The global distribution and burden of dengue

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Dengue is a systemic viral infection transmitted between humans by Aedes mosquitoes1. For some patients, dengue is a life-threatening illness2. There are currently no licensed vaccines or specific therapeutics, and substantial vector control efforts have not stopped its rapid emergence and global spread3. The contemporary worldwide distribution of the risk of dengue virus infection4 and its public health burden are poorly known2, 5. Here we undertake an exhaustive assembly of known records of dengue occurrence worldwide, and use a formal modelling framework to map the global distribution of dengue risk. We then pair the resulting risk map with detailed longitudinal information from dengue cohort studies and population surfaces to infer the public health burden of dengue in 2010. We predict dengue to be ubiquitous throughout the tropics, with local spatial variations in risk influenced strongly by rainfall, temperature and the degree of urbanization. Using cartographic approaches, we estimate there to be 390 million (95% credible interval 284–528) dengue infections per year, of which 96 million (67–136) manifest apparently (any level of disease severity). This infection total is more than three times the dengue burden estimate of the World Health Organization2. Stratification of our estimates by country allows comparison with national dengue reporting, after taking into account the probability of an apparent infection being formally reported. The most notable differences are discussed. These new risk maps and infection estimates provide novel insights into the global, regional and national public health burden imposed by dengue. We anticipate that they will provide a starting point for a wider discussion about the global impact of this disease and will help to guide improvements in disease control strategies using vaccine, drug and vector control methods, and in their economic evaluation.

At a glance


  1. Global estimates of total dengue infections.
    Figure 1: Global estimates of total dengue infections.

    Comparison of previous estimates of total global dengue infections in individuals of all ages, 1985–2010. Black triangle, ref. 5; dark blue triangle, ref. 15; green triangle, ref. 17; orange triangle, ref. 16; light blue triangle, ref. 30; pink triangle, ref. 10; red triangle, apparent infections from this study. Estimates are aligned to the year of estimate and, if not stated, aligned to the publication date. Red shading marks the credible interval of our current estimate, for comparison. Error bars from ref. 10 and ref. 16 replicated the confidence intervals provided in these publications.

  2. Global evidence consensus, risk and burden of dengue in 2010.
    Figure 2: Global evidence consensus, risk and burden of dengue in 2010.

    a, National and subnational evidence consensus on complete absence (green) through to complete presence (red) of dengue4. b, Probability of dengue occurrence at 5km×5km spatial resolution of the mean predicted map (area under the receiver operator curve of 0.81 (±0.02 s.d., n = 336)) from 336 boosted regression tree models. Areas with a high probability of dengue occurrence are shown in red and areas with a low probability in green. c, Cartogram of the annual number of infections for all ages as a proportion of national or subnational (China) geographical area.

Change history

Corrected online 24 April 2013
Minor changes were made to the text about disease severity, and an additional citation to ref. 6 was added.


  1. Simmons, C. P., Farrar, J. J., van Vinh Chau, N. & Wills, B. Dengue. N. Engl. J. Med. 366, 14231432 (2012)
  2. World Health Organization. Dengue: Guidelines for Diagnosis, Treatment, Prevention and Control. WHO/HTM/NTD/DEN/2009.1 (World Health Organization, 2009)
  3. Tatem, A. J., Hay, S. I. & Rogers, D. J. Global traffic and disease vector dispersal. Proc. Natl Acad. Sci. USA 103, 62426247 (2006)
  4. Brady, O. J. et al. Refining the global spatial limits of dengue virus transmission by evidence-based consensus. PLoS Negl. Trop. Dis. 6, e1760 (2012)
  5. Halstead, S. B. Pathogenesis of dengue: challenges to molecular biology. Science 239, 476481 (1988)
  6. Endy, T. P. et al. Determinants of inapparent and symptomatic dengue infection in a prospective study of primary school children in Kamphaeng Phet, Thailand. PLoS Negl. Trop. Dis. 5, e975 (2011)
  7. Sabchareon, A. et al. Protective efficacy of the recombinant, live-attenuated, CYD tetravalent dengue vaccine in Thai schoolchildren: a randomised, controlled phase 2b trial. Lancet 380, 15591567 (2012)
  8. Halstead, S. B. Dengue vaccine development: a 75% solution? Lancet 380, 15351536 (2012)
  9. Gubler, D. J. Dengue and dengue hemorrhagic fever. Clin. Microbiol. Rev. 11, 480496 (1998)
  10. Beatty, M. E., Letson, G. W. & Margolis, H. S. Estimating the global burden of dengue. Am. J. Trop. Med. Hyg. 81 (Suppl. 1). 231 (2009)
  11. Van Kleef, E., Bambrick, H. & Hales, S. The geographic distribution of dengue fever and the potential influence of global climate change. TropIKA. net (2009)
  12. World Health Organization. International Travel and Health: Situation as on 1 January 2012 (World Health Organization, 2012)
  13. Hales, S., de Wet, N., Maindonald, J. & Woodward, A. Potential effect of population and climate changes on global distribution of dengue fever: an empirical model. Lancet 360, 830834 (2002)
  14. Rogers, D. J., Wilson, A. J., Hay, S. I. & Graham, A. J. The global distribution of yellow fever and dengue. Adv. Parasitol. 62, 181220 (2006)
  15. Monath, T. P. Yellow fever and dengue-the interactions of virus, vector and host in the re-emergence of epidemic disease. Semin. Virol. 5, 133145 (1994)
  16. Rigau-Pérez, J. G. et al. Dengue and dengue haemorrhagic fever. Lancet 352, 971977 (1998)
  17. Rodhain, F. La situation de la dengue dans le monde. Bull. Soc. Pathol. Exot. 89, 8790 (1996)
  18. Freifeld, C. C., Mandl, K. D., Reis, B. Y. & Brownstein, J. S. HealthMap: global infectious disease monitoring through automated classification and visualization of Internet media reports. J. Am. Med. Inform. Assoc. 15, 150157 (2008)
  19. Chakravarti, A., Arora, R. & Luxemburger, C. Fifty years of dengue in India. Trans. R. Soc. Trop. Med. Hyg. 106, 273282 (2012)
  20. Kakkar, M. Dengue fever is massively under-reported in India, hampering our response. Br. Med. J. 345, e8574 (2012)
  21. Murray, C. J. L. et al. Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 380, 21972223 (2012)
  22. Cummings, D. A. et al. The impact of the demographic transition on dengue in Thailand: insights from a statistical analysis and mathematical modeling. PLoS Med. 6, e1000139 (2009)
  23. Johansson, M. A., Hombach, J. & Cummings, D. A. Models of the impact of dengue vaccines: a review of current research and potential approaches. Vaccine 29, 58605868 (2011)
  24. Hay, S. I. et al. Estimating the global clinical burden of Plasmodium falciparum malaria in 2007. PLoS Med. 7, e1000290 (2010)
  25. Gething, P. W. et al. A new world malaria map: Plasmodium falciparum endemicity in 2010. Malar. J. 10, 378 (2011)
  26. Gething, P. W. et al. A long neglected world malaria map: Plasmodium vivax endemicity in 2010. PLoS Negl. Trop. Dis. 6, e1814 (2012)
  27. Anders, K. L. & Hay, S. I. Lessons from malaria control to help meet the rising challenge of dengue. Lancet Infect. Dis. 12, 977984 (2012)
  28. Gething, P. W. et al. Modelling the global constraints of temperature on transmission of Plasmodium falciparum and P. vivax. Parasites Vectors 4, 92 (2011)
  29. Chefaoui, R. M. & Lobo, J. M. Assessing the effects of pseudo-absences on predictive distribution model performance. Ecol. Modell. 210, 478486 (2008)
  30. TDR/World Health Organization. Report of the Scientific Working Group on Dengue, 2006. TDR/SWG/08 (TDR/World Health Organization, 2006)
  31. Brownstein, J. S., Freifeld, C. C., Reis, B. Y. & Mandl, K. D. Surveillance sans frontières: internet-based emerging infectious disease intelligence and the HealthMap project. PLoS Med. 5, e151 (2008)
  32. Scharlemann, J. P. W. et al. Global data for ecology and epidemiology: a novel algorithm for temporal Fourier processing MODIS data. PLoS ONE 3, e1408 (2008)
  33. Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 19651978 (2005)
  34. Focks, D. A., Haile, D. G., Daniels, E. & Mount, G. A. Dynamic life table model for Aedes aegypti (Diptera: Culcidae): analysis of the literature and model development. J. Med. Entomol. 30, 10031017 (1993)
  35. Focks, D. A., Haile, D. G., Daniels, E. & Mount, G. A. Dynamic life table model for Aedes aegypti (Diptera: Culicidae): simulation and validation. J. Med. Entomol. 30, 10181028 (1993)
  36. Hay, S. I., Tatem, A. J., Graham, A. J., Goetz, S. J. & Rogers, D. J. Global environmental data for mapping infectious disease distribution. Adv. Parasitol. 62, 3777 (2006)
  37. Hay, S. I. et al. A world malaria map: Plasmodium falciparum endemicity in 2007. PLoS Med. 6, e48 (2009)
  38. Nelson, A. Estimated travel time to the nearest city of 50,000 or more people in year 2000. (accessed 1 January 2012) (Global Environment Monitoring Unit – Joint Research Centre of the European Commission, 2008)
  39. Nordhaus, W. D. Geography and macroeconomics: new data and new findings. Proc. Natl Acad. Sci. USA 103, 35103517 (2006)
  40. Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129151 (2006)
  41. Stevens, K. B. & Pfeiffer, D. U. Spatial modelling of disease using data- and knowledge-driven approaches. Spat. Spatiotemporal Epidemiol. 2, 125133 (2011)
  42. Breiman, L. Classification and Regression Trees (Chapman & Hall/CRC, 1984)
  43. Friedman, J. H. Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 11891232 (2001)
  44. Stokland, J. N., Halvorsen, R. & Stoa, B. Species distribution modelling. Effect of design and sample size of pseudo-absence observations. Ecol. Modell. 222, 18001809 (2011)
  45. Lobo, J. M. & Tognelli, M. F. Exploring the effects of quantity and location of pseudo-absences and sampling biases on the performance of distribution models with limited point occurrence data. J. Nat. Conserv. 19, 17 (2011)
  46. VanDerWal, J., Shoo, L. P., Graham, C. & William, S. E. Selecting pseudo-absence data for presence-only distribution modeling: how far should you stray from what you know? Ecol. Modell. 220, 589594 (2009)
  47. Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: how, where and how many? Methods Ecol. Evol. 3, 327338 (2012)
  48. Ward, G., Hastie, T., Barry, S., Elith, J. & Leathwick, J. R. Presence-only data and the EM algorithm. Biometrics 65, 554563 (2009)
  49. Araújo, M. B. & New, M. Ensemble forecasting of species distributions. Trends Ecol. Evol. 22, 4247 (2007)
  50. Hilbe, J. M. Negative Binomial Regression 2nd edn, 251 (Cambridge Univ. Press, 2011)
  51. Banerjee, S., Carlin, B. P. & Gelfand, A. E. Hierarchical Modeling and Analysis for Spatial Data. Monographs on Statistics and Applied Probability 101 (Chapman & Hall/CRC, 2004)
  52. Patil, A., Huard, D. & Fonnesbeck, C. J. PyMC: Bayesian stochastic modelling in Python. J. Stat. Softw. 35, e1000301 (2010)
  53. Balk, D. L. et al. Determining global population distribution: methods, applications and data. Adv. Parasitol. 62, 119156 (2006)

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Author information


  1. Spatial Ecology and Epidemiology Group, Tinbergen Building, Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, UK

    • Samir Bhatt,
    • Peter W. Gething,
    • Oliver J. Brady,
    • Jane P. Messina,
    • Andrew W. Farlow,
    • Catherine L. Moyes,
    • John M. Drake,
    • Monica F. Myers,
    • G. R. William Wint &
    • Simon I. Hay
  2. Oxitec Limited, Milton Park, Abingdon OX14 4RX, UK

    • Oliver J. Brady
  3. Odum School of Ecology, University of Georgia, Athens, Georgia 30602, USA

    • John M. Drake
  4. Department of Pediatrics, Harvard Medical School and Children’s Hospital Informatics Program, Boston Children’s Hospital, Boston, Massachusetts 02115, USA

    • John S. Brownstein
  5. Department of Community and Family Medicine, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire 03755, USA

    • Anne G. Hoen
  6. INDEPTH Network Secretariat, East Legon, PO Box KD 213, Accra, Ghana

    • Osman Sankoh
  7. School of Public Health, University of the Witwatersrand, Braamfontein 2000, Johannesburg, South Africa

    • Osman Sankoh
  8. Institute of Public Health, University of Heidelberg, 69120 Heidelberg, Germany

    • Osman Sankoh
  9. Fogarty International Center, National Institutes of Health, Bethesda, Maryland 20892, USA

    • Dylan B. George,
    • Thomas W. Scott &
    • Simon I. Hay
  10. Section Clinical Tropical Medicine, Department of Infectious Diseases, Heidelberg University Hospital, INF 324, D 69120 Heidelberg, Germany

    • Thomas Jaenisch
  11. Environmental Research Group Oxford (ERGO), Tinbergen Building, Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, UK

    • G. R. William Wint
  12. Oxford University Clinical Research Unit, Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam

    • Cameron P. Simmons &
    • Jeremy J. Farrar
  13. Centre for Tropical Medicine, University of Oxford, Churchill Hospital, Oxford OX3 7LJ, UK

    • Cameron P. Simmons &
    • Jeremy J. Farrar
  14. Department of Entomology, University of California Davis, Davis, California 95616, USA

    • Thomas W. Scott
  15. Department of Medicine, National University of Singapore, 119228 Singapore

    • Jeremy J. Farrar


S.I.H. and J.J.F. conceived the research. S.B. and S.I.H. drafted the manuscript. S.B. drafted the Supplementary Information with significant support on sections A (O.J.B., C.L.M.), B (J.P.M., G.R.W.W.), C (P.W.G.), D (O.J.B., T.W.S.), and O.J.B. wrote section E. J.S.B. and A.G.H. provided HealthMap occurrence data and advice on its provenance. O.J.B. reviewed all the occurrence data. S.B. did the modelling and analysis with advice from J.M.D., P.W.G. and S.I.H. J.P.M. created all maps. All authors discussed the results and contributed to the revision of the final manuscript.

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The authors declare no competing financial interests.

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    This file contains Supplementary Information Sections A-F – see contents for details.

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