IDEAS home Printed from https://ideas.repec.org/a/plo/pntd00/0005729.html
   My bibliography  Save this article

Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level

Author

Listed:
  • Cecilia de Almeida Marques-Toledo
  • Carolin Marlen Degener
  • Livia Vinhal
  • Giovanini Coelho
  • Wagner Meira
  • Claudia Torres Codeço
  • Mauro Martins Teixeira

Abstract

Background: Infectious diseases are a leading threat to public health. Accurate and timely monitoring of disease risk and progress can reduce their impact. Mentioning a disease in social networks is correlated with physician visits by patients, and can be used to estimate disease activity. Dengue is the fastest growing mosquito-borne viral disease, with an estimated annual incidence of 390 million infections, of which 96 million manifest clinically. Dengue burden is likely to increase in the future owing to trends toward increased urbanization, scarce water supplies and, possibly, environmental change. The epidemiological dynamic of Dengue is complex and difficult to predict, partly due to costly and slow surveillance systems. Methodology / Principal findings: In this study, we aimed to quantitatively assess the usefulness of data acquired by Twitter for the early detection and monitoring of Dengue epidemics, both at country and city level at a weekly basis. Here, we evaluated and demonstrated the potential of tweets modeling for Dengue estimation and forecast, in comparison with other available web-based data, Google Trends and Wikipedia access logs. Also, we studied the factors that might influence the goodness-of-fit of the model. We built a simple model based on tweets that was able to ‘nowcast’, i.e. estimate disease numbers in the same week, but also ‘forecast’ disease in future weeks. At the country level, tweets are strongly associated with Dengue cases, and can estimate present and future Dengue cases until 8 weeks in advance. At city level, tweets are also useful for estimating Dengue activity. Our model can be applied successfully to small and less developed cities, suggesting a robust construction, even though it may be influenced by the incidence of the disease, the activity of Twitter locally, and social factors, including human development index and internet access. Conclusions: Tweets association with Dengue cases is valuable to assist traditional Dengue surveillance at real-time and low-cost. Tweets are able to successfully nowcast, i.e. estimate Dengue in the present week, but also forecast, i.e. predict Dengue at until 8 weeks in the future, both at country and city level with high estimation capacity. Author summary: Dengue is a fast-growing mosquito-borne viral disease, with an estimated annual incidence of 390 million infections, of which 96 million manifest clinically. Dengue burden is likely to increase in the future. Mentioning a disease in social networks is correlated with physician visits by patients, and can be used to estimate disease activity. Traditional, biologically-focused monitoring techniques, based on laboratory diagnostics, are accurate but costly and slow. Alternative approaches for surveillance aim to capture health-seeking behavior at earlier stages of disease progression, specially capturing the asymptomatic and mild clinic manifestation population who do not seek medical care formally. Twitter data have potential application for Dengue surveillance, improving the estimation and prediction of the disease, in space and time, being a valuable and low-cost addition to assist traditional surveillance. We show that tweets are strongly associated with Dengue cases. Tweets are a useful tool for estimating and forecasting Dengue cases until 8 weeks in the future, both at country and city level, even in less developed areas.

Suggested Citation

  • Cecilia de Almeida Marques-Toledo & Carolin Marlen Degener & Livia Vinhal & Giovanini Coelho & Wagner Meira & Claudia Torres Codeço & Mauro Martins Teixeira, 2017. "Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 11(7), pages 1-20, July.
  • Handle: RePEc:plo:pntd00:0005729
    DOI: 10.1371/journal.pntd.0005729
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0005729
    Download Restriction: no

    File URL: https://journals.plos.org/plosntds/article/file?id=10.1371/journal.pntd.0005729&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pntd.0005729?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
    ---><---

    Citations

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


    Cited by:

    1. Prashant Rangarajan & Sandeep K Mody & Madhav Marathe, 2019. "Forecasting dengue and influenza incidences using a sparse representation of Google trends, electronic health records, and time series data," PLOS Computational Biology, Public Library of Science, vol. 15(11), pages 1-24, November.
    2. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    3. 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.
    4. Alexandre Gori Maia & Jose Daniel Morales Martinez & Leticia Junqueira Marteleto & Cristina Guimaraes Rodrigues & Luiz Gustavo Sereno, 2023. "Can the Content of Social Networks Explain Epidemic Outbreaks?," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 42(1), pages 1-34, February.

    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:pntd00:0005729. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.