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Thompson A, Liebeskind BJ, Scully EJ, Landis MJ. Deep Learning and Likelihood Approaches for Viral Phylogeography Converge on the Same Answers Whether the Inference Model Is Right or Wrong. Syst Biol 2024; 73:183-206. [PMID: 38189575 PMCID: PMC11249978 DOI: 10.1093/sysbio/syad074] [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: 02/15/2023] [Revised: 11/22/2023] [Accepted: 01/05/2024] [Indexed: 01/09/2024] Open
Abstract
Analysis of phylogenetic trees has become an essential tool in epidemiology. Likelihood-based methods fit models to phylogenies to draw inferences about the phylodynamics and history of viral transmission. However, these methods are often computationally expensive, which limits the complexity and realism of phylodynamic models and makes them ill-suited for informing policy decisions in real-time during rapidly developing outbreaks. Likelihood-free methods using deep learning are pushing the boundaries of inference beyond these constraints. In this paper, we extend, compare, and contrast a recently developed deep learning method for likelihood-free inference from trees. We trained multiple deep neural networks using phylogenies from simulated outbreaks that spread among 5 locations and found they achieve close to the same levels of accuracy as Bayesian inference under the true simulation model. We compared robustness to model misspecification of a trained neural network to that of a Bayesian method. We found that both models had comparable performance, converging on similar biases. We also implemented a method of uncertainty quantification called conformalized quantile regression that we demonstrate has similar patterns of sensitivity to model misspecification as Bayesian highest posterior density (HPD) and greatly overlap with HPDs, but have lower precision (more conservative). Finally, we trained and tested a neural network against phylogeographic data from a recent study of the SARS-Cov-2 pandemic in Europe and obtained similar estimates of region-specific epidemiological parameters and the location of the common ancestor in Europe. Along with being as accurate and robust as likelihood-based methods, our trained neural networks are on average over 3 orders of magnitude faster after training. Our results support the notion that neural networks can be trained with simulated data to accurately mimic the good and bad statistical properties of the likelihood functions of generative phylogenetic models.
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Affiliation(s)
- Ammon Thompson
- Participant in an Education Program Sponsored by U.S. Department of Defense (DOD) at the National Geospatial-Intelligence Agency, Springfield, VA 22150, USA
| | | | - Erik J Scully
- National Geospatial-Intelligence Agency, Springfield, VA 22150, USA
| | - Michael J Landis
- Department of Biology, Washington University in St. Louis, Rebstock Hall, St. Louis, MO 63130, USA
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2
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Vaughan TG, Scire J, Nadeau SA, Stadler T. Estimates of early outbreak-specific SARS-CoV-2 epidemiological parameters from genomic data. Proc Natl Acad Sci U S A 2024; 121:e2308125121. [PMID: 38175864 PMCID: PMC10786264 DOI: 10.1073/pnas.2308125121] [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] [Received: 05/15/2023] [Accepted: 12/02/2023] [Indexed: 01/06/2024] Open
Abstract
We estimate the basic reproductive number and case counts for 15 distinct Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreaks, distributed across 11 populations (10 countries and one cruise ship), based solely on phylodynamic analyses of genomic data. Our results indicate that, prior to significant public health interventions, the reproductive numbers for 10 (out of 15) of these outbreaks are similar, with median posterior estimates ranging between 1.4 and 2.8. These estimates provide a view which is complementary to that provided by those based on traditional line listing data. The genomic-based view is arguably less susceptible to biases resulting from differences in testing protocols, testing intensity, and import of cases into the community of interest. In the analyses reported here, the genomic data primarily provide information regarding which samples belong to a particular outbreak. We observe that once these outbreaks are identified, the sampling dates carry the majority of the information regarding the reproductive number. Finally, we provide genome-based estimates of the cumulative number of infections for each outbreak. For 7 out of 11 of the populations studied, the number of confirmed cases is much bigger than the cumulative number of infections estimated from the sequence data, a possible explanation being the presence of unsequenced outbreaks in these populations.
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Affiliation(s)
- Timothy G. Vaughan
- Department of Biosystems Science and Engineering, Eidgenössiche Technische Hochschule Zurich, Basel4058, Switzerland
- Computational Evolution Group, Swiss Institute of Bioinformatics, Lausanne1015, Switzerland
| | - Jérémie Scire
- Department of Biosystems Science and Engineering, Eidgenössiche Technische Hochschule Zurich, Basel4058, Switzerland
- Computational Evolution Group, Swiss Institute of Bioinformatics, Lausanne1015, Switzerland
| | - Sarah A. Nadeau
- Department of Biosystems Science and Engineering, Eidgenössiche Technische Hochschule Zurich, Basel4058, Switzerland
- Computational Evolution Group, Swiss Institute of Bioinformatics, Lausanne1015, Switzerland
| | - Tanja Stadler
- Department of Biosystems Science and Engineering, Eidgenössiche Technische Hochschule Zurich, Basel4058, Switzerland
- Computational Evolution Group, Swiss Institute of Bioinformatics, Lausanne1015, Switzerland
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3
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Matteson NL, Hassler GW, Kurzban E, Schwab MA, Perkins SA, Gangavarapu K, Levy JI, Parker E, Pride D, Hakim A, De Hoff P, Cheung W, Castro-Martinez A, Rivera A, Veder A, Rivera A, Wauer C, Holmes J, Wilson J, Ngo SN, Plascencia A, Lawrence ES, Smoot EW, Eisner ER, Tsai R, Chacón M, Baer NA, Seaver P, Salido RA, Aigner S, Ngo TT, Barber T, Ostrander T, Fielding-Miller R, Simmons EH, Zazueta OE, Serafin-Higuera I, Sanchez-Alavez M, Moreno-Camacho JL, García-Gil A, Murphy Schafer AR, McDonald E, Corrigan J, Malone JD, Stous S, Shah S, Moshiri N, Weiss A, Anderson C, Aceves CM, Spencer EG, Hufbauer EC, Lee JJ, King AJ, Ramesh KS, Nguyen KN, Saucedo K, Robles-Sikisaka R, Fisch KM, Gonias SL, Birmingham A, McDonald D, Karthikeyan S, Martin NK, Schooley RT, Negrete AJ, Reyna HJ, Chavez JR, Garcia ML, Cornejo-Bravo JM, Becker D, Isaksson M, Washington NL, Lee W, Garfein RS, Luna-Ruiz Esparza MA, Alcántar-Fernández J, Henson B, Jepsen K, Olivares-Flores B, Barrera-Badillo G, Lopez-Martínez I, Ramírez-González JE, Flores-León R, Kingsmore SF, Sanders A, Pradenas A, White B, Matthews G, Hale M, McLawhon RW, Reed SL, Winbush T, McHardy IH, Fielding RA, Nicholson L, Quigley MM, Harding A, Mendoza A, Bakhtar O, Browne SH, Olivas Flores J, Rincon Rodríguez DG, Gonzalez Ibarra M, Robles Ibarra LC, Arellano Vera BJ, Gonzalez Garcia J, Harvey-Vera A, Knight R, Laurent LC, Yeo GW, Wertheim JO, Ji X, Worobey M, Suchard MA, Andersen KG, Campos-Romero A, Wohl S, Zeller M. Genomic surveillance reveals dynamic shifts in the connectivity of COVID-19 epidemics. Cell 2023; 186:5690-5704.e20. [PMID: 38101407 PMCID: PMC10795731 DOI: 10.1016/j.cell.2023.11.024] [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: 03/12/2023] [Revised: 08/21/2023] [Accepted: 11/21/2023] [Indexed: 12/17/2023]
Abstract
The maturation of genomic surveillance in the past decade has enabled tracking of the emergence and spread of epidemics at an unprecedented level. During the COVID-19 pandemic, for example, genomic data revealed that local epidemics varied considerably in the frequency of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lineage importation and persistence, likely due to a combination of COVID-19 restrictions and changing connectivity. Here, we show that local COVID-19 epidemics are driven by regional transmission, including across international boundaries, but can become increasingly connected to distant locations following the relaxation of public health interventions. By integrating genomic, mobility, and epidemiological data, we find abundant transmission occurring between both adjacent and distant locations, supported by dynamic mobility patterns. We find that changing connectivity significantly influences local COVID-19 incidence. Our findings demonstrate a complex meaning of "local" when investigating connected epidemics and emphasize the importance of collaborative interventions for pandemic prevention and mitigation.
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Affiliation(s)
| | - Gabriel W Hassler
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Ezra Kurzban
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Madison A Schwab
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Sarah A Perkins
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Karthik Gangavarapu
- Department of Biomathematics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, CA, USA; Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Joshua I Levy
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Edyth Parker
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - David Pride
- Department of Pathology, University of California, San Diego, La Jolla, CA, USA; Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Abbas Hakim
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Diego, La Jolla, CA, USA; COVID-19 Detection, Investigation, Surveillance, Clinical, and Outbreak Response, California Department of Public Health, Richmond, CA, USA
| | - Peter De Hoff
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Diego, La Jolla, CA, USA; COVID-19 Detection, Investigation, Surveillance, Clinical, and Outbreak Response, California Department of Public Health, Richmond, CA, USA
| | - Willi Cheung
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Diego, La Jolla, CA, USA; COVID-19 Detection, Investigation, Surveillance, Clinical, and Outbreak Response, California Department of Public Health, Richmond, CA, USA
| | - Anelizze Castro-Martinez
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Diego, La Jolla, CA, USA; Sanford Consortium of Regenerative Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Andrea Rivera
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Anthony Veder
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Ariana Rivera
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Cassandra Wauer
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Jacqueline Holmes
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Jedediah Wilson
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Shayla N Ngo
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Ashley Plascencia
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Elijah S Lawrence
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Elizabeth W Smoot
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Emily R Eisner
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Rebecca Tsai
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Marisol Chacón
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Nathan A Baer
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Phoebe Seaver
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Rodolfo A Salido
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Stefan Aigner
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Toan T Ngo
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Tom Barber
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Tyler Ostrander
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Rebecca Fielding-Miller
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, USA; Division of Infectious Disease and Global Public Health, University of California, San Diego, La Jolla, CA, USA
| | | | - Oscar E Zazueta
- Department of Epidemiology, Secretaria de Salud de Baja California, Tijuana, Baja California, Mexico
| | | | - Manuel Sanchez-Alavez
- Centro de Diagnostico COVID-19 UABC, Tijuana, Baja California, Mexico; Department of Molecular Medicine, Scripps Research, La Jolla, CA, USA
| | | | - Abraham García-Gil
- Clinical Laboratory Department, Salud Digna, A.C, Tijuana, Baja California, Mexico
| | | | - Eric McDonald
- County of San Diego Health and Human Services Agency, San Diego, CA, USA
| | - Jeremy Corrigan
- County of San Diego Health and Human Services Agency, San Diego, CA, USA
| | - John D Malone
- County of San Diego Health and Human Services Agency, San Diego, CA, USA
| | - Sarah Stous
- County of San Diego Health and Human Services Agency, San Diego, CA, USA
| | - Seema Shah
- County of San Diego Health and Human Services Agency, San Diego, CA, USA
| | - Niema Moshiri
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA
| | - Alana Weiss
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Catelyn Anderson
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Christine M Aceves
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Emily G Spencer
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Emory C Hufbauer
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Justin J Lee
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Alison J King
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Karthik S Ramesh
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Kelly N Nguyen
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Kieran Saucedo
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | | | - Kathleen M Fisch
- Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Diego, La Jolla, CA, USA; Center for Computational Biology and Bioinformatics, University of California San Diego, La Jolla, CA, USA
| | - Steven L Gonias
- Department of Pathology, University of California, San Diego, La Jolla, CA, USA
| | - Amanda Birmingham
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Daniel McDonald
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Smruthi Karthikeyan
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Natasha K Martin
- Division of Infectious Disease and Global Public Health, University of California, San Diego, La Jolla, CA, USA
| | - Robert T Schooley
- Division of Infectious Disease and Global Public Health, University of California, San Diego, La Jolla, CA, USA
| | - Agustin J Negrete
- Facultad de Ciencias de la Salud Universidad Autonoma de Baja California Valle de Las Palmas, Tijuana, Baja California, Mexico
| | - Horacio J Reyna
- Facultad de Ciencias de la Salud Universidad Autonoma de Baja California Valle de Las Palmas, Tijuana, Baja California, Mexico
| | - Jose R Chavez
- Facultad de Ciencias de la Salud Universidad Autonoma de Baja California Valle de Las Palmas, Tijuana, Baja California, Mexico
| | - Maria L Garcia
- Facultad de Ciencias de la Salud Universidad Autonoma de Baja California Valle de Las Palmas, Tijuana, Baja California, Mexico
| | - Jose M Cornejo-Bravo
- Facultad de Ciencias Quimicas e Ingenieria, Universidad Autonoma de Baja California, Tijuana, Baja California, Mexico
| | | | | | | | | | - Richard S Garfein
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, USA
| | | | | | - Benjamin Henson
- Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Kristen Jepsen
- Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Beatriz Olivares-Flores
- Instituto de Diagnóstico y Referencia Epidemiológicos (InDRE), Ciudad de México, CDMX, Mexico
| | - Gisela Barrera-Badillo
- Instituto de Diagnóstico y Referencia Epidemiológicos (InDRE), Ciudad de México, CDMX, Mexico
| | - Irma Lopez-Martínez
- Instituto de Diagnóstico y Referencia Epidemiológicos (InDRE), Ciudad de México, CDMX, Mexico
| | - José E Ramírez-González
- Instituto de Diagnóstico y Referencia Epidemiológicos (InDRE), Ciudad de México, CDMX, Mexico
| | - Rita Flores-León
- Instituto de Diagnóstico y Referencia Epidemiológicos (InDRE), Ciudad de México, CDMX, Mexico
| | | | - Alison Sanders
- Return to Learn, University of California, San Diego, La Jolla, CA, USA
| | - Allorah Pradenas
- Return to Learn, University of California, San Diego, La Jolla, CA, USA
| | - Benjamin White
- Return to Learn, University of California, San Diego, La Jolla, CA, USA
| | - Gary Matthews
- Return to Learn, University of California, San Diego, La Jolla, CA, USA
| | - Matt Hale
- Return to Learn, University of California, San Diego, La Jolla, CA, USA
| | - Ronald W McLawhon
- Return to Learn, University of California, San Diego, La Jolla, CA, USA
| | - Sharon L Reed
- Return to Learn, University of California, San Diego, La Jolla, CA, USA
| | - Terri Winbush
- Return to Learn, University of California, San Diego, La Jolla, CA, USA
| | | | | | | | | | | | | | | | - Sara H Browne
- Division of Infectious Disease and Global Public Health, University of California, San Diego, La Jolla, CA, USA; Specialist in Global Health, Encinitas, CA, USA
| | - Jocelyn Olivas Flores
- Facultad de Ciencias Quimicas e Ingenieria, Universidad Autonoma de Baja California, Tijuana, Baja California, Mexico; University of HealthMx, Tijuana, Baja California, Mexico
| | - Diana G Rincon Rodríguez
- University of HealthMx, Tijuana, Baja California, Mexico; Facultad de Medicina, Universidad Xochicalco, Tijuana, Baja California, Mexico
| | - Martin Gonzalez Ibarra
- University of HealthMx, Tijuana, Baja California, Mexico; Facultad de Medicina, Universidad Xochicalco, Tijuana, Baja California, Mexico
| | - Luis C Robles Ibarra
- University of HealthMx, Tijuana, Baja California, Mexico; Instituto de Seguridad y Servicios Sociales de los Trabajadores del Estado, Tijuana, Baja California, Mexico
| | - Betsy J Arellano Vera
- University of HealthMx, Tijuana, Baja California, Mexico; Instituto Mexicano del Seguro Social, Tijuana, Baja California, Mexico
| | - Jonathan Gonzalez Garcia
- University of HealthMx, Tijuana, Baja California, Mexico; SIMNSA, Tijuana, Baja California, Mexico
| | | | - Rob Knight
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Louise C Laurent
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Diego, La Jolla, CA, USA; Sanford Consortium of Regenerative Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Gene W Yeo
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Sanford Consortium of Regenerative Medicine, University of California, San Diego, La Jolla, CA, USA; Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Joel O Wertheim
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Xiang Ji
- Department of Mathematics, School of Science and Engineering, Tulane University, New Orleans, LA, USA
| | - Michael Worobey
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA
| | - Marc A Suchard
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Kristian G Andersen
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA.
| | - Abraham Campos-Romero
- Innovation and Research Department, Salud Digna, A.C, Tijuana, Baja California, Mexico
| | - Shirlee Wohl
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Mark Zeller
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA.
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Mendes FK, Landis MJ. PhyloJunction: a computational framework for simulating, developing, and teaching evolutionary models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.15.571907. [PMID: 38168278 PMCID: PMC10760140 DOI: 10.1101/2023.12.15.571907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
We introduce PhyloJunction, a computational framework designed to facilitate the prototyping, testing, and characterization of evolutionary models. PhyloJunction is distributed as an open-source Python library that can be used to implement a variety of models, through its flexible graphical modeling architecture and dedicated model specification language. Model design and use are exposed to users via command-line and graphical interfaces, which integrate the steps of simulating, summarizing, and visualizing data. This paper describes the features of PhyloJunction - which include, but are not limited to, a general implementation of a popular family of phylogenetic diversification models - and, moving forward, how it may be expanded to not only include new models, but to also become a platform for conducting and teaching statistical learning.
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Affiliation(s)
- Fábio K. Mendes
- Department of Biology, Washington University in St. Louis, St. Louis, MO
| | - Michael J. Landis
- Department of Biology, Washington University in St. Louis, St. Louis, MO
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5
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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.
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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
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6
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Drummond AJ, Chen K, Mendes FK, Xie D. LinguaPhylo: A probabilistic model specification language for reproducible phylogenetic analyses. PLoS Comput Biol 2023; 19:e1011226. [PMID: 37463154 PMCID: PMC10381047 DOI: 10.1371/journal.pcbi.1011226] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 05/30/2023] [Indexed: 07/20/2023] Open
Abstract
Phylogenetic models have become increasingly complex, and phylogenetic data sets have expanded in both size and richness. However, current inference tools lack a model specification language that can concisely describe a complete phylogenetic analysis while remaining independent of implementation details. We introduce a new lightweight and concise model specification language, 'LPhy', which is designed to be both human and machine-readable. A graphical user interface accompanies 'LPhy', allowing users to build models, simulate data, and create natural language narratives describing the models. These narratives can serve as the foundation for manuscript method sections. Additionally, we present a command-line interface for converting LPhy-specified models into analysis specification files (in XML format) compatible with the BEAST2 software platform. Collectively, these tools aim to enhance the clarity of descriptions and reporting of probabilistic models in phylogenetic studies, ultimately promoting reproducibility of results.
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Affiliation(s)
- Alexei J Drummond
- Centre for Computational Evolution, University of Auckland, Auckland, New Zealand
- School of Biological Sciences, University of Auckland, Auckland, New Zealand
- School of Computer Science, University of Auckland, Auckland, New Zealand
| | - Kylie Chen
- Centre for Computational Evolution, University of Auckland, Auckland, New Zealand
- School of Biological Sciences, University of Auckland, Auckland, New Zealand
- School of Computer Science, University of Auckland, Auckland, New Zealand
| | - Fábio K Mendes
- Centre for Computational Evolution, University of Auckland, Auckland, New Zealand
- Department of Biology, Washington University in St. Louis, St. Louis, United States of America
| | - Dong Xie
- Centre for Computational Evolution, University of Auckland, Auckland, New Zealand
- School of Biological Sciences, University of Auckland, Auckland, New Zealand
- School of Computer Science, University of Auckland, Auckland, New Zealand
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7
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Jimenez-Silva C, Rivero R, Douglas J, Bouckaert R, Villabona-Arenas CJ, Atkins KE, Gastelbondo B, Calderon A, Guzman C, Echeverri-De la Hoz D, Muñoz M, Ballesteros N, Castañeda S, Patiño LH, Ramirez A, Luna N, Paniz-Mondolfi A, Serrano-Coll H, Ramirez JD, Mattar S, Drummond AJ. Genomic epidemiology of SARS-CoV-2 variants during the first two years of the pandemic in Colombia. COMMUNICATIONS MEDICINE 2023; 3:97. [PMID: 37443390 DOI: 10.1038/s43856-023-00328-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 06/22/2023] [Indexed: 07/15/2023] Open
Abstract
BACKGROUND The emergence of highly transmissible SARS-CoV-2 variants has led to surges in cases and the need for global genomic surveillance. While some variants rapidly spread worldwide, other variants only persist nationally. There is a need for more fine-scale analysis to understand transmission dynamics at a country scale. For instance, the Mu variant of interest, also known as lineage B.1.621, was first detected in Colombia and was responsible for a large local wave but only a few sporadic cases elsewhere. METHODS To better understand the epidemiology of SARS-Cov-2 variants in Colombia, we used 14,049 complete SARS-CoV-2 genomes from the 32 states of Colombia. We performed Bayesian phylodynamic analyses to estimate the time of variants' introduction, their respective effective reproductive number, and effective population size, and the impact of disease control measures. RESULTS Here, we detect a total of 188 SARS-CoV-2 Pango lineages circulating in Colombia since the pandemic's start. We show that the effective reproduction number oscillated drastically throughout the first two years of the pandemic, with Mu showing the highest transmissibility (Re and growth rate estimation). CONCLUSIONS Our results reinforce that genomic surveillance programs are essential for countries to make evidence-driven interventions toward the emergence and circulation of novel SARS-CoV-2 variants.
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Affiliation(s)
- Cinthy Jimenez-Silva
- Centre for Computational Evolution, University of Auckland, Auckland, New Zealand.
- School of Biological Sciences, University of Auckland, Auckland, New Zealand.
| | - Ricardo Rivero
- Instituto de Investigaciones Biológicas del Trópico (IIBT), Facultad de Medicina Veterinaria y Zootecnia, Universidad de Córdoba, Montería, Colombia.
- Paul G. Allen School for Global Health, Washington State University, Pullman, Washington, USA.
| | - Jordan Douglas
- Centre for Computational Evolution, University of Auckland, Auckland, New Zealand
- Department of Physics, University of Auckland, Auckland, New Zealand
| | - Remco Bouckaert
- Centre for Computational Evolution, University of Auckland, Auckland, New Zealand
- School of Computer Science, University of Auckland, Auckland, New Zealand
| | - Ch Julian Villabona-Arenas
- Centre for Mathematical Modelling of Infectious Diseases and Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Katherine E Atkins
- Centre for Mathematical Modelling of Infectious Diseases and Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
- Centre for Global Health, Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, UK
| | - Bertha Gastelbondo
- Instituto de Investigaciones Biológicas del Trópico (IIBT), Facultad de Medicina Veterinaria y Zootecnia, Universidad de Córdoba, Montería, Colombia
- Grupo de Investigaciones Microbiológicas y Biomédicas de Córdoba-GIMBIC, Universidad de Córdoba, Monteria, Colombia
- Grupo de Salud Pública y Auditoría en Salud, Corporación Universitaria del Caribe- CECAR, Sincelejo, Colombia
| | - Alfonso Calderon
- Instituto de Investigaciones Biológicas del Trópico (IIBT), Facultad de Medicina Veterinaria y Zootecnia, Universidad de Córdoba, Montería, Colombia
| | - Camilo Guzman
- Instituto de Investigaciones Biológicas del Trópico (IIBT), Facultad de Medicina Veterinaria y Zootecnia, Universidad de Córdoba, Montería, Colombia
- Grupo de Investigación, Evaluación y Desarrollo de Farmacos y Afines - IDEFARMA, Universidad de Córdoba, Montería, Colombia
| | - Daniel Echeverri-De la Hoz
- Instituto de Investigaciones Biológicas del Trópico (IIBT), Facultad de Medicina Veterinaria y Zootecnia, Universidad de Córdoba, Montería, Colombia
| | - Marina Muñoz
- Centro de Investigaciones en Microbiología y Biotecnología-UR (CIMBIUR), Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá, Colombia
| | - Nathalia Ballesteros
- Centro de Investigaciones en Microbiología y Biotecnología-UR (CIMBIUR), Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá, Colombia
| | - Sergio Castañeda
- Centro de Investigaciones en Microbiología y Biotecnología-UR (CIMBIUR), Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá, Colombia
| | - Luz H Patiño
- Centro de Investigaciones en Microbiología y Biotecnología-UR (CIMBIUR), Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá, Colombia
| | - Angie Ramirez
- Centro de Investigaciones en Microbiología y Biotecnología-UR (CIMBIUR), Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá, Colombia
| | - Nicolas Luna
- Centro de Investigaciones en Microbiología y Biotecnología-UR (CIMBIUR), Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá, Colombia
| | - Alberto Paniz-Mondolfi
- Molecular Microbiology Laboratory, Department of Pathology, Molecular and Cell-based Medicine, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Hector Serrano-Coll
- Instituto Colombiano de Medicina Tropical-Universidad CES, Medellín, Colombia
| | - Juan David Ramirez
- Centro de Investigaciones en Microbiología y Biotecnología-UR (CIMBIUR), Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá, Colombia.
- Molecular Microbiology Laboratory, Department of Pathology, Molecular and Cell-based Medicine, Icahn School of Medicine at Mount Sinai, New York, USA.
| | - Salim Mattar
- Instituto de Investigaciones Biológicas del Trópico (IIBT), Facultad de Medicina Veterinaria y Zootecnia, Universidad de Córdoba, Montería, Colombia.
| | - Alexei J Drummond
- Centre for Computational Evolution, University of Auckland, Auckland, New Zealand
- School of Biological Sciences, University of Auckland, Auckland, New Zealand
- School of Computer Science, University of Auckland, Auckland, New Zealand
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8
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Grobusch MP, Weld L, Schnyder JL, Larsen CS, Lindner AK, Popescu CP, Huits R, Goorhuis A, Gautret P, Schlagenhauf P. COVID-19 impact on EuroTravNet infectious diseases sentinel surveillance in Europe. Travel Med Infect Dis 2023; 53:102583. [PMID: 37207977 DOI: 10.1016/j.tmaid.2023.102583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 04/19/2023] [Accepted: 05/03/2023] [Indexed: 05/21/2023]
Abstract
BACKGROUND The COVID-19 pandemic resulted in a sharp decline of post-travel patient encounters at the European sentinel surveillance network (EuroTravNet) of travellers' health. We report on the impact of COVID-19 on travel-related infectious diseases as recorded by EuroTravNet clinics. METHODS Travelers who presented between January 1, 2019 and September 30, 2021 were included. Comparisons were made between the pre-pandemic period (14 months from January 1, 2019 to February 29, 2020); and the pandemic period (19 months from March 1, 2020 to September 30, 2021). RESULTS Of the 15,124 visits to the network during the 33-month observation period, 10,941 (72%) were during the pre-pandemic period, and 4183 (28%) during the pandemic period. Average monthly visits declined from 782/month (pre-COVID-19 era) to 220/month (COVID-19 pandemic era). Among non-migrants the top-10 countries of exposure changed after onset of the COVID-19 pandemic; destinations such as Italy and Austria, where COVID-19 exposure peaked in the first months, replaced typical travel destinations in Asia (Thailand, Indonesia, India). There was a small decline in migrant patients reported, with little change in the top countries of exposure (Bolivia, Mali). The three top diagnoses with the largest overall decreases in relative frequency were acute gastroenteritis (-5.3%), rabies post-exposure prophylaxis (-2.8%), and dengue (-2.6%). Apart from COVID-19 (which rose from 0.1% to 12.7%), the three top diagnoses with the largest overall relative frequency increase were schistosomiasis (+4.9%), strongyloidiasis (+2.7%), and latent tuberculosis (+2.4%). CONCLUSIONS A marked COVID-19 pandemic-induced decline in global travel activities is reflected in reduced travel-related infectious diseases sentinel surveillance reporting.
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Affiliation(s)
- Martin P Grobusch
- Center for Tropical Medicine and Travel Medicine, Department of Infectious Diseases, Amsterdam University Medical Centers, Location AMC, University of Amsterdam, Amsterdam, the Netherlands.
| | - Leisa Weld
- Statistical Consultant, Geneva, Switzerland
| | - Jenny L Schnyder
- Center for Tropical Medicine and Travel Medicine, Department of Infectious Diseases, Amsterdam University Medical Centers, Location AMC, University of Amsterdam, Amsterdam, the Netherlands
| | | | - Andreas K Lindner
- Charité-Universitätsmedizin Berlin, Charité Center for Global Health, Institute of International Health, Berlin, Germany
| | - Corneliu Petru Popescu
- Carol Davila University of Medicine and Pharmacy, Bucharest, Romania; Dr Victor Babes Clinical Hospital of Infectious and Tropical Diseases, Bucharest, Romania
| | - Ralph Huits
- Department of Infectious Tropical Diseases and Microbiology, IRCCS Sacro Cuore Don Calabria Hospital, Negrar, Verona, Italy
| | - A Goorhuis
- Center for Tropical Medicine and Travel Medicine, Department of Infectious Diseases, Amsterdam University Medical Centers, Location AMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Philippe Gautret
- IHU Méditerranée Infection, And Aix Marseille University, IRD, AP-HM, SSA, VITROME, Marseille, France
| | - Patricia Schlagenhauf
- WHO Collaborating Centre for Travellers' Health, Department of Global and Public Health, Epidemiology, Biostatistics and Prevention Institute, University of Zürich, Zürich, Switzerland
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9
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Nadeau SA, Vaughan TG, Beckmann C, Topolsky I, Chen C, Hodcroft E, Schär T, Nissen I, Santacroce N, Burcklen E, Ferreira P, Jablonski KP, Posada-Céspedes S, Capece V, Seidel S, Santamaria de Souza N, Martinez-Gomez JM, Cheng P, Bosshard PP, Levesque MP, Kufner V, Schmutz S, Zaheri M, Huber M, Trkola A, Cordey S, Laubscher F, Gonçalves AR, Aeby S, Pillonel T, Jacot D, Bertelli C, Greub G, Leuzinger K, Stange M, Mari A, Roloff T, Seth-Smith H, Hirsch HH, Egli A, Redondo M, Kobel O, Noppen C, du Plessis L, Beerenwinkel N, Neher RA, Beisel C, Stadler T. Swiss public health measures associated with reduced SARS-CoV-2 transmission using genome data. Sci Transl Med 2023; 15:eabn7979. [PMID: 36346321 PMCID: PMC9765449 DOI: 10.1126/scitranslmed.abn7979] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Genome sequences from evolving infectious pathogens allow quantification of case introductions and local transmission dynamics. We sequenced 11,357 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from Switzerland in 2020-the sixth largest effort globally. Using a representative subset of these data, we estimated viral introductions to Switzerland and their persistence over the course of 2020. We contrasted these estimates with simple null models representing the absence of certain public health measures. We show that Switzerland's border closures decoupled case introductions from incidence in neighboring countries. Under a simple model, we estimate an 86 to 98% reduction in introductions during Switzerland's strictest border closures. Furthermore, the Swiss 2020 partial lockdown roughly halved the time for sampled introductions to die out. Last, we quantified local transmission dynamics once introductions into Switzerland occurred using a phylodynamic model. We found that transmission slowed 35 to 63% upon outbreak detection in summer 2020 but not in fall. This finding may indicate successful contact tracing over summer before overburdening in fall. The study highlights the added value of genome sequencing data for understanding transmission dynamics.
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Affiliation(s)
- Sarah A. Nadeau
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland.,Corresponding author. (T.S.); (S.A.N.)
| | - Timothy G. Vaughan
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland
| | | | - Ivan Topolsky
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland
| | - Chaoran Chen
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland
| | - Emma Hodcroft
- SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland.,Institute for Social and Preventive Medicine, University of Bern; 3012, Bern, Switzerland
| | - Tobias Schär
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland
| | - Ina Nissen
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland
| | - Natascha Santacroce
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland
| | - Elodie Burcklen
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland
| | - Pedro Ferreira
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland
| | - Kim Philipp Jablonski
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland
| | - Susana Posada-Céspedes
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland
| | - Vincenzo Capece
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland
| | - Sophie Seidel
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland
| | | | - Julia M. Martinez-Gomez
- Department of Dermatology, University Hospital Zurich, University of Zurich; 8091, Zurich, Switzerland
| | - Phil Cheng
- Department of Dermatology, University Hospital Zurich, University of Zurich; 8091, Zurich, Switzerland
| | - Philipp P. Bosshard
- Department of Dermatology, University Hospital Zurich, University of Zurich; 8091, Zurich, Switzerland
| | - Mitchell P. Levesque
- Department of Dermatology, University Hospital Zurich, University of Zurich; 8091, Zurich, Switzerland
| | - Verena Kufner
- Institute of Medical Virology, University of Zurich; 8057, Zurich, Switzerland
| | - Stefan Schmutz
- Institute of Medical Virology, University of Zurich; 8057, Zurich, Switzerland
| | - Maryam Zaheri
- Institute of Medical Virology, University of Zurich; 8057, Zurich, Switzerland
| | - Michael Huber
- Institute of Medical Virology, University of Zurich; 8057, Zurich, Switzerland
| | - Alexandra Trkola
- Institute of Medical Virology, University of Zurich; 8057, Zurich, Switzerland
| | - Samuel Cordey
- Laboratory of Virology, Department of Diagnostics, Geneva University Hospitals & Faculty of Medicine; 1205, Geneva, Switzerland
| | - Florian Laubscher
- Laboratory of Virology, Department of Diagnostics, Geneva University Hospitals & Faculty of Medicine; 1205, Geneva, Switzerland
| | - Ana Rita Gonçalves
- Swiss National Reference Centre for Influenza, University Hospitals of Geneva; 1205, Geneva, Switzerland
| | - Sébastien Aeby
- Institute of Microbiology, University Hospital Centre and University of Lausanne; 1011, Lausanne, Switzerland
| | - Trestan Pillonel
- Institute of Microbiology, University Hospital Centre and University of Lausanne; 1011, Lausanne, Switzerland
| | - Damien Jacot
- Institute of Microbiology, University Hospital Centre and University of Lausanne; 1011, Lausanne, Switzerland
| | - Claire Bertelli
- Institute of Microbiology, University Hospital Centre and University of Lausanne; 1011, Lausanne, Switzerland
| | - Gilbert Greub
- Institute of Microbiology, University Hospital Centre and University of Lausanne; 1011, Lausanne, Switzerland
| | - Karoline Leuzinger
- Division of Clinical Virology, University Hospital Basel; 4051, Basel, Switzerland.,Department of Biomedicine, University of Basel; 4031, Basel, Switzerland
| | - Madlen Stange
- SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland.,Department of Biomedicine, University of Basel; 4031, Basel, Switzerland.,Division of Clinical Bacteriology and Mycology, University Hospital Basel; 4031, Basel, Switzerland
| | - Alfredo Mari
- SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland.,Department of Biomedicine, University of Basel; 4031, Basel, Switzerland.,Division of Clinical Bacteriology and Mycology, University Hospital Basel; 4031, Basel, Switzerland
| | - Tim Roloff
- SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland.,Department of Biomedicine, University of Basel; 4031, Basel, Switzerland.,Division of Clinical Bacteriology and Mycology, University Hospital Basel; 4031, Basel, Switzerland
| | - Helena Seth-Smith
- SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland.,Department of Biomedicine, University of Basel; 4031, Basel, Switzerland.,Division of Clinical Bacteriology and Mycology, University Hospital Basel; 4031, Basel, Switzerland
| | - Hans H. Hirsch
- Division of Clinical Virology, University Hospital Basel; 4051, Basel, Switzerland.,Department of Biomedicine, University of Basel; 4031, Basel, Switzerland
| | - Adrian Egli
- Department of Biomedicine, University of Basel; 4031, Basel, Switzerland.,Division of Clinical Bacteriology and Mycology, University Hospital Basel; 4031, Basel, Switzerland
| | | | | | | | - Louis du Plessis
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland
| | - Richard A. Neher
- SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland.,Biozentrum, University of Basel; 4056, Basel, Switzerland
| | - Christian Beisel
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland
| | - Tanja Stadler
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland.,Corresponding author. (T.S.); (S.A.N.)
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10
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Lamkiewicz K, Esquivel Gomez LR, Kühnert D, Marz M. Genome Structure, Life Cycle, and Taxonomy of Coronaviruses and the Evolution of SARS-CoV-2. Curr Top Microbiol Immunol 2023; 439:305-339. [PMID: 36592250 DOI: 10.1007/978-3-031-15640-3_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Coronaviruses have a broad host range and exhibit high zoonotic potential. In this chapter, we describe their genomic organization in terms of encoded proteins and provide an introduction to the peculiar discontinuous transcription mechanism. Further, we present evolutionary conserved genomic RNA secondary structure features, which are involved in the complex replication mechanism. With a focus on computational methods, we review the emergence of SARS-CoV-2 starting with the 2019 strains. In that context, we also discuss the debated hypothesis of whether SARS-CoV-2 was created in a laboratory. We focus on the molecular evolution and the epidemiological dynamics of this recently emerged pathogen and we explain how variants of concern are detected and characterised. COVID-19, the disease caused by SARS-CoV-2, can spread through different transmission routes and also depends on a number of risk factors. We describe how current computational models of viral epidemiology, or more specifically, phylodynamics, have facilitated and will continue to enable a better understanding of the epidemic dynamics of SARS-CoV-2.
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Affiliation(s)
- Kevin Lamkiewicz
- RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, Leutragraben 1, 07743, Jena, Germany
- European Virus Bioinformatics Center, Leutragraben 1, 07743, Jena, Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstr. 4, 04103, Leipzig, Germany
| | - Luis Roger Esquivel Gomez
- Transmission, Infection, Diversification and Evolution Group, Max Planck Institute for the Science of Human History, Kahlaische Straße 10, 07745, Jena, Germany
| | - Denise Kühnert
- Transmission, Infection, Diversification and Evolution Group, Max Planck Institute for the Science of Human History, Kahlaische Straße 10, 07745, Jena, Germany
- European Virus Bioinformatics Center, Leutragraben 1, 07743, Jena, Germany
| | - Manja Marz
- RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, Leutragraben 1, 07743, Jena, Germany.
- European Virus Bioinformatics Center, Leutragraben 1, 07743, Jena, Germany.
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstr. 4, 04103, Leipzig, Germany.
- FLI Leibniz Institute for Age Research, Beutenbergstraße 11, 07745, Jena, Germany.
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11
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Douglas J, Winter D, McNeill A, Carr S, Bunce M, French N, Hadfield J, de Ligt J, Welch D, Geoghegan JL. Tracing the international arrivals of SARS-CoV-2 Omicron variants after Aotearoa New Zealand reopened its border. Nat Commun 2022; 13:6484. [PMID: 36309507 PMCID: PMC9617600 DOI: 10.1038/s41467-022-34186-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 10/18/2022] [Indexed: 12/25/2022] Open
Abstract
In the second quarter of 2022, there was a global surge of emergent SARS-CoV-2 lineages that had a distinct growth advantage over then-dominant Omicron BA.1 and BA.2 lineages. By generating 10,403 Omicron genomes, we show that Aotearoa New Zealand observed an influx of these immune-evasive variants (BA.2.12.1, BA.4, and BA.5) through the border. This is explained by the return to significant levels of international travel following the border's reopening in March 2022. We estimate one Omicron transmission event from the border to the community for every ~5,000 passenger arrivals at the current levels of travel and restriction. Although most of these introductions did not instigate any detected onward transmission, a small minority triggered large outbreaks. Genomic surveillance at the border provides a lens on the rate at which new variants might gain a foothold and trigger new waves of infection.
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Affiliation(s)
- Jordan Douglas
- Centre for Computational Evolution,School of Computer Science, University of Auckland, Auckland, New Zealand.
| | - David Winter
- Institute of Environmental Science and Research, Wellington, New Zealand
| | - Andrea McNeill
- Institute of Environmental Science and Research, Wellington, New Zealand
| | - Sam Carr
- Institute of Environmental Science and Research, Wellington, New Zealand
| | - Michael Bunce
- Institute of Environmental Science and Research, Wellington, New Zealand
| | - Nigel French
- Tāwharau Ora/School of Veterinary Science, Massey University, Palmerston North, New Zealand
- Te Niwha, Infectious Diseases Research Platform, Institute of Environmental Science and Research, Palmerston North, New Zealand
| | - James Hadfield
- Fred Hutchinson Cancer Research Centre, Seattle, WA, USA
| | - Joep de Ligt
- Institute of Environmental Science and Research, Wellington, New Zealand
| | - David Welch
- Centre for Computational Evolution,School of Computer Science, University of Auckland, Auckland, New Zealand
| | - Jemma L Geoghegan
- Institute of Environmental Science and Research, Wellington, New Zealand
- Department of Microbiology and Immunology, University of Otago, Dunedin, New Zealand
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12
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Calderón Peralvo F, Cazorla Vanegas P, Avila-Ordóñez E. A systematic review of COVID-19 transport policies and mitigation strategies around the globe. TRANSPORTATION RESEARCH INTERDISCIPLINARY PERSPECTIVES 2022; 15:100653. [PMID: 35873107 PMCID: PMC9289094 DOI: 10.1016/j.trip.2022.100653] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 07/04/2022] [Accepted: 07/14/2022] [Indexed: 05/10/2023]
Abstract
This paper reports a Scopus-based systematic literature review of a wide variety of transportation policies and mitigation strategies that have been conducted around the world to minimize COVID-19 contagion risk in transportation systems. The review offers a representative coverage of countries across all continents of the planet, as well as among representative climate regions - as weather is an important factor to consider. The readership interested in policies and mitigation strategies is expected to involve a wide range of actors, each involving a particular application context; hence, the literature is also characterized by key attributes such as: transportation mode; actor (users, operators, government, industry); jurisdiction (national, provincial, city, neighborhood); and area of application (planning, regulation, operations, research, incentives). An in-depth analysis of the surveyed literature is then reported, focusing first on condensing the literature into 151 distinct policies and strategies, which are subsequently categorized into 25 broad categories that are discussed at length. The compendium and discussion of strategies and policies reported not only provide comprehensive guidelines to inform various courses of action for decision-makers, planners, and social communicators, but also emphasize on future work and the potential of some of these strategies to be the precursors of meaningful, more sustainable behavioral changes in future mobility patterns.
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Affiliation(s)
- Francisco Calderón Peralvo
- Research Group "Models, Analysis and Simulation (MAS) Applied to Transport Systems", Computer Science Department, University of Cuenca, Ecuador
| | - Patricia Cazorla Vanegas
- Research Group "Models, Analysis and Simulation (MAS) Applied to Transport Systems", Computer Science Department, University of Cuenca, Ecuador
| | - Elina Avila-Ordóñez
- Research Group "Models, Analysis and Simulation (MAS) Applied to Transport Systems", Computer Science Department, University of Cuenca, Ecuador
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13
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Attwood SW, Hill SC, Aanensen DM, Connor TR, Pybus OG. Phylogenetic and phylodynamic approaches to understanding and combating the early SARS-CoV-2 pandemic. Nat Rev Genet 2022; 23:547-562. [PMID: 35459859 PMCID: PMC9028907 DOI: 10.1038/s41576-022-00483-8] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/23/2022] [Indexed: 01/05/2023]
Abstract
Determining the transmissibility, prevalence and patterns of movement of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections is central to our understanding of the impact of the pandemic and to the design of effective control strategies. Phylogenies (evolutionary trees) have provided key insights into the international spread of SARS-CoV-2 and enabled investigation of individual outbreaks and transmission chains in specific settings. Phylodynamic approaches combine evolutionary, demographic and epidemiological concepts and have helped track virus genetic changes, identify emerging variants and inform public health strategy. Here, we review and synthesize studies that illustrate how phylogenetic and phylodynamic techniques were applied during the first year of the pandemic, and summarize their contributions to our understanding of SARS-CoV-2 transmission and control.
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Affiliation(s)
- Stephen W Attwood
- Department of Zoology, University of Oxford, Oxford, UK.
- Pathogen Genomics Unit, Public Health Wales NHS Trust, Cardiff, UK.
| | - Sarah C Hill
- Department of Pathobiology and Population Sciences, Royal Veterinary College, University of London, London, UK
| | - David M Aanensen
- Centre for Genomic Pathogen Surveillance, Wellcome Genome Campus, Hinxton, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Thomas R Connor
- Pathogen Genomics Unit, Public Health Wales NHS Trust, Cardiff, UK
- School of Biosciences, Cardiff University, Cardiff, UK
| | - Oliver G Pybus
- Department of Zoology, University of Oxford, Oxford, UK.
- Department of Pathobiology and Population Sciences, Royal Veterinary College, University of London, London, UK.
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14
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Gao J, May MR, Rannala B, Moore BR. New Phylogenetic Models Incorporating Interval-Specific Dispersal Dynamics Improve Inference of Disease Spread. Mol Biol Evol 2022; 39:msac159. [PMID: 35861314 PMCID: PMC9384482 DOI: 10.1093/molbev/msac159] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Phylodynamic methods reveal the spatial and temporal dynamics of viral geographic spread, and have featured prominently in studies of the COVID-19 pandemic. Virtually all such studies are based on phylodynamic models that assume-despite direct and compelling evidence to the contrary-that rates of viral geographic dispersal are constant through time. Here, we: (1) extend phylodynamic models to allow both the average and relative rates of viral dispersal to vary independently between pre-specified time intervals; (2) implement methods to infer the number and timing of viral dispersal events between areas; and (3) develop statistics to assess the absolute fit of discrete-geographic phylodynamic models to empirical datasets. We first validate our new methods using simulations, and then apply them to a SARS-CoV-2 dataset from the early phase of the COVID-19 pandemic. We show that: (1) under simulation, failure to accommodate interval-specific variation in the study data will severely bias parameter estimates; (2) in practice, our interval-specific discrete-geographic phylodynamic models can significantly improve the relative and absolute fit to empirical data; and (3) the increased realism of our interval-specific models provides qualitatively different inferences regarding key aspects of the COVID-19 pandemic-revealing significant temporal variation in global viral dispersal rates, viral dispersal routes, and the number of viral dispersal events between areas-and alters interpretations regarding the efficacy of intervention measures to mitigate the pandemic.
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Affiliation(s)
- Jiansi Gao
- Department of Evolution and Ecology, University of California, Storer Hall, Davis, CA 95616, USA
| | - Michael R May
- Department of Evolution and Ecology, University of California, Storer Hall, Davis, CA 95616, USA
- Department of Integrative Biology, University of California, 3060 VLSB, Berkeley, CA 94720-3140, USA
| | - Bruce Rannala
- Department of Evolution and Ecology, University of California, Storer Hall, Davis, CA 95616, USA
| | - Brian R Moore
- Department of Evolution and Ecology, University of California, Storer Hall, Davis, CA 95616, USA
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15
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Minimising the use of costly control measures in an epidemic elimination strategy: A simple mathematical model. Math Biosci 2022; 351:108885. [PMID: 35907510 PMCID: PMC9327244 DOI: 10.1016/j.mbs.2022.108885] [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: 04/04/2022] [Revised: 07/20/2022] [Accepted: 07/21/2022] [Indexed: 12/02/2022]
Abstract
Countries such as New Zealand, Australia and Taiwan responded to the Covid-19 pandemic with an elimination strategy. This involves a combination of strict border controls with a rapid and effective response to eliminate border-related re-introductions. An important question for decision makers is, when there is a new re-introduction, what is the right threshold at which to implement strict control measures designed to reduce the effective reproduction number below 1. Since it is likely that there will be multiple re-introductions, responding at too low a threshold may mean repeatedly implementing controls unnecessarily for outbreaks that would self-eliminate even without control measures. On the other hand, waiting for too high a threshold to be reached creates a risk that controls will be needed for a longer period of time, or may completely fail to contain the outbreak. Here, we use a highly idealised branching process model of small border-related outbreaks to address this question. We identify important factors that affect the choice of threshold in order to minimise the expect time period for which control measures are in force. We find that the optimal threshold for introducing controls decreases with the effective reproduction number, and increases with overdispersion of the offspring distribution and with the effectiveness of control measures. Our results are not intended as a quantitative decision-making algorithm. However, they may help decision makers understand when a wait-and-see approach is likely to be preferable over an immediate response.
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16
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Jelley L, Douglas J, Ren X, Winter D, McNeill A, Huang S, French N, Welch D, Hadfield J, de Ligt J, Geoghegan JL. Genomic epidemiology of Delta SARS-CoV-2 during transition from elimination to suppression in Aotearoa New Zealand. Nat Commun 2022; 13:4035. [PMID: 35821124 PMCID: PMC9274967 DOI: 10.1038/s41467-022-31784-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 06/30/2022] [Indexed: 12/12/2022] Open
Abstract
New Zealand's COVID-19 elimination strategy heavily relied on the use of genomics to inform contact tracing, linking cases to the border and to clusters during community outbreaks. In August 2021, New Zealand entered its second nationwide lockdown after the detection of a single community case with no immediately apparent epidemiological link to the border. This incursion resulted in the largest outbreak seen in New Zealand caused by the Delta Variant of Concern. Here we generated 3806 high quality SARS-CoV-2 genomes from cases reported in New Zealand between 17 August and 1 December 2021, representing 43% of reported cases. We detected wide geographical spread coupled with undetected community transmission, characterised by the apparent extinction and reappearance of genomically linked clusters. We also identified the emergence, and near replacement, of genomes possessing a 10-nucleotide frameshift deletion that caused the likely truncation of accessory protein ORF7a. By early October, New Zealand moved from an elimination strategy to a suppression strategy and the role of genomics changed markedly from being used to track and trace, towards population-level surveillance.
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Affiliation(s)
- Lauren Jelley
- Institute of Environmental Science and Research, Wellington, New Zealand
- Department of Microbiology and Immunology, University of Otago, Dunedin, New Zealand
| | - Jordan Douglas
- Centre for Computational Evolution, School of Computer Science, University of Auckland, Auckland, New Zealand
| | - Xiaoyun Ren
- Institute of Environmental Science and Research, Wellington, New Zealand
| | - David Winter
- Institute of Environmental Science and Research, Wellington, New Zealand
| | - Andrea McNeill
- Institute of Environmental Science and Research, Wellington, New Zealand
| | - Sue Huang
- Institute of Environmental Science and Research, Wellington, New Zealand
| | - Nigel French
- Tāwharau Ora/School of Veterinary Science, Massey University, Palmerston North, New Zealand
| | - David Welch
- Department of Microbiology and Immunology, University of Otago, Dunedin, New Zealand
| | - James Hadfield
- Fred Hutchinson Cancer Research Centre, Seattle, Washington, USA
| | - Joep de Ligt
- Institute of Environmental Science and Research, Wellington, New Zealand
| | - Jemma L Geoghegan
- Institute of Environmental Science and Research, Wellington, New Zealand.
- Department of Microbiology and Immunology, University of Otago, Dunedin, New Zealand.
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17
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The End of the Elimination Strategy: Decisive Factors towards Sustainable Management of COVID-19 in New Zealand. EPIDEMIOLGIA (BASEL, SWITZERLAND) 2022; 3:135-147. [PMID: 36417272 PMCID: PMC9620908 DOI: 10.3390/epidemiologia3010011] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 03/05/2022] [Accepted: 03/14/2022] [Indexed: 12/14/2022]
Abstract
New Zealand has long been praised for the effectiveness of its COVID-19 elimination strategy. It resulted in fewer COVID-19-related deaths, better economic recovery, and less stringent policy measures within its borders compared with other OECD countries, which opted for mitigation or suppression. However, since September 2021, the rising number of infections has not been contained anymore by the contact tracing and self-isolation system in place and the government has shifted towards a policy strategy similar to suppression to manage the crisis. In this case study, we analyse the factors that led the government to switch policy and discuss why elimination became unsustainable to manage the COVID-19 epidemic in New Zealand. Results showed that the socioeconomic and political factors, along with the appearance of new variants and a delayed vaccination program, were accountable for the switch in strategy. This switch allows the country to better adapt to the evolving nature of the disease and to address the social and economic repercussions of the first year of measures. Our conclusion does not disregard elimination as an appropriate initial strategy to contain this pandemic in the absence of a vaccine or treatment, but rather suggests that borders cannot remain closed for long periods of time without creating social, economical, and political issues.
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18
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Bouckaert RR. An Efficient Coalescent Epoch Model for Bayesian Phylogenetic Inference. Syst Biol 2022; 71:1549-1560. [PMID: 35212733 PMCID: PMC9773037 DOI: 10.1093/sysbio/syac015] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 01/24/2022] [Accepted: 02/22/2022] [Indexed: 12/25/2022] Open
Abstract
We present a two-headed approach called Bayesian Integrated Coalescent Epoch PlotS (BICEPS) for efficient inference of coalescent epoch models. Firstly, we integrate out population size parameters, and secondly, we introduce a set of more powerful Markov chain Monte Carlo (MCMC) proposals for flexing and stretching trees. Even though population sizes are integrated out and not explicitly sampled through MCMC, we are still able to generate samples from the population size posteriors. This allows demographic reconstruction through time and estimating the timing and magnitude of population bottlenecks and full population histories. Altogether, BICEPS can be considered a more muscular version of the popular Bayesian skyline model. We demonstrate its power and correctness by a well-calibrated simulation study. Furthermore, we demonstrate with an application to SARS-CoV-2 genomic data that some analyses that have trouble converging with the traditional Bayesian skyline prior and standard MCMC proposals can do well with the BICEPS approach. BICEPS is available as open-source package for BEAST 2 under GPL license and has a user-friendly graphical user interface.[Bayesian phylogenetics; BEAST 2; BICEPS; coalescent model.].
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Affiliation(s)
- Remco R Bouckaert
- Correspondence to be sent to: University of Auckland, Thomas
Building, Room 407 3 Symonds St Auckland 1010 New Zealand E-mail:
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19
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Osnes MN, Alfsnes K, Bråte J, Garcia I, Riis RK, Instefjord KH, Elshaug H, Vollan HS, Moen LV, Pedersen BN, Caugant DA, Stene-Johansen K, Hungnes O, Bragstad K, Brynildsrud O, Eldholm V. The impact of global lineage dynamics, border restrictions, and emergence of the B.1.1.7 lineage on the SARS-CoV-2 epidemic in Norway. Virus Evol 2021; 7:veab086. [PMID: 34659798 PMCID: PMC8516819 DOI: 10.1093/ve/veab086] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 09/10/2021] [Accepted: 09/22/2021] [Indexed: 01/08/2023] Open
Abstract
As the COVID-19 pandemic swept through an immunologically naïve human population, academics and public health professionals scrambled to establish methods and platforms for genomic surveillance and data sharing. This offered a rare opportunity to study the ecology and evolution of SARS-CoV-2 over the course of the ongoing pandemic. Here, we use population genetic and phylogenetic methodology to characterize the population dynamics of SARS-CoV-2 and reconstruct patterns of virus introductions and local transmission in Norway against this backdrop. The analyses demonstrated that the epidemic in Norway was largely import driven and characterized by the repeated introduction, establishment, and suppression of new transmission lineages. This pattern changed with the arrival of the B.1.1.7 lineage, which was able to establish a stable presence concomitant with the imposition of severe border restrictions.
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Affiliation(s)
- Magnus N Osnes
- Division of Infectious Disease Control and Environmental Health, Norwegian Institute of Public Health, Lovisenberggata 6, Oslo 0456, Norway
| | - Kristian Alfsnes
- Division of Infectious Disease Control and Environmental Health, Norwegian Institute of Public Health, Lovisenberggata 6, Oslo 0456, Norway
| | - Jon Bråte
- Division of Infectious Disease Control and Environmental Health, Norwegian Institute of Public Health, Lovisenberggata 6, Oslo 0456, Norway
| | - Ignacio Garcia
- Division of Infectious Disease Control and Environmental Health, Norwegian Institute of Public Health, Lovisenberggata 6, Oslo 0456, Norway
| | - Rasmus K Riis
- Division of Infectious Disease Control and Environmental Health, Norwegian Institute of Public Health, Lovisenberggata 6, Oslo 0456, Norway
| | - Kamilla H Instefjord
- Division of Infectious Disease Control and Environmental Health, Norwegian Institute of Public Health, Lovisenberggata 6, Oslo 0456, Norway
| | - Hilde Elshaug
- Division of Infectious Disease Control and Environmental Health, Norwegian Institute of Public Health, Lovisenberggata 6, Oslo 0456, Norway
| | - Hilde S Vollan
- Division of Infectious Disease Control and Environmental Health, Norwegian Institute of Public Health, Lovisenberggata 6, Oslo 0456, Norway
| | - Line Victoria Moen
- Division of Infectious Disease Control and Environmental Health, Norwegian Institute of Public Health, Lovisenberggata 6, Oslo 0456, Norway
| | - Benedikte Nevjen Pedersen
- Division of Infectious Disease Control and Environmental Health, Norwegian Institute of Public Health, Lovisenberggata 6, Oslo 0456, Norway
| | - Dominique A Caugant
- Division of Infectious Disease Control and Environmental Health, Norwegian Institute of Public Health, Lovisenberggata 6, Oslo 0456, Norway
| | - Kathrine Stene-Johansen
- Division of Infectious Disease Control and Environmental Health, Norwegian Institute of Public Health, Lovisenberggata 6, Oslo 0456, Norway
| | - Olav Hungnes
- Division of Infectious Disease Control and Environmental Health, Norwegian Institute of Public Health, Lovisenberggata 6, Oslo 0456, Norway
| | - Karoline Bragstad
- Division of Infectious Disease Control and Environmental Health, Norwegian Institute of Public Health, Lovisenberggata 6, Oslo 0456, Norway
| | - Ola Brynildsrud
- Division of Infectious Disease Control and Environmental Health, Norwegian Institute of Public Health, Lovisenberggata 6, Oslo 0456, Norway
| | - Vegard Eldholm
- Division of Infectious Disease Control and Environmental Health, Norwegian Institute of Public Health, Lovisenberggata 6, Oslo 0456, Norway
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20
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Progress and challenges in virus genomic epidemiology. Trends Parasitol 2021; 37:1038-1049. [PMID: 34620561 DOI: 10.1016/j.pt.2021.08.007] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 08/24/2021] [Accepted: 08/26/2021] [Indexed: 12/18/2022]
Abstract
Genomic epidemiology, which links pathogen genomes with associated metadata to understand disease transmission, has become a key component of outbreak response. Decreasing costs of genome sequencing and increasing computational power provide opportunities to generate and analyse large viral genomic datasets that aim to uncover the spatial scales of transmission, the demographics contributing to transmission patterns, and to forecast epidemic trends. Emerging sources of genomic data and associated metadata provide new opportunities to further unravel transmission patterns. Key challenges include how to integrate genomic data with metadata from multiple sources, how to generate efficient computational algorithms to cope with large datasets, and how to establish sampling frameworks to enable robust conclusions.
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21
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Douglas J, Geoghegan JL, Hadfield J, Bouckaert R, Storey M, Ren X, de Ligt J, French N, Welch D. Real-Time Genomics for Tracking Severe Acute Respiratory Syndrome Coronavirus 2 Border Incursions after Virus Elimination, New Zealand. Emerg Infect Dis 2021; 27:2361-2368. [PMID: 34424164 PMCID: PMC8386796 DOI: 10.3201/eid2709.211097] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Since severe acute respiratory syndrome coronavirus 2 was first eliminated in New Zealand in May 2020, a total of 13 known coronavirus disease (COVID-19) community outbreaks have occurred, 2 of which led health officials to issue stay-at-home orders. These outbreaks originated at the border via isolating returnees, airline workers, and cargo vessels. Because a public health system was informed by real-time viral genomic sequencing and complete genomes typically were available within 12 hours of community-based positive COVID-19 test results, every outbreak was well-contained. A total of 225 community cases resulted in 3 deaths. Real-time genomics were essential for establishing links between cases when epidemiologic data could not do so and for identifying when concurrent outbreaks had different origins.
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