, Chungbuk National University, Cheongju 28644, Korea
    These authors contributed equally to this work.)

  • Hyun Woo Park

    (Database/Bioinformatics Laboratory, Chungbuk National University, Cheongju 28644, Korea
    These authors contributed equally to this work.)

  • Lkhagvadorj Munkhdalai

    (Database/Bioinformatics Laboratory, Chungbuk National University, Cheongju 28644, Korea)

  • Keun Ho Ryu

    (Database/Bioinformatics Laboratory, Chungbuk National University, Cheongju 28644, Korea
    Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam)

Abstract

Syndromic Surveillance aims at analyzing medical data to detect clusters of illness or forecast disease outbreaks. Although the research in this field is flourishing in terms of publications, an insight of the global research output has been overlooked. This paper aims at analyzing the global scientific output of the research from 1993 to 2017. To this end, the paper uses bibliometric analysis and visualization to achieve its goal. Particularly, a data processing framework was proposed based on citation datasets collected from Scopus and Clarivate Analytics’ Web of Science Core Collection (WoSCC). The bibliometric method and Citespace were used to analyze the institutions, countries, and research areas as well as the current hotspots and trends. The preprocessed dataset includes 14,680 citation records. The analysis uncovered USA, England, Canada, France and Australia as the top five most productive countries publishing about Syndromic Surveillance. On the other hand, at the Pinnacle of academic institutions are the US Centers for Disease Control and Prevention (CDC). The reference co-citation analysis uncovered the common research venues and further analysis of the keyword cooccurrence revealed the most trending topics. The findings of this research will help in enriching the field with a comprehensive view of the status and future trends of the research on Syndromic Surveillance.

Suggested Citation

  • Ibrahim Musa & Hyun Woo Park & Lkhagvadorj Munkhdalai & Keun Ho Ryu, 2018. "Global Research on Syndromic Surveillance from 1993 to 2017: Bibliometric Analysis and Visualization," Sustainability, MDPI, vol. 10(10), pages 1-20, September.
  • Handle: RePEc:gam:jsusta:v:10:y:2018:i:10:p:3414-:d:171954
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/10/3414/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/10/10/3414/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Andrea Freyer Dugas & Mehdi Jalalpour & Yulia Gel & Scott Levin & Fred Torcaso & Takeru Igusa & Richard E Rothman, 2013. "Influenza Forecasting with Google Flu Trends," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-7, February.
    2. Vogt, R.L. & LaRue, D. & Klaucke, D.N. & Jillson, D.A., 1983. "Comparison of an active and passive surveillance system of primary care providers for hepatitis, measles, rubella, and salmonellosis in Vermont," American Journal of Public Health, American Public Health Association, vol. 73(7), pages 795-797.
    3. Pavlin, J.A. & Mostashari, F. & Kortepeter, M.G. & Hynes, N.A. & Chotani, R.A. & Mikol, Y.B. & Ryan, M.A.K. & Neville, J.S. & Gantz, D.T. & Writer, J.V. & Florance, J.E. & Culpepper, R.C. & Henretig, , 2003. "Innovative Surveillance Methods for Rapid Detection of Disease Outbreaks and Bioterrorism: Results of an Interagency Workshop on Health Indicator Surveillance," American Journal of Public Health, American Public Health Association, vol. 93(8), pages 1230-1235.
    4. Martin Kulldorff, 2001. "Prospective time periodic geographical disease surveillance using a scan statistic," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 61-72.
    5. Khan, A.S. & Fleischauer, A. & Casani, J. & Groseclose, S.L., 2010. "The next public health revolution: Public health information fusion and social networks," American Journal of Public Health, American Public Health Association, vol. 100(7), pages 1237-1242.
    6. Christian Sonesson & David Bock, 2003. "A review and discussion of prospective statistical surveillance in public health," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 166(1), pages 5-21, February.
    7. Nicholas Generous & Geoffrey Fairchild & Alina Deshpande & Sara Y Del Valle & Reid Priedhorsky, 2014. "Global Disease Monitoring and Forecasting with Wikipedia," PLOS Computational Biology, Public Library of Science, vol. 10(11), pages 1-16, November.
    8. Zhihao Li & Tao Liu & Guanghu Zhu & Hualiang Lin & Yonghui Zhang & Jianfeng He & Aiping Deng & Zhiqiang Peng & Jianpeng Xiao & Shannon Rutherford & Runsheng Xie & Weilin Zeng & Xing Li & Wenjun Ma, 2017. "Dengue Baidu Search Index data can improve the prediction of local dengue epidemic: A case study in Guangzhou, China," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 11(3), pages 1-13, March.
    9. Olson, K.L. & Grannis, S.J. & Mandl, K.D., 2006. "Privacy protection versus cluster detection in spatial epidemiology," American Journal of Public Health, American Public Health Association, vol. 96(11), pages 2002-2008.
    10. Kate E. Jones & Nikkita G. Patel & Marc A. Levy & Adam Storeygard & Deborah Balk & John L. Gittleman & Peter Daszak, 2008. "Global trends in emerging infectious diseases," Nature, Nature, vol. 451(7181), pages 990-993, February.
    11. Frumkin, H. & Hess, J. & Luber, G. & Malilay, J. & McGeehin, M., 2008. "Climate change: The public health response," American Journal of Public Health, American Public Health Association, vol. 98(3), pages 435-445.
    12. Steffen Unkel & C. Paddy Farrington & Paul H. Garthwaite & Chris Robertson & Nick Andrews, 2012. "Statistical methods for the prospective detection of infectious disease outbreaks: a review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(1), pages 49-82, January.
    13. Duczmal, Luiz & Assuncao, Renato, 2004. "A simulated annealing strategy for the detection of arbitrarily shaped spatial clusters," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 269-286, March.
    14. Kulldorff, Martin & Tango, Toshiro & Park, Peter J., 2003. "Power comparisons for disease clustering tests," Computational Statistics & Data Analysis, Elsevier, vol. 42(4), pages 665-684, April.
    15. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    16. Cynthia Chew & Gunther Eysenbach, 2010. "Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-13, November.
    17. Qinneng Xu & Yulia R Gel & L Leticia Ramirez Ramirez & Kusha Nezafati & Qingpeng Zhang & Kwok-Leung Tsui, 2017. "Forecasting influenza in Hong Kong with Google search queries and statistical model fusion," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-17, May.
    18. Huchang Liao & Ming Tang & Li Luo & Chunyang Li & Francisco Chiclana & Xiao-Jun Zeng, 2018. "A Bibliometric Analysis and Visualization of Medical Big Data Research," Sustainability, MDPI, vol. 10(1), pages 1-18, January.
    19. Ron Brookmeyer & Elizabeth Johnson & Robert Bollinger, 2004. "Public health vaccination policies for containing an anthrax outbreak," Nature, Nature, vol. 432(7019), pages 901-904, December.
    20. Declan Butler, 2013. "When Google got flu wrong," Nature, Nature, vol. 494(7436), pages 155-156, February.
    21. Jean-Paul Chretien & Dylan George & Jeffrey Shaman & Rohit A Chitale & F Ellis McKenzie, 2014. "Influenza Forecasting in Human Populations: A Scoping Review," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-8, April.
    22. David A Broniatowski & Michael J Paul & Mark Dredze, 2013. "National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-1, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shoukai Sun & Yuantong Jiang & Shuanning Zheng, 2020. "Research on Ecological Infrastructure from 1990 to 2018: A Bibliometric Analysis," Sustainability, MDPI, vol. 12(6), pages 1-23, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zeynep Ertem & Dorrie Raymond & Lauren Ancel Meyers, 2018. "Optimal multi-source forecasting of seasonal influenza," PLOS Computational Biology, Public Library of Science, vol. 14(9), pages 1-16, September.
    2. Logan C Brooks & David C Farrow & Sangwon Hyun & Ryan J Tibshirani & Roni Rosenfeld, 2018. "Nonmechanistic forecasts of seasonal influenza with iterative one-week-ahead distributions," PLOS Computational Biology, Public Library of Science, vol. 14(6), pages 1-29, June.
    3. Jose L Herrera & Ravi Srinivasan & John S Brownstein & Alison P Galvani & Lauren Ancel Meyers, 2016. "Disease Surveillance on Complex Social Networks," PLOS Computational Biology, Public Library of Science, vol. 12(7), pages 1-16, July.
    4. Daniel Alejandro Gónzalez-Bandala & Juan Carlos Cuevas-Tello & Daniel E. Noyola & Andreu Comas-García & Christian A García-Sepúlveda, 2020. "Computational Forecasting Methodology for Acute Respiratory Infectious Disease Dynamics," IJERPH, MDPI, vol. 17(12), pages 1-20, June.
    5. Marianne Frisén, 2014. "Spatial outbreak detection based on inference principles for multivariate surveillance," IISE Transactions, Taylor & Francis Journals, vol. 46(8), pages 759-769, August.
    6. Linus Schiöler & Marianne Fris�n, 2012. "Multivariate outbreak detection," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(2), pages 223-242, April.
    7. Fantazzini, Dean, 2020. "Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 33-54.
    8. Samuel V Scarpino & James G Scott & Rosalind M Eggo & Bruce Clements & Nedialko B Dimitrov & Lauren Ancel Meyers, 2020. "Socioeconomic bias in influenza surveillance," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-19, July.
    9. Victor Olsavszky & Mihnea Dosius & Cristian Vladescu & Johannes Benecke, 2020. "Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database," IJERPH, MDPI, vol. 17(14), pages 1-17, July.
    10. Valentina Lorenzoni & Gianni Andreozzi & Andrea Bazzani & Virginia Casigliani & Salvatore Pirri & Lara Tavoschi & Giuseppe Turchetti, 2022. "How Italy Tweeted about COVID-19: Detecting Reactions to the Pandemic from Social Media," IJERPH, MDPI, vol. 19(13), pages 1-14, June.
    11. Toshiro Tango & Kunihiko Takahashi & Kazuaki Kohriyama, 2011. "A Space–Time Scan Statistic for Detecting Emerging Outbreaks," Biometrics, The International Biometric Society, vol. 67(1), pages 106-115, March.
    12. Thais Paiva & Renato Assunção & Taynãna Simões, 2015. "Prospective space–time surveillance with cumulative surfaces for geographical identification of the emerging cluster," Computational Statistics, Springer, vol. 30(2), pages 419-440, June.
    13. William H. Woodall & J Brooke Marshall & Michael D. Joner Jr & Shannon E Fraker & Abdel‐Salam G Abdel‐Salam, 2008. "On the use and evaluation of prospective scan methods for health‐related surveillance," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(1), pages 223-237, January.
    14. Logan C Brooks & David C Farrow & Sangwon Hyun & Ryan J Tibshirani & Roni Rosenfeld, 2015. "Flexible Modeling of Epidemics with an Empirical Bayes Framework," PLOS Computational Biology, Public Library of Science, vol. 11(8), pages 1-18, August.
    15. Qiong Jia & Yue Guo & Guanlin Wang & Stuart J. Barnes, 2020. "Big Data Analytics in the Fight against Major Public Health Incidents (Including COVID-19): A Conceptual Framework," IJERPH, MDPI, vol. 17(17), pages 1-21, August.
    16. Doyo G Enki & Paul H Garthwaite & C Paddy Farrington & Angela Noufaily & Nick J Andrews & Andre Charlett, 2016. "Comparison of Statistical Algorithms for the Detection of Infectious Disease Outbreaks in Large Multiple Surveillance Systems," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-25, August.
    17. Kuchler, Theresa & Russel, Dominic & Stroebel, Johannes, 2022. "JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook," Journal of Urban Economics, Elsevier, vol. 127(C).
    18. Abay,Kibrom A. & Hirfrfot,Kibrom Tafere & Woldemichael,Andinet, 2020. "Winners and Losers from COVID-19 : Global Evidence from Google Search," Policy Research Working Paper Series 9268, The World Bank.
    19. Kai Chen & Xiaoping Lin & Han Wang & Yujie Qiang & Jie Kong & Rui Huang & Haining Wang & Hui Liu, 2022. "Visualizing the Knowledge Base and Research Hotspot of Public Health Emergency Management: A Science Mapping Analysis-Based Study," Sustainability, MDPI, vol. 14(12), pages 1-23, June.
    20. Wan, You & Pei, Tao & Zhou, Chenghu & Jiang, Yong & Qu, Chenxu & Qiao, Youlin, 2012. "ACOMCD: A multiple cluster detection algorithm based on the spatial scan statistic and ant colony optimization," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 283-296.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:10:y:2018:i:10:p:3414-:d:171954. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.