Genomic and epidemiological monitoring of yellow fever virus transmission potential

doi:10.1126/science.aat7115

. 2018 Aug 31;361(6405):894-899.

doi: 10.1126/science.aat7115. Epub 2018 Aug 23.

Genomic and epidemiological monitoring of yellow fever virus transmission potential

N R Faria¹, M U G Kraemer^{2

3

4}, S C Hill², J Goes de Jesus⁵, R S Aguiar⁶, F C M Iani^{7

8}, J Xavier⁵, J Quick⁹, L du Plessis², S Dellicour¹⁰, J Thézé², R D O Carvalho⁸, G Baele¹⁰, C-H Wu¹¹, P P Silveira⁶, M B Arruda⁶, M A Pereira⁷, G C Pereira⁷, J Lourenço², U Obolski², L Abade^{2

12}, T I Vasylyeva², M Giovanetti^{5

8}, D Yi¹³, D J Weiss¹⁴, G R W Wint², F M Shearer¹⁴, S Funk¹⁵, B Nikolay^{16

17}, V Fonseca^{8

18}, T E R Adelino⁷, M A A Oliveira⁷, M V F Silva⁷, L Sacchetto⁸, P O Figueiredo⁸, I M Rezende⁸, E M Mello⁸, R F C Said¹⁹, D A Santos¹⁹, M L Ferraz¹⁹, M G Brito¹⁹, L F Santana¹⁹, M T Menezes⁶, R M Brindeiro⁶, A Tanuri⁶, F C P Dos Santos²⁰, M S Cunha²⁰, J S Nogueira²⁰, I M Rocco²⁰, A C da Costa²¹, S C V Komninakis^{22

23}, V Azevedo⁸, A O Chieppe²⁴, E S M Araujo⁵, M C L Mendonça⁵, C C Dos Santos⁵, C D Dos Santos⁵, A M Mares-Guia⁵, R M R Nogueira⁵, P C Sequeira⁵, R G Abreu²⁵, M H O Garcia²⁵, A L Abreu²⁶, O Okumoto²⁶, E G Kroon⁸, C F C de Albuquerque²⁷, K Lewandowski²⁸, S T Pullan²⁸, M Carroll²⁹, T de Oliveira^{5

18

30}, E C Sabino²¹, R P Souza²⁰, M A Suchard^{31

32}, P Lemey¹⁰, G S Trindade⁸, B P Drumond⁸, A M B Filippis⁵, N J Loman⁹, S Cauchemez^{16

17}, L C J Alcantara^{33

8}, O G Pybus¹

Affiliations

¹ Department of Zoology, University of Oxford, Oxford, UK. nuno.faria@zoo.ox.ac.uk luiz.alcantara@ioc.fiocruz.br oliver.pybus@zoo.ox.ac.uk.
² Department of Zoology, University of Oxford, Oxford, UK.
³ Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA.
⁴ Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
⁵ Laboratório de Flavivírus, Instituto Oswaldo Cruz, FIOCRUZ, Rio de Janeiro, Brazil.
⁶ Laboratório de Virologia Molecular, Departamento de Genética, Instituto de Biologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
⁷ Laboratório Central de Saúde Pública, Instituto Octávio Magalhães, FUNED, Belo Horizonte, Minas Gerais, Brazil.
⁸ Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
⁹ Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK.
¹⁰ Department of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium.
¹¹ Department of Statistics, University of Oxford, Oxford, UK.
¹² The Global Health Network, University of Oxford, Oxford, UK.
¹³ Department of Statistics, Harvard University, Cambridge, MA, USA.
¹⁴ Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
¹⁵ Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.
¹⁶ Mathematical Modelling of Infectious Diseases and Center of Bioinformatics, Institut Pasteur, Paris, France.
¹⁷ CNRS UMR2000: Génomique Évolutive, Modélisation et Santé, Institut Pasteur, Paris, France.
¹⁸ KwaZulu-Natal Research, Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban, South Africa.
¹⁹ Secretaria de Estado de Saúde de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
²⁰ Núcleo de Doenças de Transmissão Vetorial, Instituto Adolfo Lutz, São Paulo, Brazil.
²¹ Instituto de Medicina Tropical e Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil.
²² Retrovirology Laboratory, Federal University of São Paulo, São Paulo, Brazil.
²³ School of Medicine of ABC (FMABC), Clinical Immunology Laboratory, Santo André, São Paulo, Brazil.
²⁴ Coordenação de Vigilância Epidemiológica do Estado do Rio de Janeiro, Rio de Janeiro, Brazil.
²⁵ Departamento de Vigilância das Doenças Transmissíveis da Secretaria de Vigilância em Saúde, Ministério da Saúde, Brasília-DF, Brazil.
²⁶ Secretaria de Vigilância em Saúde, Coordenação Geral de Laboratórios de Saúde Pública, Ministério da Saúde, Brasília-DF, Brazil.
²⁷ Organização Pan - Americana da Saúde/Organização Mundial da Saúde - (OPAS/OMS), Brasília-DF, Brazil.
²⁸ Public Health England, National Infections Service, Porton Down, Salisbury, UK.
²⁹ NIHR HPRU in Emerging and Zoonotic Infections, Public Health England, London, UK.
³⁰ Centre for the AIDS Programme of Research in South Africa (CAPRISA), Durban, South Africa.
³¹ Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles, CA, USA.
³² Department of Biomathematics and Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA.
³³ Laboratório de Flavivírus, Instituto Oswaldo Cruz, FIOCRUZ, Rio de Janeiro, Brazil. nuno.faria@zoo.ox.ac.uk luiz.alcantara@ioc.fiocruz.br oliver.pybus@zoo.ox.ac.uk.

PMID: 30139911
PMCID: PMC6874500
DOI: 10.1126/science.aat7115

Genomic and epidemiological monitoring of yellow fever virus transmission potential

N R Faria et al. Science. 2018.

. 2018 Aug 31;361(6405):894-899.

doi: 10.1126/science.aat7115. Epub 2018 Aug 23.

Authors

Affiliations

¹ Department of Zoology, University of Oxford, Oxford, UK. nuno.faria@zoo.ox.ac.uk luiz.alcantara@ioc.fiocruz.br oliver.pybus@zoo.ox.ac.uk.
² Department of Zoology, University of Oxford, Oxford, UK.
³ Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA.
⁴ Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
⁵ Laboratório de Flavivírus, Instituto Oswaldo Cruz, FIOCRUZ, Rio de Janeiro, Brazil.
⁶ Laboratório de Virologia Molecular, Departamento de Genética, Instituto de Biologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
⁷ Laboratório Central de Saúde Pública, Instituto Octávio Magalhães, FUNED, Belo Horizonte, Minas Gerais, Brazil.
⁸ Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
⁹ Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK.
¹⁰ Department of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium.
¹¹ Department of Statistics, University of Oxford, Oxford, UK.
¹² The Global Health Network, University of Oxford, Oxford, UK.
¹³ Department of Statistics, Harvard University, Cambridge, MA, USA.
¹⁴ Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
¹⁵ Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.
¹⁶ Mathematical Modelling of Infectious Diseases and Center of Bioinformatics, Institut Pasteur, Paris, France.
¹⁷ CNRS UMR2000: Génomique Évolutive, Modélisation et Santé, Institut Pasteur, Paris, France.
¹⁸ KwaZulu-Natal Research, Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban, South Africa.
¹⁹ Secretaria de Estado de Saúde de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
²⁰ Núcleo de Doenças de Transmissão Vetorial, Instituto Adolfo Lutz, São Paulo, Brazil.
²¹ Instituto de Medicina Tropical e Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil.
²² Retrovirology Laboratory, Federal University of São Paulo, São Paulo, Brazil.
²³ School of Medicine of ABC (FMABC), Clinical Immunology Laboratory, Santo André, São Paulo, Brazil.
²⁴ Coordenação de Vigilância Epidemiológica do Estado do Rio de Janeiro, Rio de Janeiro, Brazil.
²⁵ Departamento de Vigilância das Doenças Transmissíveis da Secretaria de Vigilância em Saúde, Ministério da Saúde, Brasília-DF, Brazil.
²⁶ Secretaria de Vigilância em Saúde, Coordenação Geral de Laboratórios de Saúde Pública, Ministério da Saúde, Brasília-DF, Brazil.
²⁷ Organização Pan - Americana da Saúde/Organização Mundial da Saúde - (OPAS/OMS), Brasília-DF, Brazil.
²⁸ Public Health England, National Infections Service, Porton Down, Salisbury, UK.
²⁹ NIHR HPRU in Emerging and Zoonotic Infections, Public Health England, London, UK.
³⁰ Centre for the AIDS Programme of Research in South Africa (CAPRISA), Durban, South Africa.
³¹ Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles, CA, USA.
³² Department of Biomathematics and Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA.
³³ Laboratório de Flavivírus, Instituto Oswaldo Cruz, FIOCRUZ, Rio de Janeiro, Brazil. nuno.faria@zoo.ox.ac.uk luiz.alcantara@ioc.fiocruz.br oliver.pybus@zoo.ox.ac.uk.

PMID: 30139911
PMCID: PMC6874500
DOI: 10.1126/science.aat7115

Abstract

The yellow fever virus (YFV) epidemic in Brazil is the largest in decades. The recent discovery of YFV in Brazilian Aedes species mosquitos highlights a need to monitor the risk of reestablishment of urban YFV transmission in the Americas. We use a suite of epidemiological, spatial, and genomic approaches to characterize YFV transmission. We show that the age and sex distribution of human cases is characteristic of sylvatic transmission. Analysis of YFV cases combined with genomes generated locally reveals an early phase of sylvatic YFV transmission and spatial expansion toward previously YFV-free areas, followed by a rise in viral spillover to humans in late 2016. Our results establish a framework for monitoring YFV transmission in real time that will contribute to a global strategy to eliminate future YFV epidemics.

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Conflict of interest statement

Competing interests

N.J.L. and L.C.J.A. received free-of-charge reagents in support of the project from Oxford Nanopore Technologies.

Figures

**Fig. 1. Spatial and temporal epidemiology of YFV and CHIKV in Minas Gerais, MG.**
**(A)** Time series of human YFV cases in MG (676 cases across 61 municipalities) confirmed by serology, RT-qPCR or virus isolation during the first YFV epidemic wave (Aug 2016 to Oct 2017). (B) Same as panel A, but showing NHP YFV cases (313 cases across 90 municipalities), confirmed by RT-qPCR. (C) Same as panel A, but for human CHIKV cases (3668 cases across 129 municipalities). (D) Geographic distribution of human YFV cases in MG. (E) Geographic distribution of NHP YFV cases in MG. Fig. S2 shows the corresponding geographic distribution of CHIKV cases. **(F)** Association between the number of human and NHP cases in each municipality of MG (Pearson’s r=0.62; p<0.0001; non-parametric Spearman’s rank ρ=0.32; p<0.05).

**Fig. 2. Age and sex distribution of YFV cases in Minas Gerais, 2016-2017.**
Red bars show the proportion of observed YFV cases in Minas Gerais that occur in each age class, in (A) males and (B) females. These empirical distributions are different from those predicted under two models of urban cycle transmission (M1 = white bars and M2 = orange bars; see text for details).

**Fig. 3. Molecular phylogenetics of the Brazilian YFV epidemic.**
(A) Maximum likelihood phylogeny of complete YFV genomes showing the outbreak clade (red triangle) within the SA1 genotype (see Figs. 4 and S6 for details). SA2, WAfr and EAfr indicate the South America II, West African, and East Africa genotypes, respectively. For clarity, five YFV strains introduced to Venezuela from Brazil (49) are not shown. The scale bar is in units of substitutions per site (s/s). Node labels indicate bootstrap support values. RO 2002 = strain BeH655417 from Roraima. MG 2003 = two strains from the previous YF outbreak in MG in 2003. 17DD = the vaccine strain used in Brazil. AO 2016 = YFV outbreak Angola in 2015-2016 (13). (B) Root-to-tip regression of sequence sampling date against genetic divergence from the root of the outbreak clade (see Fig. S6A). Sequences are coloured by sampling location. (C) Violin plots showing estimated posterior distributions (white circle=mean) of the time of the most common ancestor (TMRCA) of the outbreak clade. Estimates were obtained using two different datasets (grey=SA1 genotype, red=outbreak clade) and under different evolutionary models: a=uncorrelated lognormal relaxed clock (UCLN) model with a skygrid tree prior with covariates (specifically, the time series data shown in Figs. 1A-C; see Fig. S7); b=UCLN model with a skygrid tree prior without covariates; c=fixed local clock model (see Supplementary Materials).

**Fig. 4. Spatial and evolutionary dynamics of YFV outbreak.**
(A) Frequency of detection of YFV in non-human primates in the Americas (50). Circle sizes represent the proportion of published studies (n=15) that have detected YFV in each primate family and region. SA=South America (except Brazil), CA=Central America, CB=Caribbean, BR1=Brazil (before 2017), BR2=Brazil (this study). (B) Maximum clade credibility phylogeny inferred under a two-state (human and NHP) structured coalescent model. External node symbols denote sample type. Grey bars and labels to the indicate sample location (RJ=Rio de Janeiro, ES=Espírito Santo, BA=Bahia, others were sampled in MG). Internal nodes whose posterior state probabilities are >0.8 are annotated by circles. Node labels indicate posterior state probabilities for selected nodes. Internal branches are coloured blue for NHP, red for human. Fig. S8 shows a fully annotated tree. (C). The average number of YFV phylogenetic state transitions (from NHP to human) per month. Solid line=median estimate. Shaded area=95% BCI. (D) Expansion of the YFV epidemic wavefront estimated using a continuous phylogeographic approach (35). At each timepoint the plot shows the maximum spatial distance between phylogeny branches and the inferred location of outbreak origin. Solid line = median estimate. Shaded area = 95% BCI. **(E)** Reconstructed spatiotemporal diffusion of the YFV outbreak. Phylogeny branches are arranged in space according the locations of phylogeny nodes (circles). Locations of external nodes are known, whilst those of internal nodes are inferred (44). DF=Distrito Federal, GO=Goiás, SP=São Paulo. Shaded regions show 95% credible regions of internal nodes. Nodes and uncertainty regions are coloured according to time.

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Comment in

The reemergence of yellow fever.
Barrett ADT. Barrett ADT. Science. 2018 Aug 31;361(6405):847-848. doi: 10.1126/science.aau8225. Epub 2018 Aug 23. Science. 2018. PMID: 30139914 No abstract available.
Monitoring yellow fever.
Stower H. Stower H. Nat Med. 2018 Dec;24(12):1781. doi: 10.1038/s41591-018-0280-7. Nat Med. 2018. PMID: 30523315 No abstract available.

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References

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[1] Garske T, et al. Yellow Fever in Africa: estimating the burden of disease and impact of mass vaccination from outbreak and serological data. PLoS Medicine. 2014;11:e1001638. - PMC - PubMed

[2] Garske T, et al. Yellow Fever in Africa: estimating the burden of disease and impact of mass vaccination from outbreak and serological data. PLoS Medicine. 2014;11:e1001638. - PMC - PubMed

[3] Paules CI, Fauci AS. Yellow Fever - Once Again on the Radar Screen in the Americas. The New England Journal of Medicine. 2017:1397–1399. - PubMed

[4] Paules CI, Fauci AS. Yellow Fever - Once Again on the Radar Screen in the Americas. The New England Journal of Medicine. 2017:1397–1399. - PubMed

[5] Theiler M, Smith HH. The effect of prolonged cultivation in vitro upon the pathogenicity of yellow fever virus. J Exp Med. 1937;65:767–786. - PMC - PubMed

[6] Theiler M, Smith HH. The effect of prolonged cultivation in vitro upon the pathogenicity of yellow fever virus. J Exp Med. 1937;65:767–786. - PMC - PubMed

[7] Shearer FM, et al. Global yellow fever vaccination coverage from 1970 to 2016: an adjusted retrospective analysis. The Lancet Infectious Diseases. 2017;17:1209–1217. - PMC - PubMed

[8] Shearer FM, et al. Global yellow fever vaccination coverage from 1970 to 2016: an adjusted retrospective analysis. The Lancet Infectious Diseases. 2017;17:1209–1217. - PMC - PubMed

[9] Nunes MR, et al. Genomic and phylogenetic characterization of Brazilian yellow fever virus strains. Journal of Virology. 2012;86:13263–13271. - PMC - PubMed

[10] Nunes MR, et al. Genomic and phylogenetic characterization of Brazilian yellow fever virus strains. Journal of Virology. 2012;86:13263–13271. - PMC - PubMed

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Genomic and epidemiological monitoring of yellow fever virus transmission potential

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Genomic and epidemiological monitoring of yellow fever virus transmission potential

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