IDEAS home Printed from https://ideas.repec.org/a/plo/pntd00/0005871.html
   My bibliography  Save this article

Internet-based biosurveillance methods for vector-borne diseases: Are they novel public health tools or just novelties?

Author

Listed:
  • Simon Pollett
  • Benjamin M Althouse
  • Brett Forshey
  • George W Rutherford
  • Richard G Jarman

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.

Suggested Citation

  • Simon Pollett & Benjamin M Althouse & Brett Forshey & George W Rutherford & Richard G Jarman, 2017. "Internet-based biosurveillance methods for vector-borne diseases: Are they novel public health tools or just novelties?," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 11(11), pages 1-13, November.
  • Handle: RePEc:plo:pntd00:0005871
    DOI: 10.1371/journal.pntd.0005871
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0005871
    Download Restriction: no

    File URL: https://journals.plos.org/plosntds/article/file?id=10.1371/journal.pntd.0005871&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pntd.0005871?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Samir Bhatt & Peter W. Gething & Oliver J. Brady & Jane P. Messina & Andrew W. Farlow & Catherine L. Moyes & John M. Drake & John S. Brownstein & Anne G. Hoen & Osman Sankoh & Monica F. Myers & Dylan , 2013. "The global distribution and burden of dengue," Nature, Nature, vol. 496(7446), pages 504-507, April.
    2. 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.
    3. Mauricio Santillana & André T Nguyen & Mark Dredze & Michael J Paul & Elaine O Nsoesie & John S Brownstein, 2015. "Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance," PLOS Computational Biology, Public Library of Science, vol. 11(10), pages 1-15, October.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    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. 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).
    3. 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.
    4. 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.
    5. Pi Guo & Tao Liu & Qin Zhang & Li Wang & Jianpeng Xiao & Qingying Zhang & Ganfeng Luo & Zhihao Li & Jianfeng He & Yonghui Zhang & Wenjun Ma, 2017. "Developing a dengue forecast model using machine learning: A case study in China," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 11(10), pages 1-22, October.
    6. Tiago França Melo De Lima & Raquel Martins Lana & Tiago Garcia De Senna Carneiro & Cláudia Torres Codeço & Gabriel Souza Machado & Lucas Saraiva Ferreira & Líliam César De Castro Medeiros & Clodoveu A, 2016. "DengueME: A Tool for the Modeling and Simulation of Dengue Spatiotemporal Dynamics," IJERPH, MDPI, vol. 13(9), pages 1-21, September.
    7. 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.
    8. David H Chae & Sean Clouston & Mark L Hatzenbuehler & Michael R Kramer & Hannah L F Cooper & Sacoby M Wilson & Seth I Stephens-Davidowitz & Robert S Gold & Bruce G Link, 2015. "Association between an Internet-Based Measure of Area Racism and Black Mortality," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-12, April.
    9. Xiaoli Wang & Shuangsheng Wu & C Raina MacIntyre & Hongbin Zhang & Weixian Shi & Xiaomin Peng & Wei Duan & Peng Yang & Yi Zhang & Quanyi Wang, 2015. "Using an Adjusted Serfling Regression Model to Improve the Early Warning at the Arrival of Peak Timing of Influenza in Beijing," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-14, March.
    10. Ishani Chaudhuri & Parthajit Kayal, 2022. "Predicting Power of Ticker Search Volume in Indian Stock Market," Working Papers 2022-214, Madras School of Economics,Chennai,India.
    11. Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
    12. Markowitz, Sara & Nesson, Erik & Robinson, Joshua J., 2019. "The effects of employment on influenza rates," Economics & Human Biology, Elsevier, vol. 34(C), pages 286-295.
    13. Bentzen, Jeanet Sinding, 2021. "In crisis, we pray: Religiosity and the COVID-19 pandemic," Journal of Economic Behavior & Organization, Elsevier, vol. 192(C), pages 541-583.
    14. Jesse T. Richman & Ryan J. Roberts, 2023. "Assessing Spurious Correlations in Big Search Data," Forecasting, MDPI, vol. 5(1), pages 1-12, February.
    15. Sakirul Khan & Sheikh Mohammad Fazle Akbar & Takaaki Yahiro & Mamun Al Mahtab & Kazunori Kimitsuki & Takehiro Hashimoto & Akira Nishizono, 2022. "Dengue Infections during COVID-19 Period: Reflection of Reality or Elusive Data Due to Effect of Pandemic," IJERPH, MDPI, vol. 19(17), pages 1-12, August.
    16. Linus Schiöler & Marianne Fris�n, 2012. "Multivariate outbreak detection," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(2), pages 223-242, April.
    17. Sasikiran Kandula & Jeffrey Shaman, 2019. "Reappraising the utility of Google Flu Trends," PLOS Computational Biology, Public Library of Science, vol. 15(8), pages 1-16, August.
    18. Shengzhang Dong & George Dimopoulos, 2023. "Aedes aegypti Argonaute 2 controls arbovirus infection and host mortality," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    19. Zhao, Xinxing & Li, Kainan & Ang, Candice Ke En & Cheong, Kang Hao, 2023. "A deep learning based hybrid architecture for weekly dengue incidences forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    20. Eunha Shim, 2017. "Cost-effectiveness of dengue vaccination in Yucatán, Mexico using a dynamic dengue transmission model," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-17, April.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pntd00:0005871. 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: plosntds (email available below). General contact details of provider: https://journals.plos.org/plosntds/ .

    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.