1
|
Dellicour S, Bastide P, Rocu P, Fargette D, Hardy OJ, Suchard MA, Guindon S, Lemey P. How fast are viruses spreading in the wild? BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.10.588821. [PMID: 38645268 PMCID: PMC11030353 DOI: 10.1101/2024.04.10.588821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Genomic data collected from viral outbreaks can be exploited to reconstruct the dispersal history of viral lineages in a two-dimensional space using continuous phylogeographic inference. These spatially explicit reconstructions can subsequently be used to estimate dispersal metrics allowing to unveil the dispersal dynamics and evaluate the capacity to spread among hosts. Heterogeneous sampling intensity of genomic sequences can however impact the accuracy of dispersal insights gained through phylogeographic inference. In our study, we implement a simulation framework to evaluate the robustness of three dispersal metrics - a lineage dispersal velocity, a diffusion coefficient, and an isolation-by-distance signal metric - to the sampling effort. Our results reveal that both the diffusion coefficient and isolation-by-distance signal metrics appear to be robust to the number of samples considered for the phylogeographic reconstruction. We then use these two dispersal metrics to compare the dispersal pattern and capacity of various viruses spreading in animal populations. Our comparative analysis reveals a broad range of isolation-by-distance patterns and diffusion coefficients mostly reflecting the dispersal capacity of the main infected host species but also, in some cases, the likely signature of rapid and/or long-distance dispersal events driven by human-mediated movements through animal trade. Overall, our study provides key recommendations for the lineage dispersal metrics to consider in future studies and illustrates their application to compare the spread of viruses in various settings.
Collapse
|
2
|
Roddur MS, Snir S, El-Kebir M. Enforcing Temporal Consistency in Migration History Inference. J Comput Biol 2024; 31:396-415. [PMID: 38754138 DOI: 10.1089/cmb.2023.0352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2024] Open
Abstract
In addition to undergoing evolution, members of biological populations may also migrate between locations. Examples include the spread of tumor cells from the primary tumor to distant metastases or the spread of pathogens from one host to another. One may represent migration histories by assigning a location label to each vertex of a given phylogenetic tree such that an edge connecting vertices with distinct locations represents a migration. Some biological populations undergo comigration, a phenomenon where multiple taxa from distinct lineages simultaneously comigrate from one location to another. In this work, we show that a previous problem statement for inferring migration histories that are parsimonious in terms of migrations and comigrations may lead to temporally inconsistent solutions. To remedy this deficiency, we introduce precise definitions of temporal consistency of comigrations in a phylogenetic tree, leading to three successive problems. First, we formulate the temporally consistent comigration problem to check if a set of comigrations is temporally consistent and provide a linear time algorithm for solving this problem. Second, we formulate the parsimonious consistent comigrations (PCC) problem, which aims to find comigrations given a location labeling of a phylogenetic tree. We show that PCC is NP-hard. Third, we formulate the parsimonious consistent comigration history (PCCH) problem, which infers the migration history given a phylogenetic tree and locations of its extant vertices only. We show that PCCH is NP-hard as well. On the positive side, we propose integer linear programming models to solve the PCC and PCCH problems. We demonstrate our algorithms on simulated and real data.
Collapse
Affiliation(s)
- Mrinmoy Saha Roddur
- Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
| | - Sagi Snir
- Department of Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Mohammed El-Kebir
- Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Cancer Center at Illinois, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
| |
Collapse
|
3
|
Schar D, Zhang Z, Pires J, Vrancken B, Suchard MA, Lemey P, Ip M, Gilbert M, Van Boeckel T, Dellicour S. Dispersal history and bidirectional human-fish host switching of invasive, hypervirulent Streptococcus agalactiae sequence type 283. PLOS GLOBAL PUBLIC HEALTH 2023; 3:e0002454. [PMID: 37856430 PMCID: PMC10586614 DOI: 10.1371/journal.pgph.0002454] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Accepted: 09/25/2023] [Indexed: 10/21/2023]
Abstract
Human group B Streptococcus (GBS) infections attributable to an invasive, hypervirulent sequence type (ST) 283 have been associated with freshwater fish consumption in Asia. The origin, geographic dispersion pathways and host transitions of GBS ST283 remain unresolved. We gather 328 ST283 isolate whole-genome sequences collected from humans and fish between 1998 and 2021, representing eleven countries across four continents. We apply Bayesian phylogeographic analyses to reconstruct the dispersal history of ST283 and combine ST283 phylogenies with genetic markers and host association to investigate host switching and the gain and loss of antimicrobial resistance and virulence factor genes. Initial dispersal within Asia followed ST283 emergence in the early 1980s, with Singapore, Thailand and Hong Kong observed as early transmission hubs. Subsequent intercontinental dispersal originating from Vietnam began in the decade commencing 2001, demonstrating ST283 holds potential to expand geographically. Furthermore, we observe bidirectional host switching, with the detection of more frequent human-to-fish than fish-to-human transitions, suggesting that sound wastewater management, hygiene and sanitation may help to interrupt chains of transmission between hosts. We also show that antimicrobial resistance and virulence factor genes were lost more frequently than gained across the evolutionary history of ST283. Our findings highlight the need for enhanced surveillance, clinical awareness, and targeted risk mitigation to limit transmission and reduce the impact of an emerging pathogen associated with a high-growth aquaculture industry.
Collapse
Affiliation(s)
- Daniel Schar
- Spatial Epidemiology Laboratory, Université Libre de Bruxelles, Brussels, Belgium
| | - Zhenyu Zhang
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, United States of America
| | - Joao Pires
- Institute for Environmental Decisions, ETH Zurich, Zurich, Switzerland
| | - Bram Vrancken
- Spatial Epidemiology Laboratory, Université Libre de Bruxelles, Brussels, Belgium
- Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, KU Leuven, Leuven, Belgium
| | - Marc A. Suchard
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, United States of America
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, CA, United States of America
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, KU Leuven, Leuven, Belgium
| | - Margaret Ip
- Department of Microbiology, Faculty of Medicine, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Marius Gilbert
- Spatial Epidemiology Laboratory, Université Libre de Bruxelles, Brussels, Belgium
- Fonds National de la Recherche Scientifique, Brussels, Belgium
| | - Thomas Van Boeckel
- Institute for Environmental Decisions, ETH Zurich, Zurich, Switzerland
- Center for Diseases Dynamics, Economics, and Policy, New Delhi, India
| | - Simon Dellicour
- Spatial Epidemiology Laboratory, Université Libre de Bruxelles, Brussels, Belgium
- Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, KU Leuven, Leuven, Belgium
| |
Collapse
|
4
|
Hufsky F, Abecasis AB, Babaian A, Beck S, Brierley L, Dellicour S, Eggeling C, Elena SF, Gieraths U, Ha AD, Harvey W, Jones TC, Lamkiewicz K, Lovate GL, Lücking D, Machyna M, Nishimura L, Nocke MK, Renard BY, Sakaguchi S, Sakellaridi L, Spangenberg J, Tarradas-Alemany M, Triebel S, Vakulenko Y, Wijesekara RY, González-Candelas F, Krautwurst S, Pérez-Cataluña A, Randazzo W, Sánchez G, Marz M. The International Virus Bioinformatics Meeting 2023. Viruses 2023; 15:2031. [PMID: 37896809 PMCID: PMC10612056 DOI: 10.3390/v15102031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 09/08/2023] [Accepted: 09/14/2023] [Indexed: 10/29/2023] Open
Abstract
The 2023 International Virus Bioinformatics Meeting was held in Valencia, Spain, from 24-26 May 2023, attracting approximately 180 participants worldwide. The primary objective of the conference was to establish a dynamic scientific environment conducive to discussion, collaboration, and the generation of novel research ideas. As the first in-person event following the SARS-CoV-2 pandemic, the meeting facilitated highly interactive exchanges among attendees. It served as a pivotal gathering for gaining insights into the current status of virus bioinformatics research and engaging with leading researchers and emerging scientists. The event comprised eight invited talks, 19 contributed talks, and 74 poster presentations across eleven sessions spanning three days. Topics covered included machine learning, bacteriophages, virus discovery, virus classification, virus visualization, viral infection, viromics, molecular epidemiology, phylodynamic analysis, RNA viruses, viral sequence analysis, viral surveillance, and metagenomics. This report provides rewritten abstracts of the presentations, a summary of the key research findings, and highlights shared during the meeting.
Collapse
Affiliation(s)
- Franziska Hufsky
- European Virus Bioinformatics Center, 07743 Jena, Germany (A.B.A.); (L.B.); (S.D.); (C.E.); (S.F.E.); (T.C.J.); (K.L.); (G.L.L.); (M.K.N.); (B.Y.R.); (F.G.-C.); (A.P.-C.); (W.R.); (G.S.)
- RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, 07743 Jena, Germany;
| | - Ana B. Abecasis
- European Virus Bioinformatics Center, 07743 Jena, Germany (A.B.A.); (L.B.); (S.D.); (C.E.); (S.F.E.); (T.C.J.); (K.L.); (G.L.L.); (M.K.N.); (B.Y.R.); (F.G.-C.); (A.P.-C.); (W.R.); (G.S.)
- Global Health and Tropical Medicine, GHTM, Associate Laboratory in Translation and Innovation towards Global Health, LA-REAL, Instituto de Higiene e Medicina Tropical, IHMT, Universidade NOVA de Lisboa, UNL, Rua da Junqueira 100, 1349-008 Lisboa, Portugal
| | - Artem Babaian
- European Virus Bioinformatics Center, 07743 Jena, Germany (A.B.A.); (L.B.); (S.D.); (C.E.); (S.F.E.); (T.C.J.); (K.L.); (G.L.L.); (M.K.N.); (B.Y.R.); (F.G.-C.); (A.P.-C.); (W.R.); (G.S.)
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
- Donnelly Centre, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - Sebastian Beck
- Leibniz Institute of Virology, Department Viral Zoonoses—One Health, 20251 Hamburg, Germany;
| | - Liam Brierley
- European Virus Bioinformatics Center, 07743 Jena, Germany (A.B.A.); (L.B.); (S.D.); (C.E.); (S.F.E.); (T.C.J.); (K.L.); (G.L.L.); (M.K.N.); (B.Y.R.); (F.G.-C.); (A.P.-C.); (W.R.); (G.S.)
- Department of Health Data Science, University of Liverpool, Liverpool L69 3GF, UK
| | - Simon Dellicour
- European Virus Bioinformatics Center, 07743 Jena, Germany (A.B.A.); (L.B.); (S.D.); (C.E.); (S.F.E.); (T.C.J.); (K.L.); (G.L.L.); (M.K.N.); (B.Y.R.); (F.G.-C.); (A.P.-C.); (W.R.); (G.S.)
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, CP160/12, 50 av. FD Roosevelt, 1050 Bruxelles, Belgium
- Laboratory for Clinical and Epidemiological Virology, Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, University of Leuven, 3000 Leuven, Belgium
| | - Christian Eggeling
- European Virus Bioinformatics Center, 07743 Jena, Germany (A.B.A.); (L.B.); (S.D.); (C.E.); (S.F.E.); (T.C.J.); (K.L.); (G.L.L.); (M.K.N.); (B.Y.R.); (F.G.-C.); (A.P.-C.); (W.R.); (G.S.)
- Institute of Applied Optics and Biophysics, Friedrich Schiller University Jena, Max-Wien-Platz 1, 07743 Jena, Germany
| | - Santiago F. Elena
- European Virus Bioinformatics Center, 07743 Jena, Germany (A.B.A.); (L.B.); (S.D.); (C.E.); (S.F.E.); (T.C.J.); (K.L.); (G.L.L.); (M.K.N.); (B.Y.R.); (F.G.-C.); (A.P.-C.); (W.R.); (G.S.)
- Institute for Integrative Systems Biology (I2SysBio), CSIC-Universitat de Valencia, Catedratico Agustin Escardino 9, 46980 Valencia, Spain
| | - Udo Gieraths
- Institute of Virology, Charité, Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Anh D. Ha
- Department of Biological Sciences, Virginia Tech, Blacksburg, VA 24061, USA
| | - Will Harvey
- The Roslin Institute, University of Edinburgh, Edinburgh EH25 9RG, UK
| | - Terry C. Jones
- European Virus Bioinformatics Center, 07743 Jena, Germany (A.B.A.); (L.B.); (S.D.); (C.E.); (S.F.E.); (T.C.J.); (K.L.); (G.L.L.); (M.K.N.); (B.Y.R.); (F.G.-C.); (A.P.-C.); (W.R.); (G.S.)
- Institute of Virology, Charité, Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
- Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK
| | - Kevin Lamkiewicz
- European Virus Bioinformatics Center, 07743 Jena, Germany (A.B.A.); (L.B.); (S.D.); (C.E.); (S.F.E.); (T.C.J.); (K.L.); (G.L.L.); (M.K.N.); (B.Y.R.); (F.G.-C.); (A.P.-C.); (W.R.); (G.S.)
- RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, 07743 Jena, Germany;
| | - Gabriel L. Lovate
- European Virus Bioinformatics Center, 07743 Jena, Germany (A.B.A.); (L.B.); (S.D.); (C.E.); (S.F.E.); (T.C.J.); (K.L.); (G.L.L.); (M.K.N.); (B.Y.R.); (F.G.-C.); (A.P.-C.); (W.R.); (G.S.)
- RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, 07743 Jena, Germany;
| | - Dominik Lücking
- Max-Planck Institute for Marine Microbiology, Celsiusstraße 1, 28359 Bremen, Germany
| | - Martin Machyna
- Paul-Ehrlich-Institut, Host-Pathogen-Interactions, 63225 Langen, Germany
| | - Luca Nishimura
- European Virus Bioinformatics Center, 07743 Jena, Germany (A.B.A.); (L.B.); (S.D.); (C.E.); (S.F.E.); (T.C.J.); (K.L.); (G.L.L.); (M.K.N.); (B.Y.R.); (F.G.-C.); (A.P.-C.); (W.R.); (G.S.)
- Department of Genetics, School of Life Science, The Graduate University for Advanced Studies (SOKENDAI), Mishima 411-8540, Japan
- Human Genetics Laboratory, National Institute of Genetics, Mishima 411-8540, Japan
| | - Maximilian K. Nocke
- European Virus Bioinformatics Center, 07743 Jena, Germany (A.B.A.); (L.B.); (S.D.); (C.E.); (S.F.E.); (T.C.J.); (K.L.); (G.L.L.); (M.K.N.); (B.Y.R.); (F.G.-C.); (A.P.-C.); (W.R.); (G.S.)
- Department for Molecular & Medical Virology, Ruhr University Bochum, 44801 Bochum, Germany
| | - Bernard Y. Renard
- European Virus Bioinformatics Center, 07743 Jena, Germany (A.B.A.); (L.B.); (S.D.); (C.E.); (S.F.E.); (T.C.J.); (K.L.); (G.L.L.); (M.K.N.); (B.Y.R.); (F.G.-C.); (A.P.-C.); (W.R.); (G.S.)
- Digital Engineering Faculty, Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany
| | - Shoichi Sakaguchi
- Department of Microbiology and Infection Control, Faculty of Medicine, Osaka Medical and Pharmaceutical University, Osaka 569-8686, Japan;
| | - Lygeri Sakellaridi
- Institute for Virology and Immunobiology, University of Würzburg, Versbacher Str. 7, 97078 Würzburg, Germany
| | - Jannes Spangenberg
- European Virus Bioinformatics Center, 07743 Jena, Germany (A.B.A.); (L.B.); (S.D.); (C.E.); (S.F.E.); (T.C.J.); (K.L.); (G.L.L.); (M.K.N.); (B.Y.R.); (F.G.-C.); (A.P.-C.); (W.R.); (G.S.)
- RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, 07743 Jena, Germany;
| | - Maria Tarradas-Alemany
- Computational Genomics Lab., Department of Genetics, Microbiology and Statistics, Institut de Biomedicina UB (IBUB), Universitat de Barcelona (UB), 08028 Barcelona, Spain
| | - Sandra Triebel
- European Virus Bioinformatics Center, 07743 Jena, Germany (A.B.A.); (L.B.); (S.D.); (C.E.); (S.F.E.); (T.C.J.); (K.L.); (G.L.L.); (M.K.N.); (B.Y.R.); (F.G.-C.); (A.P.-C.); (W.R.); (G.S.)
- RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, 07743 Jena, Germany;
| | - Yulia Vakulenko
- Martsinovsky Institute of Medical Parasitology, Tropical and Vector Borne Diseases, Sechenov First Moscow State Medical University, 119991 Moscow, Russia
| | - Rajitha Yasas Wijesekara
- Institute for Bioinformatics, University of Medicine Greifswald, Felix-Hausdorff-Str. 8, 17475 Greifswald, Germany
| | - Fernando González-Candelas
- European Virus Bioinformatics Center, 07743 Jena, Germany (A.B.A.); (L.B.); (S.D.); (C.E.); (S.F.E.); (T.C.J.); (K.L.); (G.L.L.); (M.K.N.); (B.Y.R.); (F.G.-C.); (A.P.-C.); (W.R.); (G.S.)
- Institute for Integrative Systems Biology (I2SysBio), CSIC-Universitat de Valencia, Catedratico Agustin Escardino 9, 46980 Valencia, Spain
- Joint Research Unit “Infection and Public Health” FISABIO, University of Valencia, 46010 Valencia, Spain
| | - Sarah Krautwurst
- RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, 07743 Jena, Germany;
| | - Alba Pérez-Cataluña
- European Virus Bioinformatics Center, 07743 Jena, Germany (A.B.A.); (L.B.); (S.D.); (C.E.); (S.F.E.); (T.C.J.); (K.L.); (G.L.L.); (M.K.N.); (B.Y.R.); (F.G.-C.); (A.P.-C.); (W.R.); (G.S.)
- VISAFELab, Department of Preservation and Food Safety Technologies, Institute of Agrochemistry and Food Technology, IATA-CSIC, 46980 Valencia, Spain
| | - Walter Randazzo
- European Virus Bioinformatics Center, 07743 Jena, Germany (A.B.A.); (L.B.); (S.D.); (C.E.); (S.F.E.); (T.C.J.); (K.L.); (G.L.L.); (M.K.N.); (B.Y.R.); (F.G.-C.); (A.P.-C.); (W.R.); (G.S.)
- VISAFELab, Department of Preservation and Food Safety Technologies, Institute of Agrochemistry and Food Technology, IATA-CSIC, 46980 Valencia, Spain
| | - Gloria Sánchez
- European Virus Bioinformatics Center, 07743 Jena, Germany (A.B.A.); (L.B.); (S.D.); (C.E.); (S.F.E.); (T.C.J.); (K.L.); (G.L.L.); (M.K.N.); (B.Y.R.); (F.G.-C.); (A.P.-C.); (W.R.); (G.S.)
- VISAFELab, Department of Preservation and Food Safety Technologies, Institute of Agrochemistry and Food Technology, IATA-CSIC, 46980 Valencia, Spain
| | - Manja Marz
- European Virus Bioinformatics Center, 07743 Jena, Germany (A.B.A.); (L.B.); (S.D.); (C.E.); (S.F.E.); (T.C.J.); (K.L.); (G.L.L.); (M.K.N.); (B.Y.R.); (F.G.-C.); (A.P.-C.); (W.R.); (G.S.)
- RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, 07743 Jena, Germany;
- German Center for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, 04103 Leipzig, Germany
- Michael Stifel Center Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, 07745 Jena, Germany
- Leibniz Institute for Age Research—Fritz Lippman Institute, 07745 Jena, Germany
| |
Collapse
|
5
|
Layan M, Dacheux L, Lemey P, Brunker K, Ma L, Troupin C, Dussart P, Chevalier V, Wood JLN, Ly S, Duong V, Bourhy H, Dellicour S. Uncovering the endemic circulation of rabies in Cambodia. Mol Ecol 2023; 32:5140-5155. [PMID: 37540190 DOI: 10.1111/mec.17087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 07/18/2023] [Indexed: 08/05/2023]
Abstract
In epidemiology, endemicity characterizes sustained pathogen circulation in a geographical area, which involves a circulation that is not being maintained by external introductions. Because it could potentially shape the design of public health interventions, there is an interest in fully uncovering the endemic pattern of a disease. Here, we use a phylogeographic approach to investigate the endemic signature of rabies virus (RABV) circulation in Cambodia. Cambodia is located in one of the most affected regions by rabies in the world, but RABV circulation between and within Southeast Asian countries remains understudied. Our analyses are based on a new comprehensive data set of 199 RABV genomes collected between 2014 and 2017 as well as previously published Southeast Asian RABV sequences. We show that most Cambodian sequences belong to a distinct clade that has been circulating almost exclusively in Cambodia. Our results thus point towards rabies circulation in Cambodia that does not rely on external introductions. We further characterize within-Cambodia RABV circulation by estimating lineage dispersal metrics that appear to be similar to other settings, and by performing landscape phylogeographic analyses to investigate environmental factors impacting the dispersal dynamic of viral lineages. The latter analyses do not lead to the identification of environmental variables that would be associated with the heterogeneity of viral lineage dispersal velocities, which calls for a better understanding of local dog ecology and further investigations of the potential drivers of RABV spread in the region. Overall, our study illustrates how phylogeographic investigations can be performed to assess and characterize viral endemicity in a context of relatively limited data.
Collapse
Affiliation(s)
- Maylis Layan
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, UMR2000, CNRS, Paris, France
- Collège Doctoral, Sorbonne Université, Paris, France
| | - Laurent Dacheux
- Lyssavirus Epidemiology and Neuropathology Unit, Institut Pasteur, Université Paris Cité, Paris, France
- WHO Collaborating Centre for Reference and Research on Rabies, Institut Pasteur, Université Paris Cité, Paris, France
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory for Clinical and Epidemiological Virology, KU Leuven, University of Leuven, Leuven, Belgium
| | - Kirstyn Brunker
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK
| | - Laurence Ma
- Biomics, Center for Technological Resources and Research (C2RT), Institut Pasteur, Université Paris Cité, Paris, France
| | - Cécile Troupin
- Virology Unit, Institut Pasteur du Cambodge, Phnom Penh, Cambodia
| | - Philippe Dussart
- Virology Unit, Institut Pasteur du Cambodge, Phnom Penh, Cambodia
| | - Véronique Chevalier
- CIRAD, UMR ASTRE, Montpellier, France
- ASTRE, Univ. Montpellier CIRAD, INRAE, Montpellier, France
- Epidemiology and Clinical Research, Institut Pasteur de Madagascar, Antananarivo, Madagascar
| | - James L N Wood
- Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - Sowath Ly
- Epidemiology and Public Health, Institut Pasteur du Cambodge, Phnom Penh, Cambodia
| | - Veasna Duong
- Virology Unit, Institut Pasteur du Cambodge, Phnom Penh, Cambodia
| | - Hervé Bourhy
- Lyssavirus Epidemiology and Neuropathology Unit, Institut Pasteur, Université Paris Cité, Paris, France
- WHO Collaborating Centre for Reference and Research on Rabies, Institut Pasteur, Université Paris Cité, Paris, France
| | - Simon Dellicour
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory for Clinical and Epidemiological Virology, KU Leuven, University of Leuven, Leuven, Belgium
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Bruxelles, Belgium
| |
Collapse
|
6
|
Hollingsworth BD, Grubaugh ND, Lazzaro BP, Murdock CC. Leveraging insect-specific viruses to elucidate mosquito population structure and dynamics. PLoS Pathog 2023; 19:e1011588. [PMID: 37651317 PMCID: PMC10470969 DOI: 10.1371/journal.ppat.1011588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/02/2023] Open
Abstract
Several aspects of mosquito ecology that are important for vectored disease transmission and control have been difficult to measure at epidemiologically important scales in the field. In particular, the ability to describe mosquito population structure and movement rates has been hindered by difficulty in quantifying fine-scale genetic variation among populations. The mosquito virome represents a possible avenue for quantifying population structure and movement rates across multiple spatial scales. Mosquito viromes contain a diversity of viruses, including several insect-specific viruses (ISVs) and "core" viruses that have high prevalence across populations. To date, virome studies have focused on viral discovery and have only recently begun examining viral ecology. While nonpathogenic ISVs may be of little public health relevance themselves, they provide a possible route for quantifying mosquito population structure and dynamics. For example, vertically transmitted viruses could behave as a rapidly evolving extension of the host's genome. It should be possible to apply established analytical methods to appropriate viral phylogenies and incidence data to generate novel approaches for estimating mosquito population structure and dispersal over epidemiologically relevant timescales. By studying the virome through the lens of spatial and genomic epidemiology, it may be possible to investigate otherwise cryptic aspects of mosquito ecology. A better understanding of mosquito population structure and dynamics are key for understanding mosquito-borne disease ecology and methods based on ISVs could provide a powerful tool for informing mosquito control programs.
Collapse
Affiliation(s)
- Brandon D Hollingsworth
- Department of Entomology, Cornell University, Ithaca, New York, United States of America
- Cornell Institute for Host Microbe Interaction and Disease, Cornell University, Ithaca, New York, United States of America
| | - Nathan D Grubaugh
- Yale School of Public Health, New Haven, Connecticut, United States of America
- Yale University, New Haven, Connecticut, United States of America
| | - Brian P Lazzaro
- Department of Entomology, Cornell University, Ithaca, New York, United States of America
- Cornell Institute for Host Microbe Interaction and Disease, Cornell University, Ithaca, New York, United States of America
| | - Courtney C Murdock
- Department of Entomology, Cornell University, Ithaca, New York, United States of America
- Cornell Institute for Host Microbe Interaction and Disease, Cornell University, Ithaca, New York, United States of America
- Northeast Regional Center for Excellence in Vector-borne Diseases, Cornell University, Ithaca, New York, United States of America
| |
Collapse
|
7
|
Hamede R, Fountain‐Jones NM, Arce F, Jones M, Storfer A, Hohenlohe PA, McCallum H, Roche B, Ujvari B, Thomas F. The tumour is in the detail: Local phylogenetic, population and epidemiological dynamics of a transmissible cancer in Tasmanian devils. Evol Appl 2023; 16:1316-1327. [PMID: 37492149 PMCID: PMC10363845 DOI: 10.1111/eva.13569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 06/01/2023] [Accepted: 06/01/2023] [Indexed: 07/27/2023] Open
Abstract
Infectious diseases are a major threat for biodiversity conservation and can exert strong influence on wildlife population dynamics. Understanding the mechanisms driving infection rates and epidemic outcomes requires empirical data on the evolutionary trajectory of pathogens and host selective processes. Phylodynamics is a robust framework to understand the interaction of pathogen evolutionary processes with epidemiological dynamics, providing a powerful tool to evaluate disease control strategies. Tasmanian devils have been threatened by a fatal transmissible cancer, devil facial tumour disease (DFTD), for more than two decades. Here we employ a phylodynamic approach using tumour mitochondrial genomes to assess the role of tumour genetic diversity in epidemiological and population dynamics in a devil population subject to 12 years of intensive monitoring, since the beginning of the epidemic outbreak. DFTD molecular clock estimates of disease introduction mirrored observed estimates in the field, and DFTD genetic diversity was positively correlated with estimates of devil population size. However, prevalence and force of infection were the lowest when devil population size and tumour genetic diversity was the highest. This could be due to either differential virulence or transmissibility in tumour lineages or the development of host defence strategies against infection. Our results support the view that evolutionary processes and epidemiological trade-offs can drive host-pathogen coexistence, even when disease-induced mortality is extremely high. We highlight the importance of integrating pathogen and population evolutionary interactions to better understand long-term epidemic dynamics and evaluating disease control strategies.
Collapse
Affiliation(s)
- Rodrigo Hamede
- School of Natural SciencesUniversity of TasmaniaHobartTasmaniaAustralia
- CANECEV, Centre de Recherches Ecologiques et Evolutives sur le CancerMontpellierFrance
| | | | - Fernando Arce
- School of Natural SciencesUniversity of TasmaniaHobartTasmaniaAustralia
| | - Menna Jones
- School of Natural SciencesUniversity of TasmaniaHobartTasmaniaAustralia
| | - Andrew Storfer
- School of Biological SciencesWashington State UniversityPullmanWashingtonUSA
| | - Paul A. Hohenlohe
- Department of Biological Sciences, Institute for Bioinformatics and Evolutionary StudiesUniversity of IdahoMoscowIdahoUSA
| | - Hamish McCallum
- Centre for Planetary Health and Food SecurityGriffith University, Nathan CampusNathanQueenslandAustralia
| | - Benjamin Roche
- CREEC, MIVEGEC (CREES)University of Montpellier, CNRS, IRDMontpelierFrance
| | - Beata Ujvari
- CANECEV, Centre de Recherches Ecologiques et Evolutives sur le CancerMontpellierFrance
- Centre for Integrative Ecology, School of Life and Environmental SciencesDeakin UniversityWaurn PondsVictoriaAustralia
| | - Frédéric Thomas
- CANECEV, Centre de Recherches Ecologiques et Evolutives sur le CancerMontpellierFrance
- CREEC, MIVEGEC (CREES)University of Montpellier, CNRS, IRDMontpelierFrance
| |
Collapse
|
8
|
Salichos L, Warrell J, Cevasco H, Chung A, Gerstein M. Genetic determination of regional connectivity in modelling the spread of COVID-19 outbreak for more efficient mitigation strategies. Sci Rep 2023; 13:8470. [PMID: 37231011 DOI: 10.1038/s41598-023-34959-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 05/10/2023] [Indexed: 05/27/2023] Open
Abstract
For the COVID-19 pandemic, viral transmission has been documented in many historical and geographical contexts. Nevertheless, few studies have explicitly modeled the spatiotemporal flow based on genetic sequences, to develop mitigation strategies. Additionally, thousands of SARS-CoV-2 genomes have been sequenced with associated records, potentially providing a rich source for such spatiotemporal analysis, an unprecedented amount during a single outbreak. Here, in a case study of seven states, we model the first wave of the outbreak by determining regional connectivity from phylogenetic sequence information (i.e. "genetic connectivity"), in addition to traditional epidemiologic and demographic parameters. Our study shows nearly all of the initial outbreak can be traced to a few lineages, rather than disconnected outbreaks, indicative of a mostly continuous initial viral flow. While the geographic distance from hotspots is initially important in the modeling, genetic connectivity becomes increasingly significant later in the first wave. Moreover, our model predicts that isolated local strategies (e.g. relying on herd immunity) can negatively impact neighboring regions, suggesting more efficient mitigation is possible with unified, cross-border interventions. Finally, our results suggest that a few targeted interventions based on connectivity can have an effect similar to that of an overall lockdown. They also suggest that while successful lockdowns are very effective in mitigating an outbreak, less disciplined lockdowns quickly decrease in effectiveness. Our study provides a framework for combining phylodynamic and computational methods to identify targeted interventions.
Collapse
Affiliation(s)
- Leonidas Salichos
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA.
- Biological and Chemical Sciences, New York Institute of Technology, Manhattan, NY, 10023, USA.
| | - Jonathan Warrell
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
| | - Hannah Cevasco
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
| | - Alvin Chung
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
| | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA.
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA.
- Department of Computer Science, Yale University, New Haven, CT, 06520, USA.
- Center for Biomedical Data Science, Yale University, New Haven, CT, 06520, USA.
- Department of Statistics & Data Science, Yale University, New Haven, CT, 06520, USA.
| |
Collapse
|
9
|
Hassler GW, Magee A, Zhang Z, Baele G, Lemey P, Ji X, Fourment M, Suchard MA. Data integration in Bayesian phylogenetics. ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION 2022; 10:353-377. [PMID: 38774036 PMCID: PMC11108065 DOI: 10.1146/annurev-statistics-033021-112532] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2024]
Abstract
Researchers studying the evolution of viral pathogens and other organisms increasingly encounter and use large and complex data sets from multiple different sources. Statistical research in Bayesian phylogenetics has risen to this challenge. Researchers use phylogenetics not only to reconstruct the evolutionary history of a group of organisms, but also to understand the processes that guide its evolution and spread through space and time. To this end, it is now the norm to integrate numerous sources of data. For example, epidemiologists studying the spread of a virus through a region incorporate data including genetic sequences (e.g. DNA), time, location (both continuous and discrete) and environmental covariates (e.g. social connectivity between regions) into a coherent statistical model. Evolutionary biologists routinely do the same with genetic sequences, location, time, fossil and modern phenotypes, and ecological covariates. These complex, hierarchical models readily accommodate both discrete and continuous data and have enormous combined discrete/continuous parameter spaces including, at a minimum, phylogenetic tree topologies and branch lengths. The increased size and complexity of these statistical models have spurred advances in computational methods to make them tractable. We discuss both the modeling and computational advances below, as well as unsolved problems and areas of active research.
Collapse
Affiliation(s)
- Gabriel W Hassler
- Department of Computational Medicine, University of California, Los Angeles, USA, 90095
| | - Andrew Magee
- Department of Biostatistics, University of California, Los Angeles, USA, 90095
| | - Zhenyu Zhang
- Department of Biostatistics, University of California, Los Angeles, USA, 90095
| | - Guy Baele
- Department of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium, 3000
| | - Philippe Lemey
- Department of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium, 3000
| | - Xiang Ji
- Department of Mathematics, Tulane University, New Orleans, USA, 70118
| | - Mathieu Fourment
- Australian Institute for Microbiology and Infection, University of Technology Sydney, Ultimo NSW, Australia, 2007
| | - Marc A Suchard
- Department of Computational Medicine, University of California, Los Angeles, USA, 90095
- Department of Biostatistics, University of California, Los Angeles, USA, 90095
- Department of Human Genetics, University of California, Los Angeles, USA, 90095
| |
Collapse
|
10
|
Klitting R, Kafetzopoulou LE, Thiery W, Dudas G, Gryseels S, Kotamarthi A, Vrancken B, Gangavarapu K, Momoh M, Sandi JD, Goba A, Alhasan F, Grant DS, Okogbenin S, Ogbaini-Emovo E, Garry RF, Smither AR, Zeller M, Pauthner MG, McGraw M, Hughes LD, Duraffour S, Günther S, Suchard MA, Lemey P, Andersen KG, Dellicour S. Predicting the evolution of the Lassa virus endemic area and population at risk over the next decades. Nat Commun 2022; 13:5596. [PMID: 36167835 PMCID: PMC9515147 DOI: 10.1038/s41467-022-33112-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 09/02/2022] [Indexed: 01/27/2023] Open
Abstract
Lassa fever is a severe viral hemorrhagic fever caused by a zoonotic virus that repeatedly spills over to humans from its rodent reservoirs. It is currently not known how climate and land use changes could affect the endemic area of this virus, currently limited to parts of West Africa. By exploring the environmental data associated with virus occurrence using ecological niche modelling, we show how temperature, precipitation and the presence of pastures determine ecological suitability for virus circulation. Based on projections of climate, land use, and population changes, we find that regions in Central and East Africa will likely become suitable for Lassa virus over the next decades and estimate that the total population living in ecological conditions that are suitable for Lassa virus circulation may drastically increase by 2070. By analysing geotagged viral genomes using spatially-explicit phylogeography and simulating virus dispersal, we find that in the event of Lassa virus being introduced into a new suitable region, its spread might remain spatially limited over the first decades.
Collapse
Affiliation(s)
- Raphaëlle Klitting
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, 92037, USA.
| | - Liana E. Kafetzopoulou
- grid.5596.f0000 0001 0668 7884Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory for Clinical and Epidemiological Virology, KU Leuven - University of Leuven, Leuven, Belgium ,grid.424065.10000 0001 0701 3136Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany
| | - Wim Thiery
- grid.8767.e0000 0001 2290 8069Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel, Brussels, Belgium
| | - Gytis Dudas
- grid.6441.70000 0001 2243 2806Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Sophie Gryseels
- grid.5284.b0000 0001 0790 3681Evolutionary Ecology group, Department of Biology, University of Antwerp, 2610 Antwerp, Belgium ,grid.20478.390000 0001 2171 9581Vertebrate group, Directorate Taxonomy and Phylogeny, Royal Belgian Institute of Natural Sciences, 1000 Brussels, Belgium
| | - Anjali Kotamarthi
- grid.214007.00000000122199231Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA 92037 USA
| | - Bram Vrancken
- grid.5596.f0000 0001 0668 7884Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory for Clinical and Epidemiological Virology, KU Leuven - University of Leuven, Leuven, Belgium
| | - Karthik Gangavarapu
- grid.214007.00000000122199231Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA 92037 USA
| | - Mambu Momoh
- grid.442296.f0000 0001 2290 9707Eastern Technical University of Sierra Leone, Kenema, Sierra Leone ,grid.463455.50000 0004 1799 2069Viral Hemorrhagic Fever Program, Kenema Government Hospital, Ministry of Health and Sanitation, Kenema, Sierra Leone
| | - John Demby Sandi
- grid.463455.50000 0004 1799 2069Viral Hemorrhagic Fever Program, Kenema Government Hospital, Ministry of Health and Sanitation, Kenema, Sierra Leone
| | - Augustine Goba
- grid.463455.50000 0004 1799 2069Viral Hemorrhagic Fever Program, Kenema Government Hospital, Ministry of Health and Sanitation, Kenema, Sierra Leone
| | - Foday Alhasan
- grid.463455.50000 0004 1799 2069Viral Hemorrhagic Fever Program, Kenema Government Hospital, Ministry of Health and Sanitation, Kenema, Sierra Leone
| | - Donald S. Grant
- grid.463455.50000 0004 1799 2069Viral Hemorrhagic Fever Program, Kenema Government Hospital, Ministry of Health and Sanitation, Kenema, Sierra Leone ,grid.442296.f0000 0001 2290 9707College of Medicine and Allied Health Sciences, University of Sierra Leone, Kenema, Sierra Leone
| | - Sylvanus Okogbenin
- grid.508091.5Irrua Specialist Teaching Hospital, Irrua, Nigeria ,grid.411357.50000 0000 9018 355XFaculty of Clinical Sciences, College of Medicine, Ambrose Alli University, Ekpoma, Nigeria
| | | | - Robert F. Garry
- grid.265219.b0000 0001 2217 8588Department of Microbiology and Immunology, Tulane University, School of Medicine, New Orleans, LA 70112 USA ,grid.505518.c0000 0004 5901 1919Zalgen Labs, LCC, Frederick, MD 21703 USA ,grid.475149.aGlobal Virus Network (GVN), Baltimore, MD 21201 USA
| | - Allison R. Smither
- grid.265219.b0000 0001 2217 8588Department of Microbiology and Immunology, Tulane University, School of Medicine, New Orleans, LA 70112 USA
| | - Mark Zeller
- grid.214007.00000000122199231Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA 92037 USA
| | - Matthias G. Pauthner
- grid.214007.00000000122199231Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA 92037 USA
| | - Michelle McGraw
- grid.214007.00000000122199231Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA 92037 USA
| | - Laura D. Hughes
- grid.214007.00000000122199231Department of Integrative, Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037 USA
| | - Sophie Duraffour
- grid.424065.10000 0001 0701 3136Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany ,grid.452463.2German Center for Infection Research (DZIF), Partner site Hamburg–Lübeck–Borstel–Riems, Hamburg, Germany
| | - Stephan Günther
- grid.424065.10000 0001 0701 3136Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany ,grid.452463.2German Center for Infection Research (DZIF), Partner site Hamburg–Lübeck–Borstel–Riems, Hamburg, Germany
| | - Marc A. Suchard
- grid.19006.3e0000 0000 9632 6718Department of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, CA USA ,grid.19006.3e0000 0000 9632 6718Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, CA USA ,grid.19006.3e0000 0000 9632 6718Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA USA
| | - Philippe Lemey
- grid.5596.f0000 0001 0668 7884Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory for Clinical and Epidemiological Virology, KU Leuven - University of Leuven, Leuven, Belgium
| | - Kristian G. Andersen
- grid.214007.00000000122199231Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA 92037 USA ,grid.214007.00000000122199231Scripps Research Translational Institute, La Jolla, CA 92037 USA
| | - Simon Dellicour
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory for Clinical and Epidemiological Virology, KU Leuven - University of Leuven, Leuven, Belgium. .,Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, CP160/12 50, av. FD Roosevelt, 1050, Bruxelles, Belgium.
| |
Collapse
|
11
|
Nahata KD, Bielejec F, Monetta J, Dellicour S, Rambaut A, Suchard MA, Baele G, Lemey P. SPREAD 4: online visualisation of pathogen phylogeographic reconstructions. Virus Evol 2022; 8:veac088. [PMID: 36325034 PMCID: PMC9615431 DOI: 10.1093/ve/veac088] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 09/16/2022] [Indexed: 01/27/2023] Open
Abstract
Phylogeographic analyses aim to extract information about pathogen spread from genomic data, and visualising spatio-temporal reconstructions is a key aspect of this process. Here we present SPREAD 4, a feature-rich web-based application that visualises estimates of pathogen dispersal resulting from Bayesian phylogeographic inference using BEAST on a geographic map, offering zoom-and-filter functionality and smooth animation over time. SPREAD 4 takes as input phylogenies with both discrete and continuous location annotation and offers customised visualisation as well as generation of publication-ready figures. SPREAD 4 now features account-based storage and easy sharing of visualisations by means of unique web addresses. SPREAD 4 is intuitive to use and is available online at https://spreadviz.org, with an accompanying web page containing answers to frequently asked questions at https://beast.community/spread4.
Collapse
Affiliation(s)
- Kanika D Nahata
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Herestraat 49, Leuven 3000, Belgium
| | - Filip Bielejec
- Nonce Filip Bielejec, Łódź Voivodeship, 90-245 Lodz, Poland
| | - Juan Monetta
- Departamento de Montevideo, Guayabos 1924, Montevideo 11200,Uruguay
| | | | | | | | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Herestraat 49, Leuven 3000, Belgium
| | | |
Collapse
|
12
|
Dellicour S, Lemey P, Suchard MA, Gilbert M, Baele G. Accommodating sampling location uncertainty in continuous phylogeography. Virus Evol 2022; 8:veac041. [PMID: 35795297 PMCID: PMC9248920 DOI: 10.1093/ve/veac041] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 04/22/2022] [Accepted: 05/18/2022] [Indexed: 02/01/2023] Open
Abstract
Phylogeographic inference of the dispersal history of viral lineages offers key opportunities to tackle epidemiological questions about the spread of fast-evolving pathogens across human, animal and plant populations. In continuous space, i.e. when locations are specified by longitude and latitude, these reconstructions are however often limited by the availability or accessibility of precise sampling locations required for such spatially explicit analyses. We here review the different approaches that can be considered when genomic sequences are associated with a geographic area of sampling instead of precise coordinates. In particular, we describe and compare the approaches to define homogeneous and heterogeneous prior ranges of sampling coordinates.
Collapse
Affiliation(s)
| | | | | | - Marius Gilbert
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, CP160/12, 50 av. FD Roosevelt, Bruxelles 1050, Belgium
| | | |
Collapse
|
13
|
Hoehn KB, Pybus OG, Kleinstein SH. Phylogenetic analysis of migration, differentiation, and class switching in B cells. PLoS Comput Biol 2022; 18:e1009885. [PMID: 35468128 PMCID: PMC9037912 DOI: 10.1371/journal.pcbi.1009885] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 01/31/2022] [Indexed: 11/20/2022] Open
Abstract
B cells undergo rapid mutation and selection for antibody binding affinity when producing antibodies capable of neutralizing pathogens. This evolutionary process can be intermixed with migration between tissues, differentiation between cellular subsets, and switching between functional isotypes. B cell receptor (BCR) sequence data has the potential to elucidate important information about these processes. However, there is currently no robust, generalizable framework for making such inferences from BCR sequence data. To address this, we develop three parsimony-based summary statistics to characterize migration, differentiation, and isotype switching along B cell phylogenetic trees. We use simulations to demonstrate the effectiveness of this approach. We then use this framework to infer patterns of cellular differentiation and isotype switching from high throughput BCR sequence datasets obtained from patients in a study of HIV infection and a study of food allergy. These methods are implemented in the R package dowser, available at https://dowser.readthedocs.io. B cells produce high affinity antibodies through an evolutionary process of mutation and selection during adaptive immune responses. Migration between tissues, differentiation to cellular subtypes, and switching between different antibody isotypes can be important factors in shaping the role B cells play in response to infection, autoimmune disease, and allergies. B cell receptor (BCR) sequence data has the potential to elucidate important information about these processes. However, there is currently no robust, generalizable framework for making such inferences from BCR sequence data. Here, we develop three parsimony-based summary statistics to characterize migration, differentiation, and isotype switching along B cell phylogenetic trees. We confirm the effectiveness of our approach using simulations and further apply our method to data from patients with HIV and allergy. Our methods are released in the R package dowser: https://dowser.readthedocs.io.
Collapse
Affiliation(s)
- Kenneth B. Hoehn
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut, United States of America
| | - Oliver G. Pybus
- Department of Zoology, University of Oxford, Oxford, United Kingdom
- Department of Pathobiology and Population Sciences, Royal Veterinary College London, London, United Kingdom
| | - Steven H. Kleinstein
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut, United States of America
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
- Department of Immunobiology, Yale School of Medicine, New Haven, Connecticut, United States of America
- * E-mail:
| |
Collapse
|
14
|
Xavier JB, Monk JM, Poudel S, Norsigian CJ, Sastry AV, Liao C, Bento J, Suchard MA, Arrieta-Ortiz ML, Peterson EJ, Baliga NS, Stoeger T, Ruffin F, Richardson RA, Gao CA, Horvath TD, Haag AM, Wu Q, Savidge T, Yeaman MR. Mathematical models to study the biology of pathogens and the infectious diseases they cause. iScience 2022; 25:104079. [PMID: 35359802 PMCID: PMC8961237 DOI: 10.1016/j.isci.2022.104079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Mathematical models have many applications in infectious diseases: epidemiologists use them to forecast outbreaks and design containment strategies; systems biologists use them to study complex processes sustaining pathogens, from the metabolic networks empowering microbial cells to ecological networks in the microbiome that protects its host. Here, we (1) review important models relevant to infectious diseases, (2) draw parallels among models ranging widely in scale. We end by discussing a minimal set of information for a model to promote its use by others and to enable predictions that help us better fight pathogens and the diseases they cause.
Collapse
Affiliation(s)
- Joao B. Xavier
- Program for Computational and Systems Biology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | | | - Saugat Poudel
- Department of Bioengineering, UC San Diego, San Diego, CA, USA
| | | | - Anand V. Sastry
- Department of Bioengineering, UC San Diego, San Diego, CA, USA
| | - Chen Liao
- Program for Computational and Systems Biology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Jose Bento
- Computer Science Department, Boston College, Chestnut Hill, MA, USA
| | - Marc A. Suchard
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA
| | | | | | | | - Thomas Stoeger
- Department of Chemical and Biological Engineering; Northwestern University, Evanston, IL 60208, USA
- Successful Clinical Response in Pneumonia Therapy (SCRIPT) Systems Biology Center, Northwestern University, Chicago, IL, USA
| | - Felicia Ruffin
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Reese A.K. Richardson
- Department of Chemical and Biological Engineering; Northwestern University, Evanston, IL 60208, USA
- Successful Clinical Response in Pneumonia Therapy (SCRIPT) Systems Biology Center, Northwestern University, Chicago, IL, USA
| | - Catherine A. Gao
- Successful Clinical Response in Pneumonia Therapy (SCRIPT) Systems Biology Center, Northwestern University, Chicago, IL, USA
- Division of Pulmonary and Critical Care, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Thomas D. Horvath
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Pathology, Texas Children’s Microbiome Center, Texas Children’s Hospital, Houston, TX 77030, USA
| | - Anthony M. Haag
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Pathology, Texas Children’s Microbiome Center, Texas Children’s Hospital, Houston, TX 77030, USA
| | - Qinglong Wu
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Pathology, Texas Children’s Microbiome Center, Texas Children’s Hospital, Houston, TX 77030, USA
| | - Tor Savidge
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Pathology, Texas Children’s Microbiome Center, Texas Children’s Hospital, Houston, TX 77030, USA
| | - Michael R. Yeaman
- David Geffen School of Medicine at UCLA & Lundquist Institute for Infection & Immunity at Harbor UCLA Medical Center, Los Angeles, CA, USA
| |
Collapse
|
15
|
Nian F, Shi Y, Cao J. COVID-19 Propagation Model Based on Economic Development and Interventions. WIRELESS PERSONAL COMMUNICATIONS 2022; 122:2355-2365. [PMID: 34421225 PMCID: PMC8371428 DOI: 10.1007/s11277-021-08998-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/08/2021] [Indexed: 05/03/2023]
Abstract
In order to understand the influencing factors affecting the COVID-19 propagation, and analyze the development trend of the epidemic situation in the world, COVID-19 propagation model to simulate the COVID-19 propagation in the population is proposed in this paper. First of all, this paper analyzes the economic factors and interventions affecting the COVID-19 propagation in various different countries. Then, the touch number for COVID-19 High-risk Population Dynamic Network in this paper was redefined, and it predicts and analyzes the development trend of the epidemic situation in different countries. The simulation data and the published confirmed data by the world health organization could fit well, which also verified the reliability of the model. Finally, this paper also analyzes the impact of public awareness of prevention on the control of the epidemic. The analysis shows that increasing the awareness of prevention, timely and early adoption of protective measures such as wearing masks, and reducing travel can greatly reduce the risk of infection and the outbreak scale.
Collapse
Affiliation(s)
- Fuzhong Nian
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050 China
| | - Yayong Shi
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050 China
| | - Jun Cao
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050 China
| |
Collapse
|
16
|
Nahata KD, Bollen N, Gill MS, Layan M, Bourhy H, Dellicour S, Baele G. On the Use of Phylogeographic Inference to Infer the Dispersal History of Rabies Virus: A Review Study. Viruses 2021; 13:v13081628. [PMID: 34452492 PMCID: PMC8402743 DOI: 10.3390/v13081628] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/06/2021] [Accepted: 08/11/2021] [Indexed: 12/28/2022] Open
Abstract
Rabies is a neglected zoonotic disease which is caused by negative strand RNA-viruses belonging to the genus Lyssavirus. Within this genus, rabies viruses circulate in a diverse set of mammalian reservoir hosts, is present worldwide, and is almost always fatal in non-vaccinated humans. Approximately 59,000 people are still estimated to die from rabies each year, leading to a global initiative to work towards the goal of zero human deaths from dog-mediated rabies by 2030, requiring scientific efforts from different research fields. The past decade has seen a much increased use of phylogeographic and phylodynamic analyses to study the evolution and spread of rabies virus. We here review published studies in these research areas, making a distinction between the geographic resolution associated with the available sequence data. We pay special attention to environmental factors that these studies found to be relevant to the spread of rabies virus. Importantly, we highlight a knowledge gap in terms of applying these methods when all required data were available but not fully exploited. We conclude with an overview of recent methodological developments that have yet to be applied in phylogeographic and phylodynamic analyses of rabies virus.
Collapse
Affiliation(s)
- Kanika D. Nahata
- Department of Microbiology, Immunology and Transplantation, Rega Institute KU Leuven, 3000 Leuven, Belgium; (N.B.); (M.S.G.); (S.D.); (G.B.)
- Correspondence:
| | - Nena Bollen
- Department of Microbiology, Immunology and Transplantation, Rega Institute KU Leuven, 3000 Leuven, Belgium; (N.B.); (M.S.G.); (S.D.); (G.B.)
| | - Mandev S. Gill
- Department of Microbiology, Immunology and Transplantation, Rega Institute KU Leuven, 3000 Leuven, Belgium; (N.B.); (M.S.G.); (S.D.); (G.B.)
| | - Maylis Layan
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Sorbonne Université, UMR2000, CNRS, 75015 Paris, France;
| | - Hervé Bourhy
- Lyssavirus Epidemiology and Neuropathology Unit, Institut Pasteur, 75015 Paris, France;
- WHO Collaborating Centre for Reference and Research on Rabies, Institut Pasteur, 75015 Paris, France
| | - Simon Dellicour
- Department of Microbiology, Immunology and Transplantation, Rega Institute KU Leuven, 3000 Leuven, Belgium; (N.B.); (M.S.G.); (S.D.); (G.B.)
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, 1050 Bruxelles, Belgium
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute KU Leuven, 3000 Leuven, Belgium; (N.B.); (M.S.G.); (S.D.); (G.B.)
| |
Collapse
|
17
|
Accounting for the Biological Complexity of Pathogenic Fungi in Phylogenetic Dating. J Fungi (Basel) 2021; 7:jof7080661. [PMID: 34436200 PMCID: PMC8400180 DOI: 10.3390/jof7080661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 08/11/2021] [Accepted: 08/11/2021] [Indexed: 11/17/2022] Open
Abstract
In the study of pathogen evolution, temporal dating of phylogenies provides information on when species and lineages may have diverged in the past. When combined with spatial and epidemiological data in phylodynamic models, these dated phylogenies can also help infer where and when outbreaks occurred, how pathogens may have spread to new geographic locations and/or niches, and how virulence or drug resistance has developed over time. Although widely applied to viruses and, increasingly, to bacterial pathogen outbreaks, phylogenetic dating is yet to be widely used in the study of pathogenic fungi. Fungi are complex organisms with several biological processes that could present issues with appropriate inference of phylogenies, clock rates, and divergence times, including high levels of recombination and slower mutation rates although with potentially high levels of mutation rate variation. Here, we discuss some of the key methodological challenges in accurate phylogeny reconstruction for fungi in the context of the temporal analyses conducted to date and make recommendations for future dating studies to aid development of a best practices roadmap in light of the increasing threat of fungal outbreaks and antifungal drug resistance worldwide.
Collapse
|
18
|
Dellicour S, Gill MS, Faria NR, Rambaut A, Pybus OG, Suchard MA, Lemey P. Relax, Keep Walking - A Practical Guide to Continuous Phylogeographic Inference with BEAST. Mol Biol Evol 2021; 38:3486-3493. [PMID: 33528560 PMCID: PMC8321535 DOI: 10.1093/molbev/msab031] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Spatially explicit phylogeographic analyses can be performed with an inference framework that employs relaxed random walks to reconstruct phylogenetic dispersal histories in continuous space. This core model was first implemented 10 years ago and has opened up new opportunities in the field of phylodynamics, allowing researchers to map and analyze the spatial dissemination of rapidly evolving pathogens. We here provide a detailed and step-by-step guide on how to set up, run, and interpret continuous phylogeographic analyses using the programs BEAUti, BEAST, Tracer, and TreeAnnotator.
Collapse
Affiliation(s)
- Simon Dellicour
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Bruxelles, Belgium
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Mandev S Gill
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Nuno R Faria
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Imperial College London, London, United Kingdom
- Department of Zoology, University of Oxford, Oxford, United Kingdom
- Instituto de Medicina Tropical, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Andrew Rambaut
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, United Kingdom
| | - Oliver G Pybus
- Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Marc A Suchard
- Department of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| |
Collapse
|
19
|
Bendall ML, Gibson KM, Steiner MC, Rentia U, Pérez-Losada M, Crandall KA. HAPHPIPE: Haplotype Reconstruction and Phylodynamics for Deep Sequencing of Intrahost Viral Populations. Mol Biol Evol 2021; 38:1677-1690. [PMID: 33367849 PMCID: PMC8042772 DOI: 10.1093/molbev/msaa315] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Deep sequencing of viral populations using next-generation sequencing (NGS) offers opportunities to understand and investigate evolution, transmission dynamics, and population genetics. Currently, the standard practice for processing NGS data to study viral populations is to summarize all the observed sequences from a sample as a single consensus sequence, thus discarding valuable information about the intrahost viral molecular epidemiology. Furthermore, existing analytical pipelines may only analyze genomic regions involved in drug resistance, thus are not suited for full viral genome analysis. Here, we present HAPHPIPE, a HAplotype and PHylodynamics PIPEline for genome-wide assembly of viral consensus sequences and haplotypes. The HAPHPIPE protocol includes modules for quality trimming, error correction, de novo assembly, alignment, and haplotype reconstruction. The resulting consensus sequences, haplotypes, and alignments can be further analyzed using a variety of phylogenetic and population genetic software. HAPHPIPE is designed to provide users with a single pipeline to rapidly analyze sequences from viral populations generated from NGS platforms and provide quality output properly formatted for downstream evolutionary analyses.
Collapse
Affiliation(s)
- Matthew L Bendall
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Keylie M Gibson
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Margaret C Steiner
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Uzma Rentia
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Marcos Pérez-Losada
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA.,Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA.,CIBIO-InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Vairão, Portugal
| | - Keith A Crandall
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA.,Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| |
Collapse
|
20
|
Munsey A, Mwiine FN, Ochwo S, Velazquez-Salinas L, Ahmed Z, Maree F, Rodriguez LL, Rieder E, Perez A, Dellicour S, VanderWaal K. Phylogeographic analysis of foot-and-mouth disease virus serotype O dispersal and associated drivers in East Africa. Mol Ecol 2021; 30:3815-3825. [PMID: 34008868 DOI: 10.1111/mec.15991] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 05/06/2021] [Accepted: 05/10/2021] [Indexed: 02/06/2023]
Abstract
The continued endemicity of foot and mouth disease virus (FMDV) in East Africa has significant implications for livestock production and poverty reduction, yet its complex epidemiology in endemic settings remains poorly understood. Identifying FMDV dispersal routes and drivers of transmission is key to improved control strategies. Environmental heterogeneity and anthropogenic drivers (e.g., demand for animal products) can impact viral spread by influencing host movements. Here, we utilized FMDV serotype O VP1 genetic sequences and corresponding spatiotemporal data in order to (i) infer the recent dispersal history, and (II) investigate the impact of external factors (cattle density, human population density, proximity to livestock markets, and drought) on dispersal velocity, location, and direction of FMDV serotype O in East Africa. We identified statistical evidence of long-distance transmission events, and we found that FMDV serotype O tends to remain circulating in areas of high cattle density, high human population density, and in close proximity to livestock markets. The latter two findings highlight the influence of anthropogenic factors on FMDV serotype O spread in this region. These findings contribute to the understanding of FMDV epidemiology in East Africa and can help guide improved control measures.
Collapse
Affiliation(s)
- Anna Munsey
- Veterinary Population Medicine Department, University of Minnesota College of Veterinary Medicine, St. Paul, MN, USA
| | - Frank Norbert Mwiine
- Makerere University College of Veterinary Medicine, Animal Resources and Biosecurity (COVAB), Kampala, Uganda
| | - Sylvester Ochwo
- Makerere University College of Veterinary Medicine, Animal Resources and Biosecurity (COVAB), Kampala, Uganda
| | - Lauro Velazquez-Salinas
- Veterinary Population Medicine Department, University of Minnesota College of Veterinary Medicine, St. Paul, MN, USA.,Foreign Animal Disease Research Unit, Plum Island Animal Disease Center, Agricultural Research Service (ARS), United States Department of Agriculture, Greenport, NY, USA
| | - Zaheer Ahmed
- Animal and Plant Health Inspection Services (APHIS), National Veterinary Services Laboratories, Foreign Animal Disease Diagnostic Laboratory, Plum Island Animal Disease Center, United States Department of Agriculture, Greenport, NY, USA
| | - Francois Maree
- Onderstepoort Veterinary Institute, Pretoria, South Africa
| | - Luis L Rodriguez
- Foreign Animal Disease Research Unit, Plum Island Animal Disease Center, Agricultural Research Service (ARS), United States Department of Agriculture, Greenport, NY, USA
| | - Elizabeth Rieder
- Foreign Animal Disease Research Unit, Plum Island Animal Disease Center, Agricultural Research Service (ARS), United States Department of Agriculture, Greenport, NY, USA
| | - Andres Perez
- Veterinary Population Medicine Department, University of Minnesota College of Veterinary Medicine, St. Paul, MN, USA
| | - Simon Dellicour
- Spatial Epidemiology Laboratory (SpELL), Université Libre de Bruxelles, Bruxelles, Belgium.,Department of Microbiology, Immunology and Transplantation, Laboratory for Clinical and Epidemiological Virology, Rega Institute, KU Leuven, Leuven, Belgium
| | - Kimberly VanderWaal
- Veterinary Population Medicine Department, University of Minnesota College of Veterinary Medicine, St. Paul, MN, USA
| |
Collapse
|
21
|
Mathematical modelling and phylodynamics for the study of dog rabies dynamics and control: A scoping review. PLoS Negl Trop Dis 2021; 15:e0009449. [PMID: 34043640 PMCID: PMC8189497 DOI: 10.1371/journal.pntd.0009449] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 06/09/2021] [Accepted: 05/05/2021] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Rabies is a fatal yet vaccine-preventable disease. In the last two decades, domestic dog populations have been shown to constitute the predominant reservoir of rabies in developing countries, causing 99% of human rabies cases. Despite substantial control efforts, dog rabies is still widely endemic and is spreading across previously rabies-free areas. Developing a detailed understanding of dog rabies dynamics and the impact of vaccination is essential to optimize existing control strategies and developing new ones. In this scoping review, we aimed at disentangling the respective contributions of mathematical models and phylodynamic approaches to advancing the understanding of rabies dynamics and control in domestic dog populations. We also addressed the methodological limitations of both approaches and the remaining issues related to studying rabies spread and how this could be applied to rabies control. METHODOLOGY/PRINCIPAL FINDINGS We reviewed how mathematical modelling of disease dynamics and phylodynamics have been developed and used to characterize dog rabies dynamics and control. Through a detailed search of the PubMed, Web of Science, and Scopus databases, we identified a total of n = 59 relevant studies using mathematical models (n = 30), phylodynamic inference (n = 22) and interdisciplinary approaches (n = 7). We found that despite often relying on scarce rabies epidemiological data, mathematical models investigated multiple aspects of rabies dynamics and control. These models confirmed the overwhelming efficacy of massive dog vaccination campaigns in all settings and unraveled the role of dog population structure and frequent introductions in dog rabies maintenance. Phylodynamic approaches successfully disentangled the evolutionary and environmental determinants of rabies dispersal and consistently reported support for the role of reintroduction events and human-mediated transportation over long distances in the maintenance of rabies in endemic areas. Potential biases in data collection still need to be properly accounted for in most of these analyses. Finally, interdisciplinary studies were determined to provide the most comprehensive assessments through hypothesis generation and testing. They also represent new avenues, especially concerning the reconstruction of local transmission chains or clusters through data integration. CONCLUSIONS/SIGNIFICANCE Despite advances in rabies knowledge, substantial uncertainty remains regarding the mechanisms of local spread, the role of wildlife in dog rabies maintenance, and the impact of community behavior on the efficacy of control strategies including vaccination of dogs. Future integrative approaches that use phylodynamic analyses and mechanistic models within a single framework could take full advantage of not only viral sequences but also additional epidemiological information as well as dog ecology data to refine our understanding of rabies spread and control. This would represent a significant improvement on past studies and a promising opportunity for canine rabies research in the frame of the One Health concept that aims to achieve better public health outcomes through cross-sector collaboration.
Collapse
|
22
|
Tao Y, Hite JL, Lafferty KD, Earn DJD, Bharti N. Transient disease dynamics across ecological scales. THEOR ECOL-NETH 2021; 14:625-640. [PMID: 34075317 PMCID: PMC8156581 DOI: 10.1007/s12080-021-00514-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 05/04/2021] [Indexed: 11/25/2022]
Abstract
Analyses of transient dynamics are critical to understanding infectious disease transmission and persistence. Identifying and predicting transients across scales, from within-host to community-level patterns, plays an important role in combating ongoing epidemics and mitigating the risk of future outbreaks. Moreover, greater emphases on non-asymptotic processes will enable timely evaluations of wildlife and human diseases and lead to improved surveillance efforts, preventive responses, and intervention strategies. Here, we explore the contributions of transient analyses in recent models spanning the fields of epidemiology, movement ecology, and parasitology. In addition to their roles in predicting epidemic patterns and endemic outbreaks, we explore transients in the contexts of pathogen transmission, resistance, and avoidance at various scales of the ecological hierarchy. Examples illustrate how (i) transient movement dynamics at the individual host level can modify opportunities for transmission events over time; (ii) within-host energetic processes often lead to transient dynamics in immunity, pathogen load, and transmission potential; (iii) transient connectivity between discrete populations in response to environmental factors and outbreak dynamics can affect disease spread across spatial networks; and (iv) increasing species richness in a community can provide transient protection to individuals against infection. Ultimately, we suggest that transient analyses offer deeper insights and raise new, interdisciplinary questions for disease research, consequently broadening the applications of dynamical models for outbreak preparedness and management. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s12080-021-00514-w.
Collapse
Affiliation(s)
- Yun Tao
- Intelligence Community Postdoctoral Research Fellowship Program, Department of Ecology, Evolution and Marine Biology, University of California, Santa Barbara, CA 93106 USA
| | - Jessica L. Hite
- School of Veterinary Medicine, Department of Pathobiological Sciences, University of Wisconsin, Madison, WI 53706 USA
| | - Kevin D. Lafferty
- Western Ecological Research Center at UCSB Marine Science Institute, U.S. Geological Survey, CA 93106 Santa Barbara, USA
| | - David J. D. Earn
- Department of Mathematics and Statistics, McMaster University, Hamilton, ON L8S 4K1 Canada
| | - Nita Bharti
- Department of Biology Center for Infectious Disease Dynamics, Penn State University, University Park, PA 16802 USA
| |
Collapse
|
23
|
Brissette C, Niu X, Jiang C, Gao J, Korniss G, Szymanski BK. Heuristic assessment of choices for risk network control. Sci Rep 2021; 11:7645. [PMID: 33828120 PMCID: PMC8026632 DOI: 10.1038/s41598-021-85432-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 02/28/2021] [Indexed: 11/17/2022] Open
Abstract
Data-driven risk networks describe many complex system dynamics arising in fields such as epidemiology and ecology. They lack explicit dynamics and have multiple sources of cost, both of which are beyond the current scope of traditional control theory. We construct the global economy risk network by combining the consensus of experts from the World Economic Forum with risk activation data to define its topology and interactions. Many of these risks, including extreme weather and drastic inflation, pose significant economic costs when active. We introduce a method for converting network interaction data into continuous dynamics to which we apply optimal control. We contribute the first method for constructing and controlling risk network dynamics based on empirically collected data. We simulate applying this method to control the spread of COVID-19 and show that the choice of risks through which the network is controlled has significant influence on both the cost of control and the total cost of keeping network stable. We additionally describe a heuristic for choosing the risks trough which the network is controlled, given a general risk network.
Collapse
Affiliation(s)
- Christopher Brissette
- Network Science and Technology Center, Rensselaer Polytechnic Institute (RPI), Troy, NY, 12180, USA.,Department of Computer Science, Rensselaer Polytechnic Institute (RPI), Troy, NY, 12180, USA
| | - Xiang Niu
- Network Science and Technology Center, Rensselaer Polytechnic Institute (RPI), Troy, NY, 12180, USA.,Department of Computer Science, Rensselaer Polytechnic Institute (RPI), Troy, NY, 12180, USA
| | - Chunheng Jiang
- Network Science and Technology Center, Rensselaer Polytechnic Institute (RPI), Troy, NY, 12180, USA.,Department of Computer Science, Rensselaer Polytechnic Institute (RPI), Troy, NY, 12180, USA
| | - Jianxi Gao
- Network Science and Technology Center, Rensselaer Polytechnic Institute (RPI), Troy, NY, 12180, USA.,Department of Computer Science, Rensselaer Polytechnic Institute (RPI), Troy, NY, 12180, USA
| | - Gyorgy Korniss
- Network Science and Technology Center, Rensselaer Polytechnic Institute (RPI), Troy, NY, 12180, USA.,Department of Physics, Applied Physics, and Astronomy, Rensselaer Polytechnic Institute (RPI), Troy, NY, 12180, USA
| | - Boleslaw K Szymanski
- Network Science and Technology Center, Rensselaer Polytechnic Institute (RPI), Troy, NY, 12180, USA. .,Department of Computer Science, Rensselaer Polytechnic Institute (RPI), Troy, NY, 12180, USA. .,Department of Physics, Applied Physics, and Astronomy, Rensselaer Polytechnic Institute (RPI), Troy, NY, 12180, USA. .,Społeczna Akademia Nauk, Łódź, Poland.
| |
Collapse
|
24
|
Sixteen novel lineages of SARS-CoV-2 in South Africa. Nat Med 2021; 27:440-446. [PMID: 33531709 DOI: 10.1038/s41591-021-01255-3] [Citation(s) in RCA: 251] [Impact Index Per Article: 83.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 01/15/2021] [Indexed: 12/15/2022]
Abstract
The first severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in South Africa was identified on 5 March 2020, and by 26 March the country was in full lockdown (Oxford stringency index of 90)1. Despite the early response, by November 2020, over 785,000 people in South Africa were infected, which accounted for approximately 50% of all known African infections2. In this study, we analyzed 1,365 near whole genomes and report the identification of 16 new lineages of SARS-CoV-2 isolated between 6 March and 26 August 2020. Most of these lineages have unique mutations that have not been identified elsewhere. We also show that three lineages (B.1.1.54, B.1.1.56 and C.1) spread widely in South Africa during the first wave, comprising ~42% of all infections in the country at the time. The newly identified C lineage of SARS-CoV-2, C.1, which has 16 nucleotide mutations as compared with the original Wuhan sequence, including one amino acid change on the spike protein, D614G (ref. 3), was the most geographically widespread lineage in South Africa by the end of August 2020. An early South African-specific lineage, B.1.106, which was identified in April 2020 (ref. 4), became extinct after nosocomial outbreaks were controlled in KwaZulu-Natal Province. Our findings show that genomic surveillance can be implemented on a large scale in Africa to identify new lineages and inform measures to control the spread of SARS-CoV-2. Such genomic surveillance presented in this study has been shown to be crucial in the identification of the 501Y.V2 variant in South Africa in December 2020 (ref. 5).
Collapse
|
25
|
Harvey WT, Mulatti P, Fusaro A, Scolamacchia F, Zecchin B, Monne I, Marangon S. Spatiotemporal reconstruction and transmission dynamics during the 2016-17 H5N8 highly pathogenic avian influenza epidemic in Italy. Transbound Emerg Dis 2021; 68:37-50. [PMID: 31788978 PMCID: PMC8048528 DOI: 10.1111/tbed.13420] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 10/03/2019] [Accepted: 10/29/2019] [Indexed: 11/29/2022]
Abstract
Effective control of avian diseases in domestic populations requires understanding of the transmission dynamics facilitating viral emergence and spread. In 2016-17, Italy experienced a significant avian influenza epidemic caused by a highly pathogenic A(H5N8) virus, which affected domestic premises housing around 2.7 million birds, primarily in the north-eastern regions with the highest density of poultry farms (Lombardy, Emilia-Romagna and Veneto). We perform integrated analyses of genetic, spatiotemporal and host data within a Bayesian phylogenetic framework. Using continuous and discrete phylogeography, we estimate the locations of movements responsible for the spread and persistence of the epidemic. The information derived from these analyses on rates of transmission between regions through time can be used to assess the success of control measures. Using an approach based on phylogenetic-temporal distances between domestic cases, we infer the presence of cryptic wild bird-mediated transmission, information that can be used to complement existing epidemiological methods for distinguishing transmission within the domestic population from incursions across the wildlife-domestic interface, a common challenge in veterinary epidemiology. Spatiotemporal reconstruction of the epidemic reveals a highly skewed distribution of virus movements with a high proportion of shorter distance local movements interspersed with occasional long-distance dispersal events associated with wild birds. We also show how such inference be used to identify possible instances of human-mediated movements where distances between phylogenetically linked domestic cases are unusually high.
Collapse
Affiliation(s)
- William T. Harvey
- Boyd Orr Centre for Population and Ecosystem HealthInstitute of Biodiversity, Animal Health and Comparative MedicineCollege of Medical, Veterinary and Life SciencesUniversity of GlasgowGlasgowUK
| | - Paolo Mulatti
- Istituto Zooprofilattico Sperimentale delle VenezieLegnaro (Padua)Italy
| | - Alice Fusaro
- Istituto Zooprofilattico Sperimentale delle VenezieLegnaro (Padua)Italy
| | | | - Bianca Zecchin
- Istituto Zooprofilattico Sperimentale delle VenezieLegnaro (Padua)Italy
| | - Isabella Monne
- Istituto Zooprofilattico Sperimentale delle VenezieLegnaro (Padua)Italy
| | - Stefano Marangon
- Istituto Zooprofilattico Sperimentale delle VenezieLegnaro (Padua)Italy
| |
Collapse
|
26
|
Computational Evolutionary Biology. Adv Bioinformatics 2021. [DOI: 10.1007/978-981-33-6191-1_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
|
27
|
Kuno G. The Absence of Yellow Fever in Asia: History, Hypotheses, Vector Dispersal, Possibility of YF in Asia, and Other Enigmas. Viruses 2020; 12:E1349. [PMID: 33255615 PMCID: PMC7759908 DOI: 10.3390/v12121349] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 11/12/2020] [Accepted: 11/17/2020] [Indexed: 01/11/2023] Open
Abstract
Since the recent epidemics of yellow fever in Angola and Brazil as well as the importation of cases to China in 2016, there has been an increased interest in the century-old enigma, absence of yellow fever in Asia. Although this topic has been repeatedly reviewed before, the history of human intervention has never been considered a critical factor. A two-stage literature search online for this review, however, yielded a rich history indispensable for the debate over this medical enigma. As we combat the pandemic of COVID-19 coronavirus worldwide today, we can learn invaluable lessons from the historical events in Asia. In this review, I explore the history first and then critically examine in depth major hypotheses proposed in light of accumulated data, global dispersal of the principal vector, patterns of YF transmission, persistence of urban transmission, and the possibility of YF in Asia. Through this process of re-examination of the current knowledge, the subjects for research that should be conducted are identified. This review also reveals the importance of holistic approach incorporating ecological and human factors for many unresolved subjects, such as the enigma of YF absence in Asia, vector competence, vector dispersal, spillback, viral persistence and transmission mechanisms.
Collapse
Affiliation(s)
- Goro Kuno
- Centers for Disease Control and Prevention, Formerly Division of Vector-Borne Infectious Diseases, Fort Collins, CO 80521, USA
| |
Collapse
|
28
|
Fountain-Jones NM, Appaw RC, Carver S, Didelot X, Volz E, Charleston M. Emerging phylogenetic structure of the SARS-CoV-2 pandemic. Virus Evol 2020; 6:veaa082. [PMID: 33335743 PMCID: PMC7717445 DOI: 10.1093/ve/veaa082] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Since spilling over into humans, SARS-CoV-2 has rapidly spread across the globe, accumulating significant genetic diversity. The structure of this genetic diversity and whether it reveals epidemiological insights are fundamental questions for understanding the evolutionary trajectory of this virus. Here, we use a recently developed phylodynamic approach to uncover phylogenetic structures underlying the SARS-CoV-2 pandemic. We find support for three SARS-CoV-2 lineages co-circulating, each with significantly different demographic dynamics concordant with known epidemiological factors. For example, Lineage C emerged in Europe with a high growth rate in late February, just prior to the exponential increase in cases in several European countries. Non-synonymous mutations that characterize Lineage C occur in functionally important gene regions responsible for viral replication and cell entry. Even though Lineages A and B had distinct demographic patterns, they were much more difficult to distinguish. Continuous application of phylogenetic approaches to track the evolutionary epidemiology of SARS-CoV-2 lineages will be increasingly important to validate the efficacy of control efforts and monitor significant evolutionary events in the future.
Collapse
Affiliation(s)
| | - Raima Carol Appaw
- School of Natural Sciences, University of Tasmania, Hobart, 7001, Australia
| | - Scott Carver
- School of Natural Sciences, University of Tasmania, Hobart, 7001, Australia
| | - Xavier Didelot
- School of Life Sciences and Department of Statistics, University of Warwick, Coventry CV47AL, UK
| | - Erik Volz
- Department of Infectious Disease Epidemiology, MRC Centre for Global Infectious Disease Analysis, Imperial College London, London W2 1PG, UK
| | - Michael Charleston
- School of Natural Sciences, University of Tasmania, Hobart, 7001, Australia
| |
Collapse
|
29
|
Dellicour S, Lequime S, Vrancken B, Gill MS, Bastide P, Gangavarapu K, Matteson NL, Tan Y, du Plessis L, Fisher AA, Nelson MI, Gilbert M, Suchard MA, Andersen KG, Grubaugh ND, Pybus OG, Lemey P. Epidemiological hypothesis testing using a phylogeographic and phylodynamic framework. Nat Commun 2020; 11:5620. [PMID: 33159066 PMCID: PMC7648063 DOI: 10.1038/s41467-020-19122-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 09/30/2020] [Indexed: 01/05/2023] Open
Abstract
Computational analyses of pathogen genomes are increasingly used to unravel the dispersal history and transmission dynamics of epidemics. Here, we show how to go beyond historical reconstructions and use spatially-explicit phylogeographic and phylodynamic approaches to formally test epidemiological hypotheses. We illustrate our approach by focusing on the West Nile virus (WNV) spread in North America that has substantially impacted public, veterinary, and wildlife health. We apply an analytical workflow to a comprehensive WNV genome collection to test the impact of environmental factors on the dispersal of viral lineages and on viral population genetic diversity through time. We find that WNV lineages tend to disperse faster in areas with higher temperatures and we identify temporal variation in temperature as a main predictor of viral genetic diversity through time. By contrasting inference with simulation, we find no evidence for viral lineages to preferentially circulate within the same migratory bird flyway, suggesting a substantial role for non-migratory birds or mosquito dispersal along the longitudinal gradient.
Collapse
Affiliation(s)
- Simon Dellicour
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, CP160/12, 50 Avenue FD Roosevelt, 1050, Bruxelles, Belgium.
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Herestraat 49, 3000, Leuven, Belgium.
| | - Sebastian Lequime
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Herestraat 49, 3000, Leuven, Belgium
| | - Bram Vrancken
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Herestraat 49, 3000, Leuven, Belgium
| | - Mandev S Gill
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Herestraat 49, 3000, Leuven, Belgium
| | - Paul Bastide
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Herestraat 49, 3000, Leuven, Belgium
| | - Karthik Gangavarapu
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, 92037, USA
| | - Nathaniel L Matteson
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, 92037, USA
| | - Yi Tan
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Infectious Diseases Group, J. Craig Venter Institute, Rockville, MD, USA
| | | | - Alexander A Fisher
- Department of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Martha I Nelson
- Fogarty International Center, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Marius Gilbert
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, CP160/12, 50 Avenue FD Roosevelt, 1050, Bruxelles, Belgium
| | - Marc A Suchard
- Department of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Kristian G Andersen
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, 92037, USA
- Scripps Research Translational Institute, La Jolla, CA, 92037, USA
| | - Nathan D Grubaugh
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, 06510, USA
| | | | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Herestraat 49, 3000, Leuven, Belgium
| |
Collapse
|
30
|
Vaiente MA, Scotch M. Going back to the roots: Evaluating Bayesian phylogeographic models with discrete trait uncertainty. INFECTION, GENETICS AND EVOLUTION : JOURNAL OF MOLECULAR EPIDEMIOLOGY AND EVOLUTIONARY GENETICS IN INFECTIOUS DISEASES 2020; 85:104501. [PMID: 32798768 PMCID: PMC7686256 DOI: 10.1016/j.meegid.2020.104501] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 08/06/2020] [Accepted: 08/09/2020] [Indexed: 01/14/2023]
Abstract
Phylogeography is a popular way to analyze virus sequences annotated with discrete, epidemiologically-relevant, trait data. For applied public health surveillance, a key quantity of interest is often the state at the root of the inferred phylogeny. In epidemiological terms, this represents the geographic origin of the observed outbreak. Since determining the origin of an outbreak is often critical for public health intervention, it is prudent to understand how well phylogeographic models perform this root state classification task under various analytical scenarios. Specifically, we investigate how discrete state space and sequence data set influence the root state classification accuracy. We performed phylogeographic inference on several simulated DNA data sets while i) increasing the number of sequences and ii) increasing the total number of possible discrete trait values. We show that phylogeographic models tend to perform best at intermediate sequence data set sizes. Further, we demonstrate that a popular metric used for evaluation of phylogeographic models, the Kullback-Leibler (KL) divergence, both increases with discrete state space and data set sizes. Further, by modeling phylogeographic root state classification accuracy using logistic regression, we show that KL is not supported as a predictor of model accuracy, indicating its limited utility for assessing phylogeographic model performance on empirical data. These results suggest that relying solely on the KL metric may lead to artificially inflated support for models with finer discretization schemes and larger data set sizes. These results will be important for public health practitioners seeking to use phylogeographic models for applied infectious disease surveillance.
Collapse
Affiliation(s)
- Matteo A Vaiente
- Biodesign Center for Environmental Health Engineering, Arizona State University, 727 E. Tyler St, Tempe, AZ 85281, USA; College of Health Solutions, Arizona State University, 500 N 3rd St, Phoenix, AZ 85004, USA
| | - Matthew Scotch
- Biodesign Center for Environmental Health Engineering, Arizona State University, 727 E. Tyler St, Tempe, AZ 85281, USA; College of Health Solutions, Arizona State University, 500 N 3rd St, Phoenix, AZ 85004, USA.
| |
Collapse
|
31
|
Candido DS, Claro IM, de Jesus JG, Souza WM, Moreira FRR, Dellicour S, Mellan TA, du Plessis L, Pereira RHM, Sales FCS, Manuli ER, Thézé J, Almeida L, Menezes MT, Voloch CM, Fumagalli MJ, Coletti TM, da Silva CAM, Ramundo MS, Amorim MR, Hoeltgebaum HH, Mishra S, Gill MS, Carvalho LM, Buss LF, Prete CA, Ashworth J, Nakaya HI, Peixoto PS, Brady OJ, Nicholls SM, Tanuri A, Rossi ÁD, Braga CKV, Gerber AL, de C Guimarães AP, Gaburo N, Alencar CS, Ferreira ACS, Lima CX, Levi JE, Granato C, Ferreira GM, Francisco RS, Granja F, Garcia MT, Moretti ML, Perroud MW, Castiñeiras TMPP, Lazari CS, Hill SC, de Souza Santos AA, Simeoni CL, Forato J, Sposito AC, Schreiber AZ, Santos MNN, de Sá CZ, Souza RP, Resende-Moreira LC, Teixeira MM, Hubner J, Leme PAF, Moreira RG, Nogueira ML, Ferguson NM, Costa SF, Proenca-Modena JL, Vasconcelos ATR, Bhatt S, Lemey P, Wu CH, Rambaut A, Loman NJ, Aguiar RS, Pybus OG, Sabino EC, Faria NR. Evolution and epidemic spread of SARS-CoV-2 in Brazil. Science 2020; 369:1255-1260. [PMID: 32703910 PMCID: PMC7402630 DOI: 10.1126/science.abd2161] [Citation(s) in RCA: 335] [Impact Index Per Article: 83.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 07/16/2020] [Indexed: 12/16/2022]
Abstract
Brazil currently has one of the fastest-growing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemics in the world. Because of limited available data, assessments of the impact of nonpharmaceutical interventions (NPIs) on this virus spread remain challenging. Using a mobility-driven transmission model, we show that NPIs reduced the reproduction number from >3 to 1 to 1.6 in São Paulo and Rio de Janeiro. Sequencing of 427 new genomes and analysis of a geographically representative genomic dataset identified >100 international virus introductions in Brazil. We estimate that most (76%) of the Brazilian strains fell in three clades that were introduced from Europe between 22 February and 11 March 2020. During the early epidemic phase, we found that SARS-CoV-2 spread mostly locally and within state borders. After this period, despite sharp decreases in air travel, we estimated multiple exportations from large urban centers that coincided with a 25% increase in average traveled distances in national flights. This study sheds new light on the epidemic transmission and evolutionary trajectories of SARS-CoV-2 lineages in Brazil and provides evidence that current interventions remain insufficient to keep virus transmission under control in this country.
Collapse
Affiliation(s)
- Darlan S Candido
- Department of Zoology, University of Oxford, Oxford, UK
- Instituto de Medicina Tropical, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Ingra M Claro
- Instituto de Medicina Tropical, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
- Departamento de Moléstias Infecciosas e Parasitárias, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Jaqueline G de Jesus
- Instituto de Medicina Tropical, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
- Departamento de Moléstias Infecciosas e Parasitárias, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - William M Souza
- Centro de Pesquisa em Virologia, Faculdade de Medicina de Ribeirão Preto, Ribeirão Preto, Brazil
| | - Filipe R R Moreira
- Departamento de Genética, Instituto de Biologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Simon Dellicour
- Spatial Epidemiology Lab, Université Libre de Bruxelles, Brussels, Belgium
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Thomas A Mellan
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Imperial College London, London, UK
| | | | | | - Flavia C S Sales
- Instituto de Medicina Tropical, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
- Departamento de Moléstias Infecciosas e Parasitárias, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Erika R Manuli
- Instituto de Medicina Tropical, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
- Departamento de Moléstias Infecciosas e Parasitárias, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Julien Thézé
- Université Clermont Auvergne, INRAE, VetAgro Sup, UMR EPIA, Saint-Genès-Champanelle, France
| | - Luiz Almeida
- Laboratório de Bioinformática, Laboratório Nacional de Computação Científica, Petrópolis, Brazil
| | - Mariane T Menezes
- Departamento de Genética, Instituto de Biologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Carolina M Voloch
- Departamento de Genética, Instituto de Biologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Marcilio J Fumagalli
- Centro de Pesquisa em Virologia, Faculdade de Medicina de Ribeirão Preto, Ribeirão Preto, Brazil
| | - Thaís M Coletti
- Instituto de Medicina Tropical, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
- Departamento de Moléstias Infecciosas e Parasitárias, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Camila A M da Silva
- Instituto de Medicina Tropical, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
- Departamento de Moléstias Infecciosas e Parasitárias, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Mariana S Ramundo
- Instituto de Medicina Tropical, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
- Departamento de Moléstias Infecciosas e Parasitárias, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Mariene R Amorim
- Departamento de Genética, Evolução, Microbiologia e Imunologia, Instituto de Biologia, Universidade Estadual de Campinas, Campinas, Brazil
| | | | - Swapnil Mishra
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Imperial College London, London, UK
| | - Mandev S Gill
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Luiz M Carvalho
- Escola de Matemática Aplicada (EMAp), Fundação Getúlio Vargas, Rio de Janeiro, Brazil
| | - Lewis F Buss
- Instituto de Medicina Tropical, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Carlos A Prete
- Department of Electronic Systems Engineering, University of São Paulo, São Paulo, Brazil
| | | | - Helder I Nakaya
- Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil
| | - Pedro S Peixoto
- Departamento de Matemática Aplicada, Instituto de Matemática e Estatística, Universidade de São Paulo, São Paulo, Brazil
| | - Oliver J Brady
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Samuel M Nicholls
- Institute for Microbiology and Infection, University of Birmingham, Birmingham, UK
| | - Amilcar Tanuri
- Departamento de Genética, Instituto de Biologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Átila D Rossi
- Departamento de Genética, Instituto de Biologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | | | - Alexandra L Gerber
- Laboratório de Bioinformática, Laboratório Nacional de Computação Científica, Petrópolis, Brazil
| | - Ana Paula de C Guimarães
- Laboratório de Bioinformática, Laboratório Nacional de Computação Científica, Petrópolis, Brazil
| | | | - Cecila Salete Alencar
- LIM 03 Laboratório de Medicina Laboratorial, Hospital das Clínicas Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | | | - Cristiano X Lima
- Departamento de Cirurgia, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Simile Instituto de Imunologia Aplicada Ltda, Belo Horizonte, Brazil
| | | | | | - Giulia M Ferreira
- Laboratório de Virologia, Instituto de Ciências Biomédicas, Universidade Federal de Uberlândia, Uberlândia, Brazil
| | - Ronaldo S Francisco
- Laboratório de Bioinformática, Laboratório Nacional de Computação Científica, Petrópolis, Brazil
| | - Fabiana Granja
- Departamento de Genética, Evolução, Microbiologia e Imunologia, Instituto de Biologia, Universidade Estadual de Campinas, Campinas, Brazil
- Centro de Estudos da Biodiversidade, Universidade Federal de Roraima, Boa Vista, Brazil
| | - Marcia T Garcia
- Divisão de Doenças Infecciosas, Faculdade de Ciências Médicas, Universidade Estadual de Campinas, Campinas, Brazil
| | - Maria Luiza Moretti
- Divisão de Doenças Infecciosas, Faculdade de Ciências Médicas, Universidade Estadual de Campinas, Campinas, Brazil
| | - Mauricio W Perroud
- Hospital Estadual Sumaré, Universidade Estadual de Campinas, Campinas, Brazil
| | - Terezinha M P P Castiñeiras
- Departamento de Doenças Infecciosas e Parasitárias, Faculdade de Medicina, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Carolina S Lazari
- Divisão de Laboratório Central do Hospital das Clínicas, da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Sarah C Hill
- Department of Zoology, University of Oxford, Oxford, UK
- Department of Pathobiology and Population Sciences, Royal Veterinary College, Hatfield, UK
| | | | - Camila L Simeoni
- Departamento de Genética, Evolução, Microbiologia e Imunologia, Instituto de Biologia, Universidade Estadual de Campinas, Campinas, Brazil
| | - Julia Forato
- Departamento de Genética, Evolução, Microbiologia e Imunologia, Instituto de Biologia, Universidade Estadual de Campinas, Campinas, Brazil
| | - Andrei C Sposito
- Departamento de Clínica Médica, Faculdade de Ciências Médicas, Universidade Estadual de Campinas, Campinas, Brazil
| | - Angelica Z Schreiber
- Departamento de Patologia Clínica, Faculdade de Ciências Médicas, Universidade Estadual de Campinas, Campinas, Brazil
| | - Magnun N N Santos
- Departamento de Patologia Clínica, Faculdade de Ciências Médicas, Universidade Estadual de Campinas, Campinas, Brazil
| | - Camila Zolini de Sá
- Departamento de Genética, Ecologia e Evolução, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Renan P Souza
- Departamento de Genética, Ecologia e Evolução, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Luciana C Resende-Moreira
- Departamento de Botânica, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Mauro M Teixeira
- Departamento de Bioquímica e Imunologia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Josy Hubner
- Departamento de Biologia Celular, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Patricia A F Leme
- Centro de Saúde da Comunidade, Universidade Estadual de Campinas, Campinas, Brazil
| | - Rennan G Moreira
- Centro de Laboratórios Multiusuários, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Maurício L Nogueira
- Laboratório de Pesquisas em Virologia, Faculdade de Medicina de São José do Rio Preto, São José do Rio Preto, São Paulo, Brazil
| | - Neil M Ferguson
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Imperial College London, London, UK
| | - Silvia F Costa
- Instituto de Medicina Tropical, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
- Departamento de Moléstias Infecciosas e Parasitárias, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - José Luiz Proenca-Modena
- Departamento de Genética, Evolução, Microbiologia e Imunologia, Instituto de Biologia, Universidade Estadual de Campinas, Campinas, Brazil
| | - Ana Tereza R Vasconcelos
- Laboratório de Bioinformática, Laboratório Nacional de Computação Científica, Petrópolis, Brazil
| | - Samir Bhatt
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Imperial College London, London, UK
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Chieh-Hsi Wu
- Mathematical Sciences, University of Southampton, Southampton, UK
| | - Andrew Rambaut
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Nick J Loman
- Institute for Microbiology and Infection, University of Birmingham, Birmingham, UK
| | - Renato S Aguiar
- Departamento de Genética, Ecologia e Evolução, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | - Ester C Sabino
- Instituto de Medicina Tropical, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil.
- Departamento de Moléstias Infecciosas e Parasitárias, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Nuno Rodrigues Faria
- Department of Zoology, University of Oxford, Oxford, UK.
- Instituto de Medicina Tropical, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Imperial College London, London, UK
| |
Collapse
|
32
|
Lequime S, Bastide P, Dellicour S, Lemey P, Baele G. nosoi: A stochastic agent-based transmission chain simulation framework in r. Methods Ecol Evol 2020; 11:1002-1007. [PMID: 32983401 PMCID: PMC7496779 DOI: 10.1111/2041-210x.13422] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 05/13/2020] [Indexed: 12/22/2022]
Abstract
The transmission process of an infectious agent creates a connected chain of hosts linked by transmission events, known as a transmission chain. Reconstructing transmission chains remains a challenging endeavour, except in rare cases characterized by intense surveillance and epidemiological inquiry. Inference frameworks attempt to estimate or approximate these transmission chains but the accuracy and validity of such methods generally lack formal assessment on datasets for which the actual transmission chain was observed.We here introduce nosoi, an open-source r package that offers a complete, tunable and expandable agent-based framework to simulate transmission chains under a wide range of epidemiological scenarios for single-host and dual-host epidemics. nosoi is accessible through GitHub and CRAN, and is accompanied by extensive documentation, providing help and practical examples to assist users in setting up their own simulations.Once infected, each host or agent can undergo a series of events during each time step, such as moving (between locations) or transmitting the infection, all of these being driven by user-specified rules or data, such as travel patterns between locations. nosoi is able to generate a multitude of epidemic scenarios, that can-for example-be used to validate a wide range of reconstruction methods, including epidemic modelling and phylodynamic analyses. nosoi also offers a comprehensive framework to leverage empirically acquired data, allowing the user to explore how variations in parameters can affect epidemic potential. Aside from research questions, nosoi can provide lecturers with a complete teaching tool to offer students a hands-on exploration of the dynamics of epidemiological processes and the factors that impact it. Because the package does not rely on mathematical formalism but uses a more intuitive algorithmic approach, even extensive changes of the entire model can be easily and quickly implemented.
Collapse
Affiliation(s)
- Sebastian Lequime
- Department of Microbiology, Immunology and TransplantationRega InstituteKU LeuvenLeuvenBelgium
- Cluster of Microbial EcologyGroningen Institute for Evolutionary Life SciencesUniversity of GroningenGroningenThe Netherlands
| | - Paul Bastide
- Department of Microbiology, Immunology and TransplantationRega InstituteKU LeuvenLeuvenBelgium
- IMAGCNRSUniversity of MontpellierMontpellierFrance
| | - Simon Dellicour
- Department of Microbiology, Immunology and TransplantationRega InstituteKU LeuvenLeuvenBelgium
- Spatial Epidemiology Lab (SpELL)Université Libre de BruxellesBrusselsBelgium
| | - Philippe Lemey
- Department of Microbiology, Immunology and TransplantationRega InstituteKU LeuvenLeuvenBelgium
| | - Guy Baele
- Department of Microbiology, Immunology and TransplantationRega InstituteKU LeuvenLeuvenBelgium
| |
Collapse
|
33
|
Abstract
MOTIVATION The combination of genomic and epidemiological data holds the potential to enable accurate pathogen transmission history inference. However, the inference of outbreak transmission histories remains challenging due to various factors such as within-host pathogen diversity and multi-strain infections. Current computational methods ignore within-host diversity and/or multi-strain infections, often failing to accurately infer the transmission history. Thus, there is a need for efficient computational methods for transmission tree inference that accommodate the complexities of real data. RESULTS We formulate the direct transmission inference (DTI) problem for inferring transmission trees that support multi-strain infections given a timed phylogeny and additional epidemiological data. We establish hardness for the decision and counting version of the DTI problem. We introduce Transmission Tree Uniform Sampler (TiTUS), a method that uses SATISFIABILITY to almost uniformly sample from the space of transmission trees. We introduce criteria that prioritize parsimonious transmission trees that we subsequently summarize using a novel consensus tree approach. We demonstrate TiTUS's ability to accurately reconstruct transmission trees on simulated data as well as a documented HIV transmission chain. AVAILABILITY AND IMPLEMENTATION https://github.com/elkebir-group/TiTUS. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Palash Sashittal
- Department of Aerospace Engineering, University of Illinois at Urbana-Champaign, Urbama, IL 61801, USA
| | - Mohammed El-Kebir
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbama, IL 61801, USA
| |
Collapse
|
34
|
Bondaryuk AN, Sidorova EA, Adelshin RV, Andaev EI, Balakhonov SV. Reporting of New tick-borne encephalitis virus strains isolated in Eastern Siberia (Russia) in 1960-2011 and explaining them in an evolutionary context using Bayesian phylogenetic inference. Ticks Tick Borne Dis 2020; 11:101496. [PMID: 32723652 DOI: 10.1016/j.ttbdis.2020.101496] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2019] [Revised: 06/03/2020] [Accepted: 06/12/2020] [Indexed: 12/11/2022]
Abstract
Tick-borne encephalitis virus (TBEV) is one of the main tick-borne viral pathogens of humans. Infection may induce signs of meningitis, encephalitis, paralysis and high fever. TBEV is well studied by molecular phylogenetic methods. The present-day implementation of Bayesian phylogenetic models allows population dynamics to be tracked, providing changes in population size that were not directly observed. However, the description of the past population dynamics of TBEV is rare in the literature. In our investigation, we provide data on the dynamics of viral genetic diversity of TBEV in Zabaikalsky Krai (Eastern Siberia, Russia) revealed by the Bayesian coalescent inference in a BEAST program. As a data set, we used the envelope (E) protein partial gene sequences (1308 nt) of 38 TBEV strains (including six "886-84-like" or Baikalian subtype strains (TBEV-B)), isolated in Zabaikalsky Krai (Eastern Siberia, Russia) in 1960-1963 and 1995-2011. To increase estimations reliability, we compared 9 model combinations by Path sampling and Stepping-stone sampling methods. It has been shown that the genetic diversity decline in the population history of TBEV in the 1950s coincides with the date of the beginning of wide dichlorodiphenyltrichloroethane forest dusting in Siberia. We assumed that the TBEV population on the territory of Siberia went through a genetic bottleneck. Also, we provide data estimating the divergence time of TBEV-B strains and indicate the specific evolution rate of an ancestor lineage of the Baikalian subtype, illustrated on a phylogenetic tree, and reconstructed under a relaxed clock model.
Collapse
Affiliation(s)
- Artem N Bondaryuk
- Irkutsk Antiplague Research Institute of Siberia and Far East, Trilisser 78, 664047, Irkutsk, Russia.
| | - Elena A Sidorova
- Irkutsk Antiplague Research Institute of Siberia and Far East, Trilisser 78, 664047, Irkutsk, Russia.
| | - Renat V Adelshin
- Irkutsk Antiplague Research Institute of Siberia and Far East, Trilisser 78, 664047, Irkutsk, Russia; Irkutsk State University, Irkutsk, Russia.
| | - Evgeny I Andaev
- Irkutsk Antiplague Research Institute of Siberia and Far East, Trilisser 78, 664047, Irkutsk, Russia.
| | - Sergey V Balakhonov
- Irkutsk Antiplague Research Institute of Siberia and Far East, Trilisser 78, 664047, Irkutsk, Russia.
| |
Collapse
|
35
|
Gill MS, Lemey P, Suchard MA, Rambaut A, Baele G. Online Bayesian Phylodynamic Inference in BEAST with Application to Epidemic Reconstruction. Mol Biol Evol 2020; 37:1832-1842. [PMID: 32101295 PMCID: PMC7253210 DOI: 10.1093/molbev/msaa047] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Reconstructing pathogen dynamics from genetic data as they become available during an outbreak or epidemic represents an important statistical scenario in which observations arrive sequentially in time and one is interested in performing inference in an "online" fashion. Widely used Bayesian phylogenetic inference packages are not set up for this purpose, generally requiring one to recompute trees and evolutionary model parameters de novo when new data arrive. To accommodate increasing data flow in a Bayesian phylogenetic framework, we introduce a methodology to efficiently update the posterior distribution with newly available genetic data. Our procedure is implemented in the BEAST 1.10 software package, and relies on a distance-based measure to insert new taxa into the current estimate of the phylogeny and imputes plausible values for new model parameters to accommodate growing dimensionality. This augmentation creates informed starting values and re-uses optimally tuned transition kernels for posterior exploration of growing data sets, reducing the time necessary to converge to target posterior distributions. We apply our framework to data from the recent West African Ebola virus epidemic and demonstrate a considerable reduction in time required to obtain posterior estimates at different time points of the outbreak. Beyond epidemic monitoring, this framework easily finds other applications within the phylogenetics community, where changes in the data-in terms of alignment changes, sequence addition or removal-present common scenarios that can benefit from online inference.
Collapse
Affiliation(s)
- Mandev S Gill
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Marc A Suchard
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA
- Department of Biostatistics, School of Public Health, University of California, Los Angeles, CA
- Department of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, CA
| | - Andrew Rambaut
- Institute of Evolutionary Biology, University of Edinburgh, United Kingdom
- Fogarty International Center, National Institutes of Health, Bethesda, MD
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| |
Collapse
|
36
|
Libin PJK, Deforche K, Abecasis AB, Theys K. VIRULIGN: fast codon-correct alignment and annotation of viral genomes. Bioinformatics 2020; 35:1763-1765. [PMID: 30295730 PMCID: PMC6513156 DOI: 10.1093/bioinformatics/bty851] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Revised: 09/24/2018] [Accepted: 10/05/2018] [Indexed: 12/11/2022] Open
Abstract
Summary Virus sequence data are an essential resource for reconstructing spatiotemporal dynamics of viral spread as well as to inform treatment and prevention strategies. However, the potential benefit of these applications critically depends on accurate and correctly annotated alignments of genetically heterogeneous data. VIRULIGN was built for fast codon-correct alignments of large datasets, with standardized and formalized genome annotation and various alignment export formats. Availability and implementation VIRULIGN is freely available at https://github.com/rega-cev/virulign as an open source software project. Supplementary information Supplementary data is available at Bioinformatics online.
Collapse
Affiliation(s)
- Pieter J K Libin
- KU Leuven, Rega Institute for Medical, Laboratorium of Clinical and Evolutionary Virology, Leuven, Belgium.,Artificial Intelligence Lab, Department of Computer Science, Vrije Universiteit Brussel, Brussels, Belgium
| | | | - Ana B Abecasis
- Center for Global Health and Tropical Medicine, Institute for Hygiene and Tropical Medicine, Lisboa, Portugal
| | - Kristof Theys
- KU Leuven, Rega Institute for Medical, Laboratorium of Clinical and Evolutionary Virology, Leuven, Belgium
| |
Collapse
|
37
|
Vrancken B, Wawina-Bokalanga T, Vanmechelen B, Martí-Carreras J, Carroll MW, Nsio J, Kapetshi J, Makiala-Mandanda S, Muyembe-Tamfum JJ, Baele G, Vermeire K, Vergote V, Ahuka-Mundeke S, Maes P. Accounting for population structure reveals ambiguity in the Zaire Ebolavirus reservoir dynamics. PLoS Negl Trop Dis 2020; 14:e0008117. [PMID: 32130210 PMCID: PMC7075637 DOI: 10.1371/journal.pntd.0008117] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Revised: 03/16/2020] [Accepted: 02/05/2020] [Indexed: 11/18/2022] Open
Abstract
Ebolaviruses pose a substantial threat to wildlife populations and to public health in Africa. Evolutionary analyses of virus genome sequences can contribute significantly to elucidate the origin of new outbreaks, which can help guide surveillance efforts. The reconstructed between-outbreak evolutionary history of Zaire ebolavirus so far has been highly consistent. By removing the confounding impact of population growth bursts during local outbreaks on the free mixing assumption that underlies coalescent-based demographic reconstructions, we find-contrary to what previous results indicated-that the circulation dynamics of Ebola virus in its animal reservoir are highly uncertain. Our findings also accentuate the need for a more fine-grained picture of the Ebola virus diversity in its reservoir to reliably infer the reservoir origin of outbreak lineages. In addition, the recent appearance of slower-evolving variants is in line with latency as a survival mechanism and with bats as the natural reservoir host.
Collapse
Affiliation(s)
- Bram Vrancken
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Division of Clinical and Epidemiological Virology, Leuven, Belgium
| | - Tony Wawina-Bokalanga
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Division of Clinical and Epidemiological Virology, Leuven, Belgium
| | - Bert Vanmechelen
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Division of Clinical and Epidemiological Virology, Leuven, Belgium
| | - Joan Martí-Carreras
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Division of Clinical and Epidemiological Virology, Leuven, Belgium
| | - Miles W. Carroll
- Research and Development Institute, National Infection Service, Public Health England, Porton Down, Wiltshire, United Kingdom
| | - Justus Nsio
- Ministère de la Santé, Kinshasa, Democratic Republic of the Congo
| | - Jimmy Kapetshi
- Institut National de Recherche Biomédicale (INRB), Kinshasa, Democratic Republic of the Congo
| | - Sheila Makiala-Mandanda
- Institut National de Recherche Biomédicale (INRB), Kinshasa, Democratic Republic of the Congo
| | | | - Guy Baele
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Division of Clinical and Epidemiological Virology, Leuven, Belgium
| | - Kurt Vermeire
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Virology and Chemotherapy, Leuven, Belgium
| | - Valentijn Vergote
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Division of Clinical and Epidemiological Virology, Leuven, Belgium
| | - Steve Ahuka-Mundeke
- Institut National de Recherche Biomédicale (INRB), Kinshasa, Democratic Republic of the Congo
| | - Piet Maes
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Division of Clinical and Epidemiological Virology, Leuven, Belgium
| |
Collapse
|
38
|
Hostager R, Ragonnet-Cronin M, Murrell B, Hedskog C, Osinusi A, Susser S, Sarrazin C, Svarovskaia E, Wertheim JO. Hepatitis C virus genotype 1 and 2 recombinant genomes and the phylogeographic history of the 2k/1b lineage. Virus Evol 2019; 5:vez041. [PMID: 31616569 PMCID: PMC6785677 DOI: 10.1093/ve/vez041] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Recombination is an important driver of genetic diversity, though it is relatively uncommon in hepatitis C virus (HCV). Recent investigation of sequence data acquired from HCV clinical trials produced twenty-one full-genome recombinant viruses belonging to three putative inter-subtype forms 2b/1a, 2b/1b, and 2k/1b. The 2k/1b chimera is the only known HCV circulating recombinant form (CRF), provoking interest in its genetic structure and origin. Discovered in Russia in 1999, 2k/1b cases have since been detected throughout the former Soviet Union, Western Europe, and North America. Although 2k/1b prevalence is highest in the Caucasus mountain region (i.e., Armenia, Azerbaijan, and Georgia), the origin and migration patterns of CRF 2k/1b have remained obscure due to a paucity of available sequences. We assembled an alignment which spans the entire coding region of the HCV genome containing all available 2k/1b sequences (>500 nucleotides; n = 109) sampled in ninteen countries from public databases (102 individuals), additional newly sequenced genomic regions (from 48 of these 102 individuals), unpublished isolates with newly sequenced regions (5 additional individuals), and novel complete genomes (2 additional individuals) generated in this study. Analysis of this expanded dataset reconfirmed the monophyletic origin of 2k/1b with a recombination breakpoint at position 3,187 (95% confidence interval: 3,172–3,202; HCV GT1a reference strain H77). Phylogeography is a valuable tool used to reveal viral migration dynamics. Inference of the timed history of spread in a Bayesian framework identified Russia as the ancestral source of the CRF 2k/1b clade. Further, we found evidence for migration routes leading out of Russia to other former Soviet Republics or countries under the Soviet sphere of influence. These findings suggest an interplay between geopolitics and the historical spread of CRF 2k/1b.
Collapse
Affiliation(s)
- Reilly Hostager
- Department of Medicine, University of California, San Diego, CA, USA
| | | | - Ben Murrell
- Department of Medicine, University of California, San Diego, CA, USA
| | | | | | - Simone Susser
- Goethe-University Hospital, Medical Clinic, Frankfurt, Germany
| | - Christoph Sarrazin
- Gilead Sciences, Foster City, CA, USA.,St. Josefs-Hospital, Medical Clinic 2, Wiesbaden, Germany
| | | | - Joel O Wertheim
- Department of Medicine, University of California, San Diego, CA, USA
| |
Collapse
|
39
|
Hadfield J, Brito AF, Swetnam DM, Vogels CBF, Tokarz RE, Andersen KG, Smith RC, Bedford T, Grubaugh ND. Twenty years of West Nile virus spread and evolution in the Americas visualized by Nextstrain. PLoS Pathog 2019; 15:e1008042. [PMID: 31671157 PMCID: PMC6822705 DOI: 10.1371/journal.ppat.1008042] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
It has been 20 years since West Nile virus first emerged in the Americas, and since then, little progress has been made to control outbreaks caused by this virus. After its first detection in New York in 1999, West Nile virus quickly spread across the continent, causing an epidemic of human disease and massive bird die-offs. Now the virus has become endemic to the United States, where an estimated 7 million human infections have occurred, making it the leading mosquito-borne virus infection and the most common cause of viral encephalitis in the country. To bring new attention to one of the most important mosquito-borne viruses in the Americas, we provide an interactive review using Nextstrain: a visualization tool for real-time tracking of pathogen evolution (nextstrain.org/WNV/NA). Nextstrain utilizes a growing database of more than 2,000 West Nile virus genomes and harnesses the power of phylogenetics for students, educators, public health workers, and researchers to visualize key aspects of virus spread and evolution. Using Nextstrain, we use virus genomics to investigate the emergence of West Nile virus in the U S, followed by its rapid spread, evolution in a new environment, establishment of endemic transmission, and subsequent international spread. For each figure, we include a link to Nextstrain to allow the readers to directly interact with and explore the underlying data in new ways. We also provide a brief online narrative that parallels this review to further explain the data and highlight key epidemiological and evolutionary features (nextstrain.org/narratives/twenty-years-of-WNV). Mirroring the dynamic nature of outbreaks, the Nextstrain links provided within this paper are constantly updated as new West Nile virus genomes are shared publicly, helping to stay current with the research. Overall, our review showcases how genomics can track West Nile virus spread and evolution, as well as potentially uncover novel targeted control measures to help alleviate its public health burden.
Collapse
Affiliation(s)
- James Hadfield
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Anderson F. Brito
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Daniele M. Swetnam
- Department of Pathology, Microbiology and Immunology, University of California, Davis, Davis, California, United States of America
| | - Chantal B. F. Vogels
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Ryan E. Tokarz
- Department of Entomology, Iowa State University, Ames, Iowa, United States of America
| | - Kristian G. Andersen
- Department of Immunology and Microbiology, Scripps Research, La Jolla, California, United States of America
- Scripps Research Translational Institute, La Jolla, California, United States of America
| | - Ryan C. Smith
- Department of Entomology, Iowa State University, Ames, Iowa, United States of America
| | - Trevor Bedford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Nathan D. Grubaugh
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America
| |
Collapse
|
40
|
Dellicour S, Troupin C, Jahanbakhsh F, Salama A, Massoudi S, Moghaddam MK, Baele G, Lemey P, Gholami A, Bourhy H. Using phylogeographic approaches to analyse the dispersal history, velocity and direction of viral lineages - Application to rabies virus spread in Iran. Mol Ecol 2019; 28:4335-4350. [PMID: 31535448 DOI: 10.1111/mec.15222] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Revised: 08/04/2019] [Accepted: 08/05/2019] [Indexed: 12/26/2022]
Abstract
Recent years have seen the extensive use of phylogeographic approaches to unveil the dispersal history of virus epidemics. Spatially explicit reconstructions of viral spread represent valuable sources of lineage movement data that can be exploited to investigate the impact of underlying environmental layers on the dispersal of pathogens. Here, we performed phylogeographic inference and applied different post hoc approaches to analyse a new and comprehensive data set of viral genomes to elucidate the dispersal history and dynamics of rabies virus (RABV) in Iran, which have remained largely unknown. We first analysed the association between environmental factors and variations in dispersal velocity among lineages. Second, we present, test and apply a new approach to study the link between environmental conditions and the dispersal direction of lineages. The statistical performance (power of detection, false-positive rate) of this new method was assessed using simulations. We performed phylogeographic analyses of RABV genomes, allowing us to describe the large diversity of RABV in Iran and to confirm the cocirculation of several clades in the country. Overall, we estimate a relatively high lineage dispersal velocity, similar to previous estimates for dog rabies virus spread in northern Africa. Finally, we highlight a tendency for RABV lineages to spread in accessible areas associated with high human population density. Our analytical workflow illustrates how phylogeographic approaches can be used to investigate the impact of environmental factors on several aspects of viral dispersal dynamics.
Collapse
Affiliation(s)
- Simon Dellicour
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory for Clinical and Epidemiological Virology, KU Leuven, Leuven, Belgium.,Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Bruxelles, Belgium
| | - Cécile Troupin
- Unit Lyssavirus Epidemiology and Neuropathology, WHO Collaborating Centre for Reference and Research on Rabies, Institut Pasteur, Paris, France
| | - Fatemeh Jahanbakhsh
- WHO Collaborating Centre for Reference and Research on Rabies, Pasteur Institute of Iran, Tehran, Iran
| | - Akram Salama
- Department of Medicine and Infectious Diseases, Faculty of Veterinary Medicine, University of Sadat City, Sadat City, Egypt
| | - Siamak Massoudi
- Department of Environment, Wildlife Diseases Group, Wildlife Bureau, Tehran, Iran
| | - Madjid K Moghaddam
- Department of Environment, Wildlife Diseases Group, Wildlife Bureau, Tehran, Iran
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory for Clinical and Epidemiological Virology, KU Leuven, Leuven, Belgium
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory for Clinical and Epidemiological Virology, KU Leuven, Leuven, Belgium
| | - Alireza Gholami
- WHO Collaborating Centre for Reference and Research on Rabies, Pasteur Institute of Iran, Tehran, Iran
| | - Hervé Bourhy
- Unit Lyssavirus Epidemiology and Neuropathology, WHO Collaborating Centre for Reference and Research on Rabies, Institut Pasteur, Paris, France
| |
Collapse
|
41
|
Munsey A, Mwiine FN, Ochwo S, Velazquez-Salinas L, Ahmed Z, Maree F, Rodriguez LL, Rieder E, Perez A, VanderWaal K. Spatial distribution and risk factors for foot and mouth disease virus in Uganda: Opportunities for strategic surveillance. Prev Vet Med 2019; 171:104766. [PMID: 31541845 DOI: 10.1016/j.prevetmed.2019.104766] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 07/23/2019] [Accepted: 08/31/2019] [Indexed: 12/22/2022]
Abstract
Foot-and-mouth disease virus (FMDV) has a substantial impact on cattle populations in Uganda, causing short- and long-term production losses and hampering local and international trade. Although FMDV has persisted in Uganda for at least 60 years, its epidemiology there and in other endemic settings remains poorly understood. Here, we utilized a large-scale cross-sectional study of cattle to elucidate the dynamics of FMDV spread in Uganda. Sera samples (n = 14,439) from 211 herds were analyzed for non-structural protein reactivity, an indication of past FMDV exposure. Serological results were used to determine spatial patterns, and a Bayesian multivariable logistic regression mixed model was used to identify risk factors for FMDV infection. Spatial clustering of the disease was evident, with higher risk demonstrated near international borders. Additionally, high cattle density, low annual rainfall, and pastoralism were associated with increased likelihood of FMD seropositivity. These results provide insights into the complex epidemiology of FMDV in Uganda and will help inform refined control strategies in Uganda and other FMDV-endemic settings.
Collapse
Affiliation(s)
- Anna Munsey
- University of Minnesota College of Veterinary Medicine, 222 Veterinary Medical Center, 1365 Gortner Avenue, St. Paul, Minnesota, 55418, USA
| | - Frank Norbert Mwiine
- Makerere University College of Veterinary Medicine, Animal Resources and Biosecurity (COVAB), Makerere Hill Road, P.O. Box 7062, Kampala, Uganda
| | - Sylvester Ochwo
- Makerere University College of Veterinary Medicine, Animal Resources and Biosecurity (COVAB), Makerere Hill Road, P.O. Box 7062, Kampala, Uganda
| | - Lauro Velazquez-Salinas
- Agricultural Research Service (ARS), Foreign Animal Disease Research Unit, United States Department of Agriculture, Plum Island Animal Disease Center, P.O. Box 848, Greenport, NY, 11948, USA
| | - Zaheer Ahmed
- Animal and Plant Health Inspection Services (APHIS), National Veterinary Services Laboratories, Foreign Animal Disease Diagnostic Lab, United States Department of Agriculture, Plum Island Animal Disease Center, P.O. Box 848, Greenport, NY, 11948, USA
| | - Francois Maree
- Onderstepoort Veterinary Institute, 100 Soutpan Road, Pretoria, 0002, South Africa
| | - Luis L Rodriguez
- Agricultural Research Service (ARS), Foreign Animal Disease Research Unit, United States Department of Agriculture, Plum Island Animal Disease Center, P.O. Box 848, Greenport, NY, 11948, USA
| | - Elizabeth Rieder
- Agricultural Research Service (ARS), Foreign Animal Disease Research Unit, United States Department of Agriculture, Plum Island Animal Disease Center, P.O. Box 848, Greenport, NY, 11948, USA
| | - Andres Perez
- University of Minnesota College of Veterinary Medicine, 222 Veterinary Medical Center, 1365 Gortner Avenue, St. Paul, Minnesota, 55418, USA
| | - Kimberly VanderWaal
- University of Minnesota College of Veterinary Medicine, 222 Veterinary Medical Center, 1365 Gortner Avenue, St. Paul, Minnesota, 55418, USA.
| |
Collapse
|
42
|
Theys K, Lemey P, Vandamme AM, Baele G. Advances in Visualization Tools for Phylogenomic and Phylodynamic Studies of Viral Diseases. Front Public Health 2019; 7:208. [PMID: 31428595 PMCID: PMC6688121 DOI: 10.3389/fpubh.2019.00208] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2019] [Accepted: 07/12/2019] [Indexed: 01/28/2023] Open
Abstract
Genomic and epidemiological monitoring have become an integral part of our response to emerging and ongoing epidemics of viral infectious diseases. Advances in high-throughput sequencing, including portable genomic sequencing at reduced costs and turnaround time, are paralleled by continuing developments in methodology to infer evolutionary histories (dynamics/patterns) and to identify factors driving viral spread in space and time. The traditionally static nature of visualizing phylogenetic trees that represent these evolutionary relationships/processes has also evolved, albeit perhaps at a slower rate. Advanced visualization tools with increased resolution assist in drawing conclusions from phylogenetic estimates and may even have potential to better inform public health and treatment decisions, but the design (and choice of what analyses are shown) is hindered by the complexity of information embedded within current phylogenetic models and the integration of available meta-data. In this review, we discuss visualization challenges for the interpretation and exploration of reconstructed histories of viral epidemics that arose from increasing volumes of sequence data and the wealth of additional data layers that can be integrated. We focus on solutions that address joint temporal and spatial visualization but also consider what the future may bring in terms of visualization and how this may become of value for the coming era of real-time digital pathogen surveillance, where actionable results and adequate intervention strategies need to be obtained within days.
Collapse
Affiliation(s)
- Kristof Theys
- Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Clinical and Epidemiological Virology, KU Leuven, Leuven, Belgium
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Clinical and Epidemiological Virology, KU Leuven, Leuven, Belgium
| | - Anne-Mieke Vandamme
- Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Clinical and Epidemiological Virology, KU Leuven, Leuven, Belgium
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Clinical and Epidemiological Virology, KU Leuven, Leuven, Belgium
| |
Collapse
|
43
|
Rakotomalala M, Vrancken B, Pinel-Galzi A, Ramavovololona P, Hébrard E, Randrianangaly JS, Dellicour S, Lemey P, Fargette D. Comparing patterns and scales of plant virus phylogeography: Rice yellow mottle virus in Madagascar and in continental Africa. Virus Evol 2019; 5:vez023. [PMID: 31384483 PMCID: PMC6671560 DOI: 10.1093/ve/vez023] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Rice yellow mottle virus (RYMV) in Madagascar Island provides an opportunity to study the spread of a plant virus disease after a relatively recent introduction in a large and isolated country with a heterogeneous host landscape ecology. Here, we take advantage of field survey data on the occurrence of RYMV disease throughout Madagascar dating back to the 1970s, and of virus genetic data from ninety-four isolates collected since 1989 in most regions of the country to reconstruct the epidemic history. We find that the Malagasy isolates belong to a unique recombinant strain that most likely entered Madagascar through a long-distance introduction from the most eastern part of mainland Africa. We infer the spread of RYMV as a continuous process using a Bayesian statistical framework. In order to calibrate the time scale in calendar time units in this analysis, we pool the information about the RYMV evolutionary rate from several geographical partitions. Whereas the field surveys and the phylogeographic reconstructions both point to a rapid southward invasion across hundreds of kilometers throughout Madagascar within three to four decades, they differ on the inferred origin location and time of the epidemic. The phylogeographic reconstructions suggest a lineage displacement and unveil a re-invasion of the northern regions that may have remained unnoticed otherwise. Despite ecological differences that could affect the transmission potential of RYMV in Madagascar and in mainland Africa, we estimate similar invasion and dispersal rates. We could not identify environmental factors that have a relevant impact on the lineage dispersal velocity of RYMV in Madagascar. This study highlights the value and complementarity of (historical) nongenetic and (more contemporaneous) genetic surveillance data for reconstructing the history of spread of plant viruses.
Collapse
Affiliation(s)
- Mbolarinosy Rakotomalala
- Centre Régional de Recherche du Nord-Ouest du FOFIFA, BP 289, Mahavoky Avaratra, Mahajanga 401, Madagascar
| | - Bram Vrancken
- Department of Microbiology and Immunology, Laboratory for Clinical and Epidemiological Virology, Rega Institute, KU Leuven, Herestraat 49 box 1040, 3000 Leuven, Belgium
| | - Agnès Pinel-Galzi
- IRD, Cirad, Université Montpellier, IPME, 911 avenue Agropolis, BP 64501 34934 Montpellier cedex 5, France
| | - Perle Ramavovololona
- Département de Biologie et d'Ecologie Végétales, Faculté des Sciences, Université d'Antananarivo, BP 906
| | - Eugénie Hébrard
- IRD, Cirad, Université Montpellier, IPME, 911 avenue Agropolis, BP 64501 34934 Montpellier cedex 5, France
| | | | - Simon Dellicour
- Department of Microbiology and Immunology, Laboratory for Clinical and Epidemiological Virology, Rega Institute, KU Leuven, Herestraat 49 box 1040, 3000 Leuven, Belgium.,Spatial Epidemiology Lab, Université Libre de Bruxelles, CP 264 / 3,50 av FD Roosevelt, B-1050 Brussels, Belgium
| | - Philippe Lemey
- Department of Microbiology and Immunology, Laboratory for Clinical and Epidemiological Virology, Rega Institute, KU Leuven, Herestraat 49 box 1040, 3000 Leuven, Belgium
| | - Denis Fargette
- IRD, Cirad, Université Montpellier, IPME, 911 avenue Agropolis, BP 64501 34934 Montpellier cedex 5, France
| |
Collapse
|
44
|
Equine Influenza Virus in Asia: Phylogeographic Pattern and Molecular Features Reveal Circulation of an Autochthonous Lineage. J Virol 2019; 93:JVI.00116-19. [PMID: 31019053 DOI: 10.1128/jvi.00116-19] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Accepted: 04/09/2019] [Indexed: 12/12/2022] Open
Abstract
Equine influenza virus (EIV) causes severe acute respiratory disease in horses. Currently, the strains belonging to the H3N8 subtype are divided into two clades, Florida clade 1 (FC1) and Florida clade 2 (FC2), which emerged in 2002. Both FC1 and FC2 clades were reported in Asian and Middle East countries in the last decade. In this study, we described the evolution, epidemiology, and molecular characteristic of the EIV lineages, with focus on those detected in Asia from 2007 to 2017. The full genome phylogeny showed that FC1 and FC2 constituted separate and divergent lineages, without evidence of reassortment between the clades. While FC1 evolved as a single lineage, FC2 showed a divergent event around 2004 giving rise to two well-supported and coexisting sublineages, European and Asian. Furthermore, two different spread patterns of EIV in Asian countries were identified. The FC1 outbreaks were caused by independent introductions of EIV from the Americas, with the Asian isolates genetically similar to the contemporary American lineages. On the other hand, the FC2 strains detected in Asian mainland countries conformed to an autochthonous monophyletic group with a common ancestor dated in 2006 and showed evidence of an endemic circulation in a local host. Characteristic aminoacidic signature patterns were detected in all viral proteins in both Asian-FC1 and FC2 populations. Several changes were located at the top of the HA1 protein, inside or near antigenic sites. Further studies are needed to assess the potential impact of these antigenic changes in vaccination programs.IMPORTANCE The complex and continuous antigenic evolution of equine influenza viruses (EIVs) remains a major hurdle for vaccine development and the design of effective immunization programs. The present study provides a comprehensive analysis showing the EIV evolutionary dynamics, including the spread and circulation within the Asian continent and its relationship to global EIV populations over a 10-year period. Moreover, we provide a better understanding of EIV molecular evolution in Asian countries and its consequences on the antigenicity. The study underscores the association between the global horse movement and the circulation of EIV in this region. Understanding EIV evolution is imperative in order to mitigate the risk of outbreaks affecting the horse industry and to help with the selection of the viral strains to be included in the formulation of future vaccines.
Collapse
|
45
|
Omondi G, Alkhamis MA, Obanda V, Gakuya F, Sangula A, Pauszek S, Perez A, Ngulu S, van Aardt R, Arzt J, VanderWaal K. Phylogeographical and cross-species transmission dynamics of SAT1 and SAT2 foot-and-mouth disease virus in Eastern Africa. Mol Ecol 2019; 28:2903-2916. [PMID: 31074125 DOI: 10.1111/mec.15125] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 04/28/2019] [Accepted: 04/29/2019] [Indexed: 12/15/2022]
Abstract
Understanding the dynamics of foot-and-mouth disease virus (FMDV), an endemic and economically constraining disease, is critical in designing control programmes in Africa. This study investigates the evolutionary epidemiology of SAT1 and SAT2 FMDV in Eastern Africa, as well as between cattle and wild African buffalo. Bayesian phylodynamic models were used to analyse SAT1 and SAT2 VP1 gene segments collected between 1975 and 2016, focusing on the SAT1 and SAT2 viruses currently circulating in Eastern Africa. The root state posterior probabilities inferred from our analyses suggest Zimbabwe as the ancestral location for SAT1 currently circulating in Eastern Africa (p = 0.67). For the SAT2 clade, Kenya is inferred to be the ancestral location for introduction of the virus into other countries in Eastern Africa (p = 0.72). Salient (Bayes factor >10) viral dispersal routes were inferred from Tanzania to Kenya, and from Kenya to Uganda for SAT1 and SAT2, respectively. Results suggest that cattle are the source of the SAT1 and SAT2 clades currently circulating in Eastern Africa. In addition, our results suggest that the majority of SAT1 and SAT2 in livestock come from other livestock rather than wildlife, with limited evidence that buffalo serve as reservoirs for cattle. Insights from the present study highlight the role of cattle movements and anthropogenic activities in shaping the evolutionary history of SAT1 and SAT2 in Eastern Africa. While the results may be affected by inherent limitations of imperfect surveillance, our analysis elucidates the dynamics between host species in this region, which is key to guiding disease intervention activities.
Collapse
Affiliation(s)
- George Omondi
- Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, Minnesota
| | - Moh A Alkhamis
- Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, Minnesota.,Department of Epidemiology and Biostatistics, Faculty of Public Health, Health Sciences Center, Kuwait University, Kuwait, Kuwait
| | - Vincent Obanda
- Veterinary Services Department, Kenya Wildlife Service, Nairobi, Kenya
| | - Francis Gakuya
- Veterinary Services Department, Kenya Wildlife Service, Nairobi, Kenya
| | | | - Steven Pauszek
- Plum Island Animal Disease Center, Foreign Animal Disease Research Unit, USDA, Orient Point, New York
| | - Andres Perez
- Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, Minnesota
| | | | | | - Jonathan Arzt
- Plum Island Animal Disease Center, Foreign Animal Disease Research Unit, USDA, Orient Point, New York
| | - Kim VanderWaal
- Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, Minnesota
| |
Collapse
|
46
|
Ratmann O, Grabowski MK, Hall M, Golubchik T, Wymant C, Abeler-Dörner L, Bonsall D, Hoppe A, Brown AL, de Oliveira T, Gall A, Kellam P, Pillay D, Kagaayi J, Kigozi G, Quinn TC, Wawer MJ, Laeyendecker O, Serwadda D, Gray RH, Fraser C. Inferring HIV-1 transmission networks and sources of epidemic spread in Africa with deep-sequence phylogenetic analysis. Nat Commun 2019; 10:1411. [PMID: 30926780 PMCID: PMC6441045 DOI: 10.1038/s41467-019-09139-4] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Accepted: 02/22/2019] [Indexed: 11/09/2022] Open
Abstract
To prevent new infections with human immunodeficiency virus type 1 (HIV-1) in sub-Saharan Africa, UNAIDS recommends targeting interventions to populations that are at high risk of acquiring and passing on the virus. Yet it is often unclear who and where these 'source' populations are. Here we demonstrate how viral deep-sequencing can be used to reconstruct HIV-1 transmission networks and to infer the direction of transmission in these networks. We are able to deep-sequence virus from a large population-based sample of infected individuals in Rakai District, Uganda, reconstruct partial transmission networks, and infer the direction of transmission within them at an estimated error rate of 16.3% [8.8-28.3%]. With this error rate, deep-sequence phylogenetics cannot be used against individuals in legal contexts, but is sufficiently low for population-level inferences into the sources of epidemic spread. The technique presents new opportunities for characterizing source populations and for targeting of HIV-1 prevention interventions in Africa.
Collapse
Affiliation(s)
- Oliver Ratmann
- Department of Mathematics, Imperial College London, London, SW72AZ, UK.
- Department of Infectious Disease, Epidemiology School of Public Health, Imperial College London, London, W21PG, UK.
| | - M Kate Grabowski
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, 21205-2196, USA
- Rakai Health Sciences Program, Entebbe, P.O.Box 49, Uganda
| | - Matthew Hall
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, Old Road Campus, University of Oxford, Oxford, OX3 7BN, UK
| | - Tanya Golubchik
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, Old Road Campus, University of Oxford, Oxford, OX3 7BN, UK
| | - Chris Wymant
- Department of Infectious Disease, Epidemiology School of Public Health, Imperial College London, London, W21PG, UK
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, Old Road Campus, University of Oxford, Oxford, OX3 7BN, UK
| | - Lucie Abeler-Dörner
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, Old Road Campus, University of Oxford, Oxford, OX3 7BN, UK
| | - David Bonsall
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, Old Road Campus, University of Oxford, Oxford, OX3 7BN, UK
| | - Anne Hoppe
- Division of Infection and Immunity, University College London, London, WC1E 6BT, UK
| | - Andrew Leigh Brown
- School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3FF, UK
| | - Tulio de Oliveira
- College of Health Sciences, University of KwaZulu-Natal, Durban, 4041, South Africa
| | - Astrid Gall
- European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Paul Kellam
- Department of Medicine, Imperial College London, London, W12 0HS, UK
| | - Deenan Pillay
- Division of Infection and Immunity, University College London, London, WC1E 6BT, UK
- Africa Health Research Institute, Private Bag X7, Durban, 4013, South Africa
| | - Joseph Kagaayi
- Rakai Health Sciences Program, Entebbe, P.O.Box 49, Uganda
| | - Godfrey Kigozi
- Rakai Health Sciences Program, Entebbe, P.O.Box 49, Uganda
| | - Thomas C Quinn
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, 21205-2196, USA
- Division of Intramural Research, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD, 20892-9806, USA
| | - Maria J Wawer
- Rakai Health Sciences Program, Entebbe, P.O.Box 49, Uganda
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Oliver Laeyendecker
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, 21205-2196, USA
- Division of Intramural Research, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD, 20892-9806, USA
| | - David Serwadda
- Rakai Health Sciences Program, Entebbe, P.O.Box 49, Uganda
- Makerere University School of Public Health, Kampala, 8HQG+3V, Uganda
| | - Ronald H Gray
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, 21205-2196, USA
- Rakai Health Sciences Program, Entebbe, P.O.Box 49, Uganda
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Christophe Fraser
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, Old Road Campus, University of Oxford, Oxford, OX3 7BN, UK
| |
Collapse
|
47
|
Abstract
A growing number of infectious pathogens are spreading among geographic regions. Some pathogens that were previously not considered to pose a general threat to human health have emerged at regional and global scales, such as Zika and Ebola Virus Disease. Other pathogens, such as yellow fever virus, were previously thought to be under control but have recently re-emerged, causing new challenges to public health organisations. A wide array of new modelling techniques, aided by increased computing capabilities, novel diagnostic tools, and the increased speed and availability of genomic sequencing allow researchers to identify new pathogens more rapidly, assess the likelihood of geographic spread, and quantify the speed of human-to-human transmission. Despite some initial successes in predicting the spread of acute viral infections, the practicalities and sustainability of such approaches will need to be evaluated in the context of public health responses.
Collapse
|