疫学ゲノム疫学研究から明らかになったジカウイルスの米国への複数の侵入経路

Journal name:
Nature
Volume:
546,
Pages:
401–405
Date published:
DOI:
doi:10.1038/nature22400
Received
Accepted
Published online

ジカウイルス(ZIKV)は、重篤な先天異常につながる前例のない流行を引き起こしている。2016年7月、蚊が媒介するZIKV伝播が米国本土で報告され、それ以降、フロリダ州では地元での感染例が数百件報告されてきた。ZIKVの侵入時期、感染源および可能性の高い感染経路を解明するために、我々は感染した患者とネッタイシマカ( Aedes aegypti )から採取したZIKVのゲノム塩基配列を解読することで、フロリダで最初に検出された時点からこのウイルスを追跡した。その結果、少なくとも4回、可能性としては最大で40回の侵入がフロリダでの大発生につながったこと、そして地元での伝播は、最初のZIKV検出よりも数か月前の2016年の春に始まった可能性が高いことが明らかになった。サーベイランスと遺伝学的データの解析から、ZIKVがマイアミの伝播区域の間を移動したことが分かった。我々の解析は、侵入の多くはカリブ海諸島と関連していることを示しており、この知見はこの地域での発生率の高さや、この地域からマイアミ地域への交通量の多さにより裏付けられる。我々の研究は、ZIKVが新たな地域で伝播を開始する仕組みを理解する手掛かりとなる。

At a glance

Figures

  1. Zika virus outbreak in Florida.
    Figure 1: Zika virus outbreak in Florida.

    a, Weekly counts of confirmed travel-associated and locally acquired ZIKV cases in 2016. b, Four counties reported locally acquired ZIKV cases in 2016: Miami-Dade (241), Broward (5), Palm Beach (8), and Pinellas (1). There was also one case of unknown origin. c, The locations of mosquito traps and collected A. aegypti mosquitoes found to contain ZIKV RNA (ZIKV+) in relation to the transmission zones within Miami. d, Temporal distribution of weekly ZIKV cases (left y-axis), sequenced cases (bottom), and A. aegypti abundance per trap night (right y-axis) associated with the three described transmission zones. ZIKV cases and sequences are plotted in relation to symptom onset dates (n窶?窶?8). Sequenced cases without onset dates or that occurred outside the transmission zones are not shown (n窶?窶?0). Human cases and A. aegypti abundance per week were positively correlated (Spearman r窶?窶?.61, Extended Data Fig. 1b). The maps were generated using open source basemaps (http://www.esri.com/data/basemaps).

  2. Multiple introductions of Zika virus into Florida.
    Figure 2: Multiple introductions of Zika virus into Florida.

    a, Maximum clade credibility (MCC) tree of ZIKV genomes sequenced from outbreaks in the Pacific islands and the epidemic in the Americas. Tips are coloured according to collection location. The five tips outlined in blue but filled with a different colour indicate ZIKV cases in the US associated with travel (fill colour indicates the probable location of infection). Clade posterior probabilities are indicated by white circles filled with black relative to the level of support. The grey violin plot indicates the 95% HPD interval for the tMRCA for the epidemic in the Americas (AM). Lineage F4 contains two identical ZIKV genomes from the same patient. b, A zoomed-in version of the whole MCC tree showing the collection locations of Miami-Dade sequences and whether they were sequenced from mosquitoes (numbers correspond to trap locations in Fig. 1c). 95% HPD intervals are shown for the tMRCAs. c, The probability of ZIKV persistence after introduction for different R0 values. Persistence is measured as the number of days from initial introduction of viral lineages until their extinction. Vertical dashed lines show the inferred mean persistence time for lineages F1, F2 and B based on their tMRCAs. d, Total number of introductions (mean with 95% CI) that contributed to the outbreak of 241 local cases in Miami-Dade County for different R0 values.

  3. Frequent opportunities for Zika virus introductions into Miami from the Caribbean.
    Figure 3: Frequent opportunities for Zika virus introductions into Miami from the Caribbean.

    a, Reported ZIKV cases per country or territory from January to June 2016, normalized by total population. b, The number of estimated travellers entering Miami during January窶笛une 2016, by method of travel. c, The number of travellers and reported ZIKV incidence rate for the country or territory of origin were used to estimate the proportion of infected travellers coming from each region with ZIKV in the Americas. d, The observed number of weekly travel-associated ZIKV cases in Florida (black line), plotted with the expected number of ZIKV-infected travellers (as estimated in c) coming from all of the Americas (grey line) and the regional contributions (coloured areas). e, The countries visited by the 1,016 patients with travel-associated ZIKV diagnosed in Florida.

  4. Southern Florida has a high potential for A. aegypti-borne virus outbreaks.
    Figure 4: Southern Florida has a high potential for A. aegypti-borne virus outbreaks.

    The estimated number of travellers per month (circles) entering Florida cities via flights and cruise ships, plotted with estimated relative A. aegypti abundance. Only cities receiving more than 10,000 passengers per month are shown. Relative A. aegypti abundance for every month is shown in Extended Data Fig. 1d.

  5. Miami-Dade mosquito surveillance and relative A. aegypti abundance.
    Extended Data Fig. 1: Miami-Dade mosquito surveillance and relative A. aegypti abundance.

    a, Mosquito surveillance data reported from 21 June to 28 November 2016 was used to evaluate the risk of ZIKV infection from mosquito-borne transmission in Miami. A total of 24,306 A. aegypti and 45 A. albopictus were collected. Trap nights are the total number of times each trap site was used and the trap locations are shown in Fig. 1d (some 窶楼ther Miami窶?trap sites are located outside the mapped region). Up to 50 mosquitoes of the same species and trap night were pooled together for ZIKV RNA testing. The infection rates were calculated using an MLE. None of the A. albopictus pools contained ZIKV RNA. b, The number of weekly ZIKV cases (based on symptom onset) was correlated with mean A. aegypti abundance per trap night determined from the same week and zone (Spearman r窶?窶?.61). This suggests that when the virus is present, mosquito abundance numbers alone could be used to target control efforts. c, Insecticide usage, including truck and aerial adulticides and larvacides, by Miami-Dade Mosquito Control in Wynwood (left) and Miami Beach (right) was overlaid with A. aegypti abundance per trap night to demonstrate that intense usage of insecticides may have helped to reduce local mosquito populations. d, Relative A. aegypti abundance for each Florida county and month was estimated using a multivariate regression model, demonstrating spatial and temporal heterogeneity for the risk of ZIKV infection.

  6. Maximum likelihood tree and root-to-tip regression of Zika virus genomes from Pacific islands and the epidemic in the Americas.
    Extended Data Fig. 2: Maximum likelihood tree and root-to-tip regression of Zika virus genomes from Pacific islands and the epidemic in the Americas.

    a, Maximum likelihood tree of publicly available ZIKV sequences and sequences generated in this study (n窶?窶?04). Tips are coloured by location and labels in bold indicate sequences generated in this study. Florida clusters F1窶擢4 are indicated by vertical lines to the right of the tree. Bootstrap support values are shown at key nodes. All other support values can be found in Supplementary File 1. b, Linear regression of sample tip dates against divergence from root based on sequences with known collection dates estimates an evolutionary rate for the ZIKV phylogeny of 1.10窶嘉冷?10竏? nucleotide substitutions per site per year (subs/site/yr). This is consistent with BEAST analyses using a relaxed molecular clock and a Bayesian Skyline tree prior, the best performing combination of clock and demographic model according to MLEs (Extended Data Table 1c), which estimated an evolutionary rate of 1.21窶嘉冷?10竏? (95% highest posterior density: 1.01窶?.43窶嘉冷?10竏?) substitutions per site per year (Extended Data Table 1a). These values are in agreement with previous estimates based on ZIKV genomes from Brazil6.

  7. Molecular clock dating of Zika virus clades.
    Extended Data Fig. 3: Molecular clock dating of Zika virus clades.

    Maximum clade credibility (MCC) tree of ZIKV genomes collected from Pacific islands and the epidemic in Americas (n窶?窶?04). Circles at the tips are coloured according to origin location. Clade posterior probabilities are indicated by white circles filled with black relative to the support. A posterior probability of 1 fills the entire circle black. The grey violin plot indicates the 95% HPD interval for the tMRCA of the American epidemic. We estimated that the tMRCA for the ongoing epidemic in the Americas occurred during October 2013 (node AM, Extended Data Table 1, 95% HPD: August 2013窶笛anuary 2014), which is consistent with previous analysis based on ZIKV genomes from Brazil6.

  8. Estimation of basic reproductive number and number of introductions in Miami-Dade County.
    Extended Data Fig. 4: Estimation of basic reproductive number and number of introductions in Miami-Dade County.

    a, Probability distribution of estimated total number of cases caused by a single introduction (excluding the index case) for different values of R0. b, Mean and 95% CI for total number of local cases caused by 320 introduction events (that is, travel-associated cases diagnosed in Miami-Dade County) for different values of R0 and for different assumptions of proportion of infectious travellers. c, log likelihood of observing 241 local cases in Miami-Dade County with 320 introduction events for different values of R0 along with 95% MLE bounds on R0. d, Mean and 95% uncertainty interval for total number of distinct phylogenetic clusters observed in 27 sequenced ZIKV genomes from human cases diagnosed in Miami-Dade County for different values of R0 and for different assumptions of sampling bias, from ホア窶?窶? (no sampling bias) to ホア窶?窶? (skewed towards preferentially sampling larger clusters). e, log likelihood of observing three clusters (that is, ZIKV lineages F1, F2, and F4, Fig. 2a) in 27 sequenced cases for different values of R0 along with 95% MLE bounds on R0. f, Mean and 95% CI for total number of local cases caused by 320 observed travel-associated cases with travel-associated versus local reporting rates of 50%/25% and 10%/5%. This assumes that 50% of travellers are infectious. g, log likelihood of observing 241 local cases with 320 introduction events for different values of R0 along with 95% MLE bounds on R0 with travel-associated versus local reporting rates of 50%/25% and 10%/5%. h, Mean and 95% uncertainty interval for total number of distinct phylogenetic clusters observed in 27 sequenced ZIKV genomes for different values of R0 and for assumptions of local reporting rate of 5% and 25%. This assumes preferential sampling (ホア窶?窶?). i, log likelihood of observing three clusters in 27 sequenced cases for different values of R0 along with 95% MLE bounds on R0 with local reporting rates of 5% and 25%. At 5% local reporting rate, none of the 100,000 replicates for all R0 values showed three clusters.

  9. Weekly reported Zika virus case numbers and incidence rates in the Americas.
    Extended Data Fig. 5: Weekly reported Zika virus case numbers and incidence rates in the Americas.

    a, Most ZIKV case numbers reported by PAHO30 were available only as bar graphs (raw data were not made available to us at the time of request). Therefore we used WebPlotDigitizer to estimate the weekly case numbers from the PAHO bar graphs. ZIKV cases reported from Ecuador was the only dataset to include a link to the actual case numbers that also had more than 10 cases per week73. To validate the WebPlotDigitizer-derived values, we compared the weekly reported case numbers from Ecuador to our estimates. b, The reported and estimated case numbers were strongly correlated (Spearman r窶?窶?.9981). WebPlotDigitizer was used to estimate the ZIKV case numbers for all subsequent analysis. c, d, ZIKV cases (suspected and confirmed; c) and incidence rates (normalized per 100,000 population; d) are shown for each country or territory with available data per epidemiological week from 1 January to 18 September 2016. e, Each country or territory with available data is coloured by its reported ZIKV incidence rate from January to June 2016 (the time frame for analysis of ZIKV introductions into Florida).

  10. Cruise and flight traffic entering Miami from regions with Zika virus transmission.
    Extended Data Fig. 6: Cruise and flight traffic entering Miami from regions with Zika virus transmission.

    a, b, The estimated number of passengers entering Miami, by cruises (a) or flights (b), from each country or territory in the Americas with ZIKV transmission per month (left). The centre map and inset show the cumulative numbers of travellers entering Miami during January to June 2016 (the time frame for analysis of ZIKV introductions into Florida) from each country or territory per method of travel. c, The total traffic (that is, cruises and flights) entering Miami per month.

  11. Expected number of ZIKV-infected travellers from the Caribbean correlated with the total observed number of travel-associated infections.
    Extended Data Fig. 7: Expected number of ZIKV-infected travellers from the Caribbean correlated with the total observed number of travel-associated infections.

    a, To account for potential biases in ZIKV reporting accuracy, we also estimated the proportion of infected travellers using projected ZIKV attack rates78(that is, predicted proportion of population infected before epidemic burnout). About 60% of the infected travellers are expected to have arrived from the Caribbean, similar to our results using incidence rates (Fig. 3c). b, The expected number of travel-associated ZIKV cases was estimated by the number of travellers coming into Miami from each country or territory (travel capacity) and the in-country or in-territory infection likelihood (incidence rate per person) per week. The expected travel cases were summed from all of the Americas (left), Caribbean (left centre), South America (right centre), and Central America (right) and plotted with the observed travel-associated ZIKV cases. Numbers in each plot indicate Spearman correlation coefficients. Negative Spearman r coefficients indicate a negative correlation between the number of expected and observed travel cases.

  12. Greater early season potential for Zika virus introductions into Miami.
    Extended Data Fig. 8: Greater early season potential for Zika virus introductions into Miami.

    The monthly cruise ship and airline28 capacity from countries or territories with ZIKV transmission for the major US travel hubs (shown as circle diameter) with monthly potential A. aegypti abundance (circle colour), as previously estimated22. The abundance ranges were chosen with respect to the May窶徹ctober Miami mean: None to low (<2%); Low to moderate (2窶?5%); Moderate to high (25窶?5%); and High (>75%). Mosquito-borne transmission is unlikely in the 窶朗one to low窶?range. Cruise capacities from Houston and Galveston, Texas were combined.

Tables

  1. Evolutionary analyses and model selection
    Extended Data Table 1: Evolutionary analyses and model selection
  2. Validation of sequencing results
    Extended Data Table 2: Validation of sequencing results

Accession codes

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Author information

  1. These authors contributed equally to this work.

    • Nathan D. Grubaugh,
    • Jason T. Ladner,
    • Moritz U. G. Kraemer,
    • Gytis Dudas,
    • Amanda L. Tan,
    • Karthik Gangavarapu,
    • Michael R. Wiley,
    • Stephen White &
    • Julien Thテゥzテゥ
  2. These authors jointly supervised this work.

    • Pardis C. Sabeti,
    • Leah D. Gillis,
    • Scott F. Michael,
    • Trevor Bedford,
    • Oliver G. Pybus,
    • Sharon Isern,
    • Gustavo Palacios &
    • Kristian G. Andersen

Affiliations

  1. Department of Immunology and Microbial Science, The Scripps Research Institute, La Jolla, California 92037, USA

    • Nathan D. Grubaugh,
    • Karthik Gangavarapu,
    • Refugio Robles-Sikisaka &
    • Kristian G. Andersen
  2. Center for Genome Sciences, US Army Medical Research Institute of Infectious Diseases, Fort Detrick, Maryland 21702, USA

    • Jason T. Ladner,
    • Michael R. Wiley,
    • Karla Prieto,
    • Daniel Reyes,
    • Elyse R. Nagle,
    • Mariano Sanchez-Lockhart &
    • Gustavo Palacios
  3. Department of Zoology, University of Oxford, Oxford OX1 3PS, UK

    • Moritz U. G. Kraemer,
    • Julien Thテゥzテゥ,
    • Nuno R. Faria &
    • Oliver G. Pybus
  4. Boston Children窶冱 Hospital, Boston, Massachusetts 02115, USA

    • Moritz U. G. Kraemer
  5. Harvard Medical School, Boston, Massachusetts 02115, USA

    • Moritz U. G. Kraemer
  6. Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA

    • Gytis Dudas &
    • Trevor Bedford
  7. Department of Biological Sciences, College of Arts and Sciences, Florida Gulf Coast University, Fort Myers, Florida 33965, USA

    • Amanda L. Tan,
    • Lauren M. Paul,
    • Carolyn M. Barcellona,
    • Scott F. Michael &
    • Sharon Isern
  8. College of Public Health, University of Nebraska Medical Center, Omaha, Nebraska 68198, USA

    • Michael R. Wiley &
    • Karla Prieto
  9. Bureau of Public Health Laboratories, Division of Disease Control and Health Protection, Florida Department of Health, Miami, Florida 33125, USA

    • Stephen White,
    • Darryl Pronty &
    • Leah D. Gillis
  10. Department of Pathology, University of Miami Miller School of Medicine, Miami, Florida 33136, USA

    • Diogo M. Magnani,
    • Michael J. Ricciardi,
    • Varian K. Bailey &
    • David I. Watkins
  11. Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, Nebraska 68198, USA

    • Daniel Reyes,
    • Elyse R. Nagle &
    • Mariano Sanchez-Lockhart
  12. Bureau of Epidemiology, Division of Disease Control and Health Protection, Florida Department of Health, Tallahassee, Florida 32399, USA

    • Andrea M. Bingham &
    • Danielle Stanek
  13. Scripps Translational Science Institute, La Jolla, California 92037, USA

    • Glenn Oliveira &
    • Kristian G. Andersen
  14. The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA

    • Hayden C. Metsky,
    • Mary Lynn Baniecki,
    • Kayla G. Barnes,
    • Bridget Chak,
    • Catherine A. Freije,
    • Adrianne Gladden-Young,
    • Andreas Gnirke,
    • Cynthia Luo,
    • Bronwyn MacInnis,
    • Christian B. Matranga,
    • Daniel J. Park,
    • James Qu,
    • Stephen F. Schaffner,
    • Christopher Tomkins-Tinch,
    • Kendra L. West,
    • Sarah M. Winnicki,
    • Shirlee Wohl,
    • Nathan L. Yozwiak &
    • Pardis C. Sabeti
  15. Institute of Microbiology and Infection, University of Birmingham, Birmingham B15 2TT, UK

    • Joshua Quick &
    • Nicholas J. Loman
  16. Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, Colorado 80523, USA

    • Joseph R. Fauver
  17. Li Ka Shing Knowledge Institute, St Michael窶冱 Hospital, Toronto, Ontario M5B 1T8, Canada

    • Kamran Khan &
    • Shannon E. Brent
  18. Division of Infectious Diseases, Department of Medicine, University of Toronto, Toronto, Ontario M5S 1A8, Canada

    • Kamran Khan
  19. Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA

    • Robert C. Reiner Jr
  20. Division of Infectious Diseases, University of Miami Miller School of Medicine, Miami, Florida 33136, USA

    • Paola N. Lichtenberger
  21. Bureau of Public Health Laboratories, Division of Disease Control and Health Protection, Florida Department of Health, Tampa, Florida 33612, USA

    • Marshall R. Cone,
    • Edgar W. Kopp IV,
    • Kelly N. Hogan &
    • Andrew C. Cannons
  22. Florida Department of Health in Miami-Dade County, Miami, Florida 33125, USA

    • Reynald Jean
  23. National Center for Atmospheric Research, Boulder, Colorado 80307, USA

    • Andrew J. Monaghan
  24. Department of Microbiology and Immunology, Tulane University School of Medicine, New Orleans, Louisiana 70112, USA

    • Robert F. Garry
  25. Miami-Dade County Mosquito Control, Miami, Florida 33178, USA

    • Mario C. Porcelli &
    • Chalmers Vasquez
  26. Department of Biology and Emerging Pathogens Institute, University of Florida, Gainesville, Florida 32610, USA

    • Derek A. T. Cummings
  27. Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, UK

    • Andrew Rambaut
  28. Fogarty International Center, National Institutes of Health, Bethesda, Maryland 20892, USA

    • Andrew Rambaut
  29. Center for Systems Biology, Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts 02138, USA

    • Pardis C. Sabeti
  30. Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts 02115, USA

    • Pardis C. Sabeti
  31. Howard Hughes Medical Institute, Chevy Chase, Maryland 20815, USA

    • Pardis C. Sabeti
  32. Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, USA

    • Kristian G. Andersen

Contributions

All contributions are listed in order of authorship. Designed the experiments: N.D.G., J.T.L., G.D., M.U.G.K., D.A.T.C., P.C.S., L.D.G., S.F.M., T.B., O.G.P., S.I., G.P., and K.G.A. Collected samples: A.L.T., S.W., D.M.M., A.M.B., L.M.P., D.P., C.M.B., P.N.L., M.J.R., V.K.B., D.I.W., M.R.C., E.W.K., K.N.H., A.C.C., R.J., M.C.P., C.V., D.S., L.D.G., S.F.M., and S.I. Performed the sequencing: N.D.G., M.R.W., K.P., D.R., R.R.-S., G.O., and E.R.N. Provided data, reagents, or protocols: N.D.G., J.T.L., G.D., M.U.G.K., K.G., M.R.W., R.R.-S., G.O., H.C.M., M.L.B., K.G.B., B.C., C.A.F., A.G.-Y., A.G., C.L., B.M., C.B.M., D.J.P., J. Q.U, S.F.S., C.T.-T., K.L.W., S.M.W., S.W., N.L.Y., J.Qui., J.R.F., K.K., S.E.B., A.J.M., R.F.G., N.J.L., M.C.P., C.V., P.C.S., S.F.M., and S.I. Analysed the data: N.D.G., J.T.L., G.D., M.U.G.K., K.G., J.T., J.R.F., R.C.R., N.R.F., D.A.T.C., A.R., M.S.-L., T.B., S.F.M, O.G.P., S.I., and K.G.A. Edited manuscript: G.D., M.U.G.K., J.T., S.F.S., A.R., T.B., O.G.P., S.I., and G.P. Wrote manuscript: N.D.G., J.T.L., and K.G.A. All authors read and approved the manuscript.

Competing financial interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to:

Reviewer Information Nature thanks K. St George, A. Wilder-Smith, M. Worobey and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Author details

Extended data figures and tables

Extended Data Figures

  1. Extended Data Figure 1: Miami-Dade mosquito surveillance and relative A. aegypti abundance. (498 KB)

    a, Mosquito surveillance data reported from 21 June to 28 November 2016 was used to evaluate the risk of ZIKV infection from mosquito-borne transmission in Miami. A total of 24,306 A. aegypti and 45 A. albopictus were collected. Trap nights are the total number of times each trap site was used and the trap locations are shown in Fig. 1d (some 窶楼ther Miami窶?trap sites are located outside the mapped region). Up to 50 mosquitoes of the same species and trap night were pooled together for ZIKV RNA testing. The infection rates were calculated using an MLE. None of the A. albopictus pools contained ZIKV RNA. b, The number of weekly ZIKV cases (based on symptom onset) was correlated with mean A. aegypti abundance per trap night determined from the same week and zone (Spearman r窶?窶?.61). This suggests that when the virus is present, mosquito abundance numbers alone could be used to target control efforts. c, Insecticide usage, including truck and aerial adulticides and larvacides, by Miami-Dade Mosquito Control in Wynwood (left) and Miami Beach (right) was overlaid with A. aegypti abundance per trap night to demonstrate that intense usage of insecticides may have helped to reduce local mosquito populations. d, Relative A. aegypti abundance for each Florida county and month was estimated using a multivariate regression model, demonstrating spatial and temporal heterogeneity for the risk of ZIKV infection.

  2. Extended Data Figure 2: Maximum likelihood tree and root-to-tip regression of Zika virus genomes from Pacific islands and the epidemic in the Americas. (429 KB)

    a, Maximum likelihood tree of publicly available ZIKV sequences and sequences generated in this study (n窶?窶?04). Tips are coloured by location and labels in bold indicate sequences generated in this study. Florida clusters F1窶擢4 are indicated by vertical lines to the right of the tree. Bootstrap support values are shown at key nodes. All other support values can be found in Supplementary File 1. b, Linear regression of sample tip dates against divergence from root based on sequences with known collection dates estimates an evolutionary rate for the ZIKV phylogeny of 1.10窶嘉冷?10竏? nucleotide substitutions per site per year (subs/site/yr). This is consistent with BEAST analyses using a relaxed molecular clock and a Bayesian Skyline tree prior, the best performing combination of clock and demographic model according to MLEs (Extended Data Table 1c), which estimated an evolutionary rate of 1.21窶嘉冷?10竏? (95% highest posterior density: 1.01窶?.43窶嘉冷?10竏?) substitutions per site per year (Extended Data Table 1a). These values are in agreement with previous estimates based on ZIKV genomes from Brazil6.

  3. Extended Data Figure 3: Molecular clock dating of Zika virus clades. (668 KB)

    Maximum clade credibility (MCC) tree of ZIKV genomes collected from Pacific islands and the epidemic in Americas (n窶?窶?04). Circles at the tips are coloured according to origin location. Clade posterior probabilities are indicated by white circles filled with black relative to the support. A posterior probability of 1 fills the entire circle black. The grey violin plot indicates the 95% HPD interval for the tMRCA of the American epidemic. We estimated that the tMRCA for the ongoing epidemic in the Americas occurred during October 2013 (node AM, Extended Data Table 1, 95% HPD: August 2013窶笛anuary 2014), which is consistent with previous analysis based on ZIKV genomes from Brazil6.

  4. Extended Data Figure 4: Estimation of basic reproductive number and number of introductions in Miami-Dade County. (426 KB)

    a, Probability distribution of estimated total number of cases caused by a single introduction (excluding the index case) for different values of R0. b, Mean and 95% CI for total number of local cases caused by 320 introduction events (that is, travel-associated cases diagnosed in Miami-Dade County) for different values of R0 and for different assumptions of proportion of infectious travellers. c, log likelihood of observing 241 local cases in Miami-Dade County with 320 introduction events for different values of R0 along with 95% MLE bounds on R0. d, Mean and 95% uncertainty interval for total number of distinct phylogenetic clusters observed in 27 sequenced ZIKV genomes from human cases diagnosed in Miami-Dade County for different values of R0 and for different assumptions of sampling bias, from ホア窶?窶? (no sampling bias) to ホア窶?窶? (skewed towards preferentially sampling larger clusters). e, log likelihood of observing three clusters (that is, ZIKV lineages F1, F2, and F4, Fig. 2a) in 27 sequenced cases for different values of R0 along with 95% MLE bounds on R0. f, Mean and 95% CI for total number of local cases caused by 320 observed travel-associated cases with travel-associated versus local reporting rates of 50%/25% and 10%/5%. This assumes that 50% of travellers are infectious. g, log likelihood of observing 241 local cases with 320 introduction events for different values of R0 along with 95% MLE bounds on R0 with travel-associated versus local reporting rates of 50%/25% and 10%/5%. h, Mean and 95% uncertainty interval for total number of distinct phylogenetic clusters observed in 27 sequenced ZIKV genomes for different values of R0 and for assumptions of local reporting rate of 5% and 25%. This assumes preferential sampling (ホア窶?窶?). i, log likelihood of observing three clusters in 27 sequenced cases for different values of R0 along with 95% MLE bounds on R0 with local reporting rates of 5% and 25%. At 5% local reporting rate, none of the 100,000 replicates for all R0 values showed three clusters.

  5. Extended Data Figure 5: Weekly reported Zika virus case numbers and incidence rates in the Americas. (431 KB)

    a, Most ZIKV case numbers reported by PAHO30 were available only as bar graphs (raw data were not made available to us at the time of request). Therefore we used WebPlotDigitizer to estimate the weekly case numbers from the PAHO bar graphs. ZIKV cases reported from Ecuador was the only dataset to include a link to the actual case numbers that also had more than 10 cases per week73. To validate the WebPlotDigitizer-derived values, we compared the weekly reported case numbers from Ecuador to our estimates. b, The reported and estimated case numbers were strongly correlated (Spearman r窶?窶?.9981). WebPlotDigitizer was used to estimate the ZIKV case numbers for all subsequent analysis. c, d, ZIKV cases (suspected and confirmed; c) and incidence rates (normalized per 100,000 population; d) are shown for each country or territory with available data per epidemiological week from 1 January to 18 September 2016. e, Each country or territory with available data is coloured by its reported ZIKV incidence rate from January to June 2016 (the time frame for analysis of ZIKV introductions into Florida).

  6. Extended Data Figure 6: Cruise and flight traffic entering Miami from regions with Zika virus transmission. (492 KB)

    a, b, The estimated number of passengers entering Miami, by cruises (a) or flights (b), from each country or territory in the Americas with ZIKV transmission per month (left). The centre map and inset show the cumulative numbers of travellers entering Miami during January to June 2016 (the time frame for analysis of ZIKV introductions into Florida) from each country or territory per method of travel. c, The total traffic (that is, cruises and flights) entering Miami per month.

  7. Extended Data Figure 7: Expected number of ZIKV-infected travellers from the Caribbean correlated with the total observed number of travel-associated infections. (188 KB)

    a, To account for potential biases in ZIKV reporting accuracy, we also estimated the proportion of infected travellers using projected ZIKV attack rates78(that is, predicted proportion of population infected before epidemic burnout). About 60% of the infected travellers are expected to have arrived from the Caribbean, similar to our results using incidence rates (Fig. 3c). b, The expected number of travel-associated ZIKV cases was estimated by the number of travellers coming into Miami from each country or territory (travel capacity) and the in-country or in-territory infection likelihood (incidence rate per person) per week. The expected travel cases were summed from all of the Americas (left), Caribbean (left centre), South America (right centre), and Central America (right) and plotted with the observed travel-associated ZIKV cases. Numbers in each plot indicate Spearman correlation coefficients. Negative Spearman r coefficients indicate a negative correlation between the number of expected and observed travel cases.

  8. Extended Data Figure 8: Greater early season potential for Zika virus introductions into Miami. (397 KB)

    The monthly cruise ship and airline28 capacity from countries or territories with ZIKV transmission for the major US travel hubs (shown as circle diameter) with monthly potential A. aegypti abundance (circle colour), as previously estimated22. The abundance ranges were chosen with respect to the May窶徹ctober Miami mean: None to low (<2%); Low to moderate (2窶?5%); Moderate to high (25窶?5%); and High (>75%). Mosquito-borne transmission is unlikely in the 窶朗one to low窶?range. Cruise capacities from Houston and Galveston, Texas were combined.

Extended Data Tables

  1. Extended Data Table 1: Evolutionary analyses and model selection (281 KB)
  2. Extended Data Table 2: Validation of sequencing results (98 KB)

Supplementary information

Excel files

  1. Supplementary Table 1 (51 KB)

    This table contains a, a Summary of the Zika virus sequencing data produced in this study and b, epidemiological data and travellers entering Miami, Florida from January to June, 2016.

  2. Supplementary Table 2 (52 KB)

    This table contains: a, probe sequences; b, reference genomes for RNA Access targeted enrichment of Zika virus; c, rimer sequences used for long-range Zika virus amplification and d, a comparison of amplicon and enrichment Zika virus sequencing approaches.

Zip files

  1. Supplementary Data (158 KB)

    This file contains raw MAFFT codon alignment data, PhyML tree, BEAST XML file, and BEAST MCC time-structured phylogeny.

Additional data