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Letter

Model-based projections of Zika virus infections in childbearing women in the Americas

  • Nature Microbiology 1, Article number: 16126 (2016)
  • doi:10.1038/nmicrobiol.2016.126
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Abstract

Zika virus is a mosquito-borne pathogen that is rapidly spreading across the Americas. Due to associations between Zika virus infection and a range of fetal maladies1,2, the epidemic trajectory of this viral infection poses a significant concern for the nearly 15 million children born in the Americas each year. Ascertaining the portion of this population that is truly at risk is an important priority. One recent estimate3 suggested that 5.42 million childbearing women live in areas of the Americas that are suitable for Zika occurrence. To improve on that estimate, which did not take into account the protective effects of herd immunity, we developed a new approach that combines classic results from epidemiological theory with seroprevalence data and highly spatially resolved data about drivers of transmission to make location-specific projections of epidemic attack rates. Our results suggest that 1.65 (1.45–2.06) million childbearing women and 93.4 (81.6–117.1) million people in total could become infected before the first wave of the epidemic concludes. Based on current estimates of rates of adverse fetal outcomes among infected women2,4,5, these results suggest that tens of thousands of pregnancies could be negatively impacted by the first wave of the epidemic. These projections constitute a revised upper limit of populations at risk in the current Zika epidemic, and our approach offers a new way to make rapid assessments of the threat posed by emerging infectious diseases more generally.

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References

  1. 1.

    et al. Zika virus associated with microcephaly. New Engl. J. Med. 374, 951–958 (2016).

  2. 2.

    et al. Zika virus infection in pregnant women in Rio de Janeiro—preliminary report. New Engl. J. Med. (2016).

  3. 3.

    et al. Mapping global environmental suitability for Zika virus. eLife 5, 15272 (2016).

  4. 4.

    et al. Association between Zika virus infection and microcephaly in French Polynesia, 2013–2015: a retrospective study. Lancet 387, 2125–2132 (2016).

  5. 5.

    et al. Risk estimates for microcephaly related to Zika virus infection—from French Polynesia to Bahia, Brazil. Preprint at (2016).

  6. 6.

    WHO Statement on the First Meeting of the International Health Regulations (2005) (IHR 2005) Emergency Committee on Zika Virus and Observed Increase in Neurological Disorders and Neonatal Malformations (WHO, 2016);

  7. 7.

    et al. The global distribution of the arbovirus vectors Aedes aegypti and Ae. albopictus. eLife 4, e08347 (2015).

  8. 8.

    Zika—Epidemiological Update (Pan American Health Organization, 2016).

  9. 9.

    & The emerging Zika pandemic: enhancing preparedness. J. Am. Med. Assoc. 315, 865–866 (2016).

  10. 10.

    The next steps on Zika. Nature 530, 5 (2016).

  11. 11.

    & Species distribution models: ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. System. 40, 677–697 (2009).

  12. 12.

    & A contribution to the mathematical theory of epidemics. Proc. R. Soc. A 115, 700–721 (1927).

  13. 13.

    & Modeling Infectious Diseases in Humans and Animals (Princeton Univ. Press, 2007).

  14. 14.

    et al. High-resolution gridded population datasets for Latin America and the Caribbean in 2010, 2015, and 2020. Sci. Data 2, 150045 (2015).

  15. 15.

    et al. Seroprevalence of chikungunya virus (CHIKV) infection on Lamu Island, Kenya, October 2004. Am. J. Trop. Med. Hyg. 78, 333–337 (2008).

  16. 16.

    et al. Ross, Macdonald, and a theory for the dynamics and control of mosquito-transmitted pathogens. PLOS Pathogens 8, e1002588 (2012).

  17. 17.

    et al. Global temperature constraints on Aedes aegypti and Ae. albopictus persistence and competence for dengue virus transmission. Parasites Vector. 7, 338 (2014).

  18. 18.

    & The incubation periods of dengue viruses. PLoS ONE 7, e50972 (2012).

  19. 19.

    et al. Comparative analysis of dengue and Zika outbreaks reveals differences by setting and virus. Preprint at (2016).

  20. 20.

    et al. Texas lifestyle limits transmission of dengue virus. Emerg. Infect. Dis. 9, 86–89 (2003).

  21. 21.

    The final size of an epidemic and its relation to the basic reproduction number. Bull. Math. Biol. 73, 2305–2321 (2011).

  22. 22.

    et al. Determinants of heterogeneous blood feeding patterns by Aedes aegypti in Iquitos, Peru. PLoS Negl. Trop. Dis. 8, e2702 (2014).

  23. 23.

    et al. The global distribution and burden of dengue. Nature 496, 504–507 (2013).

  24. 24.

    , , , & Dengue on islands: a Bayesian approach to understanding the global ecology of dengue viruses. Trans. R. Soc. Trop. Med. Hyg. 109, 303–312 (2015).

  25. 25.

    The State of the World's Midwifery 2014 (UNFPA, 2014).

  26. 26.

    et al. Millenium development health metrics: where do Africa's children and women of childbearing age live? Popul. Health Metrics 11, 11 (2013).

  27. 27.

    et al. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).

  28. 28.

    Geography and macroeconomics: new data and new findings. Proc. Natl Acad. Sci. USA 103, 3510–3517 (2006).

  29. 29.

    et al. Stochasticity and the limits to confidence when estimating R0 of Ebola and other emerging infectious diseases. Preprint at (2016).

  30. 30.

    et al. Use of serological surveys to generate key insights into the changing global landscape of infectious disease. Lancet (2016).

  31. 31.

    et al. Mapping for maternal and newborn health: the distributions of women of childbearing age, pregnancies and births. Int. J. Health Geogr. 13, 2 (2013).

  32. 32.

    et al. Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data. PLoS ONE 10, e0107042 (2015).

  33. 33.

    World Population Prospects: The 2015 Revision (UN, 2015).

  34. 34.

    et al. An effective approach for gap-filling continental scale remotely sensed time-series. ISPRS J. Photogr. Remote Sens. 98, 106–118 (2014).

  35. 35.

    The World Factbook (US Central Intelligence Agency, 2016);

  36. 36.

    et al. Estimating chikungunya prevalence in Reunion Island outbreak by serosurveys: two methods for two critical times of the epidemic. BMC Infect. Dis. 8, 99 (2008).

  37. 37.

    Correlations between incidence and abundance are expected by chance. J. Biogeogr. 18, 463–466 (1991).

  38. 38.

    & Shape constrained additive models. Statist. Comput. 25, 543–559 (2015).

  39. 39.

    et al. Modelling adult Aedes aegypti and Aedes albopictus survival at different temperatures in laboratory and field settings. Parasites Vector. 6, 351–362 (2013).

  40. 40.

    & Aedes aegypti survival and dispersal estimated by mark–release–recapture in northern Australia. Am. J. Trop. Med. Hyg. 58, 277–282 (1998).

  41. 41.

    & Natural history of dengue virus (DENV)-1 and DENV-4 infections: reanalysis of classic studies. J. Infect. Dis. 195, 1007–1013 (2007).

  42. 42.

    et al. Longitudinal studies of Aedes aegypti (Diptera: Culicidae) in Thailand and Puerto Rico: blood feeding frequency. J. Med. Entomol. 37, 89–101 (2000).

  43. 43.

    et al. A systematic review of mathematical models of mosquito-borne pathogen transmission: 1970–2010. J. R. Soc. Interface 10, 20120921 (2013).

  44. 44.

    et al. Times to key events in the course of Zika infection and their implications for surveillance: a systematic review and pooled analysis. Preprint at (2016).

  45. 45.

    & Generality of the final size formula for an epidemic of a newly invading infectious disease. Bull. Math. Biol. 68, 679–702 (2006).

  46. 46.

    et al. Big city, small world: density, contact rates, and transmission of dengue across Pakistan. J. R. Soc. Interface 12, 20150468 (2015).

  47. 47.

    R Core Team R: A Language for Statistical Computing (R Foundation for Statistical Computing, 2014);

  48. 48.

    et al. Zika virus outbreak on Yap Island, Federated States of Micronesia. New Engl. J. Med. 360, 2536–2543 (2009).

  49. 49.

    et al. Seroprevalence of antibodies against chikungunya, dengue, and Rift Valley fever viruses after febrile illness outbreak, Madagascar. Emerg. Infect. Dis. 18, 1780–1786 (2012).

  50. 50.

    et al. Outbreak of chikungunya fever in Mayotte, Comoros archipelago, 2005–2006. Trans. R. Soc. Trop. Med. Hyg. 102, 780–786 (2008).

  51. 51.

    et al. Rapid spread of chikungunya virus infection in Orissa, India. Indian J. Med. Res. 133, 316–321 (2011).

  52. 52.

    et al. Seroprevalence and asymptomatic rates of Asian lineage chikungunya virus infection on Saint Martin, Caribbean. Am. J. Trop. Med. Hyg. 94, 393–396 (2015).

  53. 53.

    et al. Chikungunya virus in north-eastern Italy: a seroprevalence study. Am. J. Trop. Med. Hyg. 82, 508–511 (2010).

  54. 54.

    et al. Clinical attack rate of chikungunya in a cohort of Nicaraguan children. Am. J. Trop. Med. Hyg. 94, 397–399 (2016).

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Acknowledgements

The authors thank three anonymous reviewers, as well as J. Ashander, C.M. Barker, M.A. Johansson, R.C. Reiner, S.T. Stoddard, J.C. Miller and members of the Perkins Lab for discussions. The authors thank O.J. Brady for sharing code for calculating mosquito mortality as a function of temperature. T.A.P., A.S.S. and A.J.T. are supported by funding from the National Science Foundation (DEB 1641130). C.W.R. is supported by funding through the University of Southampton's Economic and Social Research Council's Doctoral Training Centre. M.U.G.K. receives funding from the International Research Consortium on Dengue Risk Assessment Management and Surveillance (IDAMS; European Commission 7th Framework Programme, 21893). A.J.T. is supported by funding from NIH/NIAID (U19AI089674), the BMGF (OPP1106427, 1032350), NORAD and a Wellcome Trust Sustaining Health Grant (106866/Z/15/Z). A.J.T. and C.W.R. acknowledge the support of the WorldPop (www.worldpop.org) and Flowminder Foundation (www.flowminder.org) teams in demographic data set production, and T.A.P. and A.S.S. acknowledge support from the Notre Dame Center for Research Computing.

Author information

Affiliations

  1. Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, 100 Galvin Hall, Notre Dame, Indiana 46556, USA

    • T. Alex Perkins
    •  & Amir S. Siraj
  2. WorldPop Project, Department of Geography and Environment, University of Southampton, Southampton SO17 1BJ, UK

    • Corrine W. Ruktanonchai
    •  & Andrew J. Tatem
  3. Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford OX1 3PS, UK

    • Moritz U. G. Kraemer
  4. Flowminder Foundation, SE-11355 Stockholm, Sweden

    • Andrew J. Tatem

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Contributions

T.A.P. conceived the research, designed the analysis and wrote the first draft of the manuscript. A.S.S. assembled data, performed calculations and contributed to writing. C.W.R. assembled data, produced map visuals and contributed to writing. M.U.G.K. assembled data and contributed to writing. A.J.T. contributed to the analysis, map visuals and writing.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to T. Alex Perkins.

Supplementary information

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    Supplementary information

    Supplementary Table 1, Supplementary Figures 1–10