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Ecological and demographic drivers of Oropouche virus transmission

Nature Health volume 1, pages 487–496 (2026) Cite this article

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Abstract

Oropouche virus (OROV) is a neglected arbovirus that has caused outbreaks in Central and South America since the 1950s. Here we investigate the ecological and demographic determinants of Oropouche fever in Brazil between 2014 and 2025. During this period, 30,086 laboratory-confirmed Oropouche fever cases were reported across 894 (16.1%) of 5,570 municipalities in all 26 states and the Federal District. Of the confirmed cases, 14,651 (48.7%) were female and 15,422 (51.3%) were male. The cumulative incidence of Oropouche fever cases in rural municipalities was 11.3 times higher than in urban municipalities between 2014 and 2025. OROV exhibited a median urban-to-rural case ratio of 0.6, while dengue, chikungunya and Zika ratios range from 2.5 to 2.8. OROV transmission fluctuated in the North Region before its 2024 geographic expansion, with transmission peaks (Rt) ranging from 3.2 to 5.5. Our risk maps revealed significant heterogeneity in OROV risk across Brazil, driven by the interactions among demographic, climatic and environmental conditions. Our findings provide a comprehensive assessment of the ecological and demographic characteristics of Oropouche fever in Brazil and improve the understanding of its transmission dynamics.

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Fig. 1: Spatiotemporal dynamics of Oropouche fever cases in Brazil from 2014 to 2025.
The alternative text for this image may have been generated using AI.
Fig. 2: Demographic characteristics of Oropouche fever cases in Brazil from 2014 to 2025.
The alternative text for this image may have been generated using AI.
Fig. 3: Population distribution of Oropouche fever cases in Brazil from 2014 to 2025.
The alternative text for this image may have been generated using AI.
Fig. 4: Transmission dynamics of Oropouche fever cases in Brazil from 2014 to 2025.
The alternative text for this image may have been generated using AI.
Fig. 5: Municipality-level risk maps of OROV outbreaks in Brazil following an OROV case introduction, based on monthly climate normals.
The alternative text for this image may have been generated using AI.

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Data availability

All data used to generate the figures in this paper are publicly available on GitHub (https://github.com/wmarciel/Oropouche_Ecology). Deidentified individual-level data from the Brazilian Ministry of Health can be provided for research purposes after approval by a committee on human experimentation (if applicable). This data can be obtained upon request to W.M.d.S. (wmdesouza@uky.edu). The estimated response time may be up to 3 weeks.

Code availability

All codes used to generate the figures in this paper are publicly available on GitHub (https://github.com/wmarciel/Oropouche_Ecology). No custom code was developed in the study.

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Acknowledgements

W.M.d.S. was supported by the Burroughs Wellcome Fund—Climate Change and Human Health Seed Grants (1022448). W.M.d.S. and N.R.F. were supported by the Wellcome Trust—Digital Technology Development Award in Climate Sensitive Infectious Disease Modelling (226075/Z/22/Z) and the Wellcome Trust Dengue and Zika Immunology and Genomics Multi-Country Network (DeZi Network; 316633/Z/24/Z). J.L.P.-M. is supported by the São Paulo Research Foundation (2022/10442-0), National Council for Scientific and Technological Development (CNPq, 309971/2023-3), and A.I.B. was supported by Cornell Atkinson Center seed funding. Y.S. and M.A.S. were supported by US National Institutes of Health grants R01 AI153044 and U19 AI135995. We thank J. A. Tida (www.plotmyscience.com) for the figure editing.

Author information

Author notes

  1. These authors contributed equally: Xinyi Hua, Laura W. Alexander.

  2. These authors jointly supervised this work: Ana I. Bento, William M. de Souza.

Authors and Affiliations

  1. Department of Microbiology, Immunology and Molecular Genetics, College of Medicine, University of Kentucky, Lexington, KY, USA

    Xinyi Hua  (华心怡), Ingra M. Claro, Shirlene T. S. de Lima & William M. de Souza

  2. Department of Public and Ecosystem Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA

    Laura W. Alexander & Ana I. Bento

  3. Department of Integrative Biology, University of California, Berkeley, Berkeley, CA, USA

    Laura W. Alexander

  4. Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA, USA

    Yucai Shao & Marc A. Suchard

  5. Centro Nacional de Inteligência Epidemiológica, Secretária de Vigilância em Saúde, Ministério da Saúde, Brasília, Brazil

    Ronaldo de Jesus & Rodrigo B. Kato

  6. Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

    Ronaldo de Jesus & Rodrigo B. Kato

  7. Laboratório Central de Saúde Pública do Ceará, Fortaleza, Brazil

    Shirlene T. S. de Lima

  8. Department of Genetics, Evolution, Microbiology and Immunology, Institute of Biology, University of Campinas, Campinas, Brazil

    Shirlene T. S. de Lima & José Luiz Proenca-Modena

  9. Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, Bloomington, IN, USA

    Paulo C. Ventura & Marco Ajelli

  10. Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, IN, USA

    André B. B. Wilke

  11. Department of Biomathematics, University of California, Los Angeles, Los Angeles, CA, USA

    Marc A. Suchard

  12. Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA

    Marc A. Suchard

  13. MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK

    Nuno R. Faria

  14. Instituto de Medicina Tropical, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil

    Nuno R. Faria

  15. Department of Microbiology and Immunology, University of Texas Medical Branch, Galveston, TX, USA

    Laura B. Dickson

Authors
  1. Xinyi Hua  (华心怡)
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Contributions

X.H., L.W.A., A.I.B. and W.M.d.S. conceptualized the study. X.H., L.W.A., I.M.C., S.T.S.d.L., A.I.B. and W.M.d.S. contributed to the acquisition of data. X.H., L.W.A., I.M.C., Y.S., R.d.J., P.C.V., R.B.K., M.A., A.B.B.W., M.A.S., N.R.F., A.I.B. and W.M.d.S. contributed to the data analysis. X.H., L.W.A., Y.S., M.A., M.A.S., N.R.F., J.L.P.-M., L.B.D., A.I.B. and W.M.d.S. contributed to data interpretation. X.H., L.W.A., Y.S., A.I.B. and W.M.d.S. drafted the paper. X.H., L.W.A., M.A., A.B.B.W., M.A.S., N.R.F., J.L.P.-M., L.B.D., A.I.B. and W.M.d.S. revised the paper. A.I.B. and W.M.d.S. acquired funding for the study. The authors acknowledge funding from the MRC Centre for Global Infectious Disease Analysis (MR/R015600/1), jointly funded by the UK Medical Research Council (MRC) and the UK Foreign, Commonwealth & Development Office (FCDO), under the MRC/FCDO Concordat agreement, and also part of the EDCTP2 programme supported by the European Union. All authors read and approved the final version of the paper and the submission. X.H., A.I.B. and W.M.d.S. accessed and verified all the data reported in the study. X.H. and L.W.A. contributed equally to this work. A.I.B. and W.M.d.S. jointly supervised this work.

Corresponding authors

Correspondence to Ana I. Bento or William M. de Souza.

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Extended data

Extended Data Fig. 1 Digital surveillance of Oropouche in Brazil.

A) Timeline of Google Trends activity for the term ‘Oropouche’ in Brazil from January 2014 to September 2025. B) Correlation between Google Trends activity and log10-transformed laboratory confirmed Oropouche fever cases per state (n = 27) from January 2014 to September 2025 (right). The correlation was calculated using the two-sided Spearman’s rank correlation coefficient (ρ = 0.6467, p < 0.0001). The black line represents the linear regression fit, and the light gray ribbon indicates 95% confidence intervals. AC, Acre; AL, Alagoas; AM, Amazonas; AP, Amapá; BA, Bahia; CE, Ceará; DF, Distrito Federal; ES, Espírito Santo; GO, Goiás; MA, Maranhão; MG, Minas Gerais; MS, Mato Grosso do Sul; MT, Mato Grosso; PA, Pará; PB, Paraíba; PE, Pernambuco; PI, Piauí; PR, Paraná; RJ, Rio de Janeiro; RN, Rio Grande do Norte; RO, Rondônia; RR, Roraima; RS, Rio Grande do Sul; SC, Santa Catarina; SE, Sergipe; SP, São Paulo; TO, Tocantins.

Extended Data Fig. 2 The distribution of diagnosis methods and types of biological samples collected for Oropouche virus detection in Brazil between 2014 and 2025.

A) Percentages of laboratory-confirmed Oropouche fever cases detected by RT-qPCR and serological methods per year from 2014 to 2025. B) Number of blood and other biological samples used to detect Oropouche virus per year from 2014 to 2025. C) Percentages of blood and other biological samples used to detect Oropouche virus per year from 2014 to 2025. D) Number of biological samples other than blood used to detected Oropouche virus per year from 2018 to 2025.

Extended Data Fig. 3 The epidemiological curve of Oropouche fever cases at national, rural, and urban levels from 2014 to 2025.

Oropouche fever laboratory-confirmed cases per epidemiological week at national (n = 203,080,756), rural (n = 25,572,339), and urban (n = 177,508,417) levels, from epidemiological week 1 of 2014 (December 29 to January 4) to epidemiological week 37 of 2025 (September 7 to 13).

Extended Data Fig. 4 Demographic characteristics of individuals diagnosed with Oropouche fever in Brazil from 2014 to 2025 based on municipality locations.

A) Oropouche fever incidence based on age-sex distribution of cases from 2023 to 2025 in rural municipalities. B) Oropouche fever incidence based on age-sex distribution of cases from 2023 to 2025 in urban municipalities.

Extended Data Fig. 5 Oropouche virus testing positivity rate by 26 Brazilian states and the Federal District for 2024 and 2025.

AC = Acre. AM = Amazonas. PA = Pará. RR = Roraima. RO = Rondônia. AP = Amapá. TO = Tocantins. PI = Piauí. BA = Bahia. MA = Maranhão. PE = Pernambuco. CE = Ceará. AL = Alagoas. SE = Sergipe. RN = Rio Grande do Norte. PB = Paraíba. MT = Mato Grosso. GO = Goiás. MS = Mato Grosso do Sul. DF = Distrito Federal (Federal District). MG = Minas Gerais. ES = Espírito Santo. RJ = Rio de Janeiro. SP = São Paulo. PR = Paraná. SC = Santa Catarina. RS = Rio Grande do Sul.

Extended Data Fig. 6 R estimates for Oropouche virus based on 590 concatenated large, medium, and small genomic segments from August 2010 to August 2024.

The posterior distribution of the viral effective reproductive number (Re) is represented as posterior mean (dark blue) and 95% CrIs (light blue ribbon).

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Hua, X., Alexander, L.W., Claro, I.M. et al. Ecological and demographic drivers of Oropouche virus transmission. Nat. Health 1, 487–496 (2026). https://doi.org/10.1038/s44360-026-00065-6

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