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Cori A, Kucharski A. Inference of epidemic dynamics in the COVID-19 era and beyond. Epidemics 2024; 48:100784. [PMID: 39167954 DOI: 10.1016/j.epidem.2024.100784] [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: 03/22/2024] [Revised: 06/25/2024] [Accepted: 07/11/2024] [Indexed: 08/23/2024] Open
Abstract
The COVID-19 pandemic demonstrated the key role that epidemiology and modelling play in analysing infectious threats and supporting decision making in real-time. Motivated by the unprecedented volume and breadth of data generated during the pandemic, we review modern opportunities for analysis to address questions that emerge during a major modern epidemic. Following the broad chronology of insights required - from understanding initial dynamics to retrospective evaluation of interventions, we describe the theoretical foundations of each approach and the underlying intuition. Through a series of case studies, we illustrate real life applications, and discuss implications for future work.
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Affiliation(s)
- Anne Cori
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, United Kingdom.
| | - Adam Kucharski
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, United Kingdom.
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2
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Lin Q, Goldberg EE, Leitner T, Molina-París C, King AA, Romero-Severson EO. The Number and Pattern of Viral Genomic Reassortments are not Necessarily Identifiable from Segment Trees. Mol Biol Evol 2024; 41:msae078. [PMID: 38648521 PMCID: PMC11152448 DOI: 10.1093/molbev/msae078] [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: 09/20/2023] [Revised: 02/23/2024] [Accepted: 04/09/2024] [Indexed: 04/25/2024] Open
Abstract
Reassortment is an evolutionary process common in viruses with segmented genomes. These viruses can swap whole genomic segments during cellular co-infection, giving rise to novel progeny formed from the mixture of parental segments. Since large-scale genome rearrangements have the potential to generate new phenotypes, reassortment is important to both evolutionary biology and public health research. However, statistical inference of the pattern of reassortment events from phylogenetic data is exceptionally difficult, potentially involving inference of general graphs in which individual segment trees are embedded. In this paper, we argue that, in general, the number and pattern of reassortment events are not identifiable from segment trees alone, even with theoretically ideal data. We call this fact the fundamental problem of reassortment, which we illustrate using the concept of the "first-infection tree," a potentially counterfactual genealogy that would have been observed in the segment trees had no reassortment occurred. Further, we illustrate four additional problems that can arise logically in the inference of reassortment events and show, using simulated data, that these problems are not rare and can potentially distort our observation of reassortment even in small data sets. Finally, we discuss how existing methods can be augmented or adapted to account for not only the fundamental problem of reassortment, but also the four additional situations that can complicate the inference of reassortment.
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Affiliation(s)
- Qianying Lin
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Emma E Goldberg
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Thomas Leitner
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Carmen Molina-París
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Aaron A King
- Department of Ecology & Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA
- Department of Mathematics, University of Michigan, Ann Arbor, MI, USA
- Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI, USA
- Santa Fe Institute, Santa Fe, NM, USA
| | - Ethan O Romero-Severson
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM, USA
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Peng D, Mulder OJ, Edge MD. Evaluating ARG-estimation methods in the context of estimating population-mean polygenic score histories. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.24.595829. [PMID: 38854009 PMCID: PMC11160635 DOI: 10.1101/2024.05.24.595829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Scalable methods for estimating marginal coalescent trees across the genome present new opportunities for studying evolution and have generated considerable excitement, with new methods extending scalability to thousands of samples. Benchmarking of the available methods has revealed general tradeoffs between accuracy and scalability, but performance in downstream applications has not always been easily predictable from general performance measures, suggesting that specific features of the ARG may be important for specific downstream applications of estimated ARGs. To exemplify this point, we benchmark ARG estimation methods with respect to a specific set of methods for estimating the historical time course of a population-mean polygenic score (PGS) using the marginal coalescent trees encoded by the ancestral recombination graph (ARG). Here we examine the performance in simulation of six ARG estimation methods: ARGweaver, RENT+, Relate, tsinfer+tsdate, ARG-Needle/ASMC-clust , and SINGER , using their estimated coalescent trees and examining bias, mean squared error (MSE), confidence interval coverage, and Type I and II error rates of the downstream methods. Although it does not scale to the sample sizes attainable by other new methods, SINGER produced the most accurate estimated PGS histories in many instances, even when Relate, tsinfer+tsdate , and ARG-Needle/ASMC-clust used samples ten times as large as those used by SINGER. In general, the best choice of method depends on the number of samples available and the historical time period of interest. In particular, the unprecedented sample sizes allowed by Relate, tsinfer+tsdate , and ARG-Needle/ASMC-clust are of greatest importance when the recent past is of interest-further back in time, most of the tree has coalesced, and differences in contemporary sample size are less salient.
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4
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King AA, Lin Q, Ionides EL. EXACT PHYLODYNAMIC LIKELIHOOD VIA STRUCTURED MARKOV GENEALOGY PROCESSES. ARXIV 2024:arXiv:2405.17032v1. [PMID: 38855555 PMCID: PMC11160859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
We consider genealogies arising from a Markov population process in which individuals are categorized into a discrete collection of compartments, with the requirement that individuals within the same compartment are statistically exchangeable. When equipped with a sampling process, each such population process induces a time-evolving tree-valued process defined as the genealogy of all sampled individuals. We provide a construction of this genealogy process and derive exact expressions for the likelihood of an observed genealogy in terms of filter equations. These filter equations can be numerically solved using standard Monte Carlo integration methods. Thus, we obtain statistically efficient likelihood-based inference for essentially arbitrary compartment models based on an observed genealogy of individuals sampled from the population.
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Affiliation(s)
- Aaron A King
- Department of Ecology & Evolutionary Biology, Center for the Study of Complex Systems, and Department of Mathematics, University of Michigan, Ann Arbor, MI 48109 USA and Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501 USA
| | - Qianying Lin
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM 87545 USA
| | - Edward L Ionides
- Department of Statistics, University of Michigan, Ann Arbor, MI 48109 USA
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5
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Yu Q, Ascensao JA, Okada T, Boyd O, Volz E, Hallatschek O. Lineage frequency time series reveal elevated levels of genetic drift in SARS-CoV-2 transmission in England. PLoS Pathog 2024; 20:e1012090. [PMID: 38620033 PMCID: PMC11045146 DOI: 10.1371/journal.ppat.1012090] [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] [Received: 04/27/2023] [Revised: 04/25/2024] [Accepted: 03/03/2024] [Indexed: 04/17/2024] Open
Abstract
Genetic drift in infectious disease transmission results from randomness of transmission and host recovery or death. The strength of genetic drift for SARS-CoV-2 transmission is expected to be high due to high levels of superspreading, and this is expected to substantially impact disease epidemiology and evolution. However, we don't yet have an understanding of how genetic drift changes over time or across locations. Furthermore, noise that results from data collection can potentially confound estimates of genetic drift. To address this challenge, we develop and validate a method to jointly infer genetic drift and measurement noise from time-series lineage frequency data. Our method is highly scalable to increasingly large genomic datasets, which overcomes a limitation in commonly used phylogenetic methods. We apply this method to over 490,000 SARS-CoV-2 genomic sequences from England collected between March 2020 and December 2021 by the COVID-19 Genomics UK (COG-UK) consortium and separately infer the strength of genetic drift for pre-B.1.177, B.1.177, Alpha, and Delta. We find that even after correcting for measurement noise, the strength of genetic drift is consistently, throughout time, higher than that expected from the observed number of COVID-19 positive individuals in England by 1 to 3 orders of magnitude, which cannot be explained by literature values of superspreading. Our estimates of genetic drift suggest low and time-varying establishment probabilities for new mutations, inform the parametrization of SARS-CoV-2 evolutionary models, and motivate future studies of the potential mechanisms for increased stochasticity in this system.
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Affiliation(s)
- QinQin Yu
- Department of Physics, University of California, Berkeley, California, United States of America
| | - Joao A. Ascensao
- Department of Bioengineering, University of California, Berkeley, California, United States of America
| | - Takashi Okada
- Department of Physics, University of California, Berkeley, California, United States of America
- Department of Integrative Biology, University of California, Berkeley, California, United States of America
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, Japan
- RIKEN iTHEMS, Wako, Saitama, Japan
| | | | - Olivia Boyd
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Erik Volz
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Oskar Hallatschek
- Department of Physics, University of California, Berkeley, California, United States of America
- Department of Integrative Biology, University of California, Berkeley, California, United States of America
- Peter Debye Institute for Soft Matter Physics, Leipzig University, Leipzig, Germany
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6
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Müller NF, Bouckaert RR, Wu CH, Bedford T. MASCOT-Skyline integrates population and migration dynamics to enhance phylogeographic reconstructions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.06.583734. [PMID: 38496513 PMCID: PMC10942421 DOI: 10.1101/2024.03.06.583734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
The spread of infectious diseases is shaped by spatial and temporal aspects, such as host population structure or changes in the transmission rate or number of infected individuals over time. These spatiotemporal dynamics are imprinted in the genome of pathogens and can be recovered from those genomes using phylodynamics methods. However, phylodynamic methods typically quantify either the temporal or spatial transmission dynamics, which leads to unclear biases, as one can potentially not be inferred without the other. Here, we address this challenge by introducing a structured coalescent skyline approach, MASCOT-Skyline that allows us to jointly infer spatial and temporal transmission dynamics of infectious diseases using Markov chain Monte Carlo inference. To do so, we model the effective population size dynamics in different locations using a non-parametric function, allowing us to approximate a range of population size dynamics. We show, using a range of different viral outbreak datasets, potential issues with phylogeographic methods. We then use these viral datasets to motivate simulations of outbreaks that illuminate the nature of biases present in the different phylogeographic methods. We show that spatial and temporal dynamics should be modeled jointly even if one seeks to recover just one of the two. Further, we showcase conditions under which we can expect phylogeographic analyses to be biased, particularly different subsampling approaches, as well as provide recommendations of when we can expect them to perform well. We implemented MASCOT-Skyline as part of the open-source software package MASCOT for the Bayesian phylodynamics platform BEAST2.
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Affiliation(s)
- Nicola F. Müller
- Division of HIV, ID and Global Medicine, University of California San Francisco, San Francisco, USA
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, USA
| | - Remco R. Bouckaert
- Centre for Computational Evolution, The University of Auckland, New Zealand
| | - Chieh-Hsi Wu
- School of Mathematical Sciences, University of Southampton, UK
| | - Trevor Bedford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, USA
- Howard Hughes Medical Institute, Seattle, USA
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Zhukova A, Hecht F, Maday Y, Gascuel O. Fast and Accurate Maximum-Likelihood Estimation of Multi-Type Birth-Death Epidemiological Models from Phylogenetic Trees. Syst Biol 2023; 72:1387-1402. [PMID: 37703335 PMCID: PMC10924745 DOI: 10.1093/sysbio/syad059] [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: 12/02/2022] [Revised: 09/05/2023] [Accepted: 09/07/2023] [Indexed: 09/15/2023] Open
Abstract
Multi-type birth-death (MTBD) models are phylodynamic analogies of compartmental models in classical epidemiology. They serve to infer such epidemiological parameters as the average number of secondary infections Re and the infectious time from a phylogenetic tree (a genealogy of pathogen sequences). The representatives of this model family focus on various aspects of pathogen epidemics. For instance, the birth-death exposed-infectious (BDEI) model describes the transmission of pathogens featuring an incubation period (when there is a delay between the moment of infection and becoming infectious, as for Ebola and SARS-CoV-2), and permits its estimation along with other parameters. With constantly growing sequencing data, MTBD models should be extremely useful for unravelling information on pathogen epidemics. However, existing implementations of these models in a phylodynamic framework have not yet caught up with the sequencing speed. Computing time and numerical instability issues limit their applicability to medium data sets (≤ 500 samples), while the accuracy of estimations should increase with more data. We propose a new highly parallelizable formulation of ordinary differential equations for MTBD models. We also extend them to forests to represent situations when a (sub-)epidemic started from several cases (e.g., multiple introductions to a country). We implemented it for the BDEI model in a maximum likelihood framework using a combination of numerical analysis methods for efficient equation resolution. Our implementation estimates epidemiological parameter values and their confidence intervals in two minutes on a phylogenetic tree of 10,000 samples. Comparison to the existing implementations on simulated data shows that it is not only much faster but also more accurate. An application of our tool to the 2014 Ebola epidemic in Sierra-Leone is also convincing, with very fast calculation and precise estimates. As MTBD models are closely related to Cladogenetic State Speciation and Extinction (ClaSSE)-like models, our findings could also be easily transferred to the macroevolution domain.
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Affiliation(s)
- Anna Zhukova
- Unité Bioinformatique Evolutive, Institut Pasteur, Université de Paris, 28 rue du docteur Roux, 75015 Paris, France
- Bioinformatics and Biostatistics Hub, Institut Pasteur, Université de Paris, 28 rue du docteur Roux, 75015 Paris, France
| | - Frédéric Hecht
- Sorbonne Université, CNRS, Université Paris Cité, Laboratoire Jacques-Louis Lions (LJLL), 4 place Jussieu, F-75005 Paris, France
| | - Yvon Maday
- Sorbonne Université, CNRS, Université Paris Cité, Laboratoire Jacques-Louis Lions (LJLL), 4 place Jussieu, F-75005 Paris, France
- Institut Universitaire de France, 1 rue Descartes, 75231 Paris CEDEX 05, France
| | - Olivier Gascuel
- Unité Bioinformatique Evolutive, Institut Pasteur, Université de Paris, 28 rue du docteur Roux, 75015 Paris, France
- Institut de Systématique, Evolution, Biodiversité (ISYEB) - URM 7205 CNRS, Museum National d’Histoire Naturelle, SU, EPHE & UA, 57 rue Cuvier, CP 50 75005 Paris, France
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8
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Rothstein AP, Jesser KJ, Feistel DJ, Konstantinidis KT, Trueba G, Levy K. Population genomics of diarrheagenic Escherichia coli uncovers high connectivity between urban and rural communities in Ecuador. INFECTION, GENETICS AND EVOLUTION : JOURNAL OF MOLECULAR EPIDEMIOLOGY AND EVOLUTIONARY GENETICS IN INFECTIOUS DISEASES 2023; 113:105476. [PMID: 37392822 PMCID: PMC10599324 DOI: 10.1016/j.meegid.2023.105476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 05/11/2023] [Accepted: 06/28/2023] [Indexed: 07/03/2023]
Abstract
Human movement may be an important driver of transmission dynamics for enteric pathogens but has largely been underappreciated except for international 'travelers' diarrhea or cholera. Phylodynamic methods, which combine genomic and epidemiological data, are used to examine rates and dynamics of disease matching underlying evolutionary history and biogeographic distributions, but these methods often are not applied to enteric bacterial pathogens. We used phylodynamics to explore the phylogeographic and evolutionary patterns of diarrheagenic E. coli in northern Ecuador to investigate the role of human travel in the geographic distribution of strains across the country. Using whole genome sequences of diarrheagenic E. coli isolates, we built a core genome phylogeny, reconstructed discrete ancestral states across urban and rural sites, and estimated migration rates between E. coli populations. We found minimal structuring based on site locations, urban vs. rural locality, pathotype, or clinical status. Ancestral states of phylogenomic nodes and tips were inferred to have 51% urban ancestry and 49% rural ancestry. Lack of structuring by location or pathotype E. coli isolates imply highly connected communities and extensive sharing of genomic characteristics across isolates. Using an approximate structured coalescent model, we estimated rates of migration among circulating isolates were 6.7 times larger for urban towards rural populations compared to rural towards urban populations. This suggests increased inferred migration rates of diarrheagenic E. coli from urban populations towards rural populations. Our results indicate that investments in water and sanitation prevention in urban areas could limit the spread of enteric bacterial pathogens among rural populations.
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Affiliation(s)
- Andrew P. Rothstein
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, WA, USA
| | - Kelsey J. Jesser
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, WA, USA
| | - Dorian J. Feistel
- School of a Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA
| | - Konstantinos T. Konstantinidis
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA
- School of a Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA
| | - Gabriel Trueba
- Instituto de Microbiología, Colegio de Ciencias Biológicas y Ambientales, Universidad San Francisco de Quito, Quito, Pichincha, Ecuador
| | - Karen Levy
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, WA, USA
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9
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Helekal D, Keeling M, Grad YH, Didelot X. Estimating the fitness cost and benefit of antimicrobial resistance from pathogen genomic data. J R Soc Interface 2023; 20:20230074. [PMID: 37312496 PMCID: PMC10265023 DOI: 10.1098/rsif.2023.0074] [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] [Accepted: 05/22/2023] [Indexed: 06/15/2023] Open
Abstract
Increasing levels of antibiotic resistance in many bacterial pathogen populations are a major threat to public health. Resistance to an antibiotic provides a fitness benefit when the bacteria are exposed to this antibiotic, but resistance also often comes at a cost to the resistant pathogen relative to susceptible counterparts. We lack a good understanding of these benefits and costs of resistance for many bacterial pathogens and antibiotics, but estimating them could lead to better use of antibiotics in a way that reduces or prevents the spread of resistance. Here, we propose a new model for the joint epidemiology of susceptible and resistant variants, which includes explicit parameters for the cost and benefit of resistance. We show how Bayesian inference can be performed under this model using phylogenetic data from susceptible and resistant lineages and that by combining data from both we are able to disentangle and estimate the resistance cost and benefit parameters separately. We applied our inferential methodology to several simulated datasets to demonstrate good scalability and accuracy. We analysed a dataset of Neisseria gonorrhoeae genomes collected between 2000 and 2013 in the USA. We found that two unrelated lineages resistant to fluoroquinolones shared similar epidemic dynamics and resistance parameters. Fluoroquinolones were abandoned for the treatment of gonorrhoea due to increasing levels of resistance, but our results suggest that they could be used to treat a minority of around 10% of cases without causing resistance to grow again.
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Affiliation(s)
- David Helekal
- Centre for Doctoral Training in Mathematics for Real-World Systems, University of Warwick, Coventry, UK
| | - Matt Keeling
- Mathematics Institute and School of Life Sciences, University of Warwick, Coventry, UK
| | - Yonatan H. Grad
- Department of Immunology and Infectious Diseases, TH Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Xavier Didelot
- School of Life Sciences and Department of Statistics, University of Warwick, Coventry, UK
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10
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Didelot X, Franceschi V, Frost SDW, Dennis A, Volz EM. Model design for nonparametric phylodynamic inference and applications to pathogen surveillance. Virus Evol 2023; 9:vead028. [PMID: 37229349 PMCID: PMC10205094 DOI: 10.1093/ve/vead028] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 04/17/2023] [Accepted: 04/26/2023] [Indexed: 05/27/2023] Open
Abstract
Inference of effective population size from genomic data can provide unique information about demographic history and, when applied to pathogen genetic data, can also provide insights into epidemiological dynamics. The combination of nonparametric models for population dynamics with molecular clock models which relate genetic data to time has enabled phylodynamic inference based on large sets of time-stamped genetic sequence data. The methodology for nonparametric inference of effective population size is well-developed in the Bayesian setting, but here we develop a frequentist approach based on nonparametric latent process models of population size dynamics. We appeal to statistical principles based on out-of-sample prediction accuracy in order to optimize parameters that control shape and smoothness of the population size over time. Our methodology is implemented in a new R package entitled mlesky. We demonstrate the flexibility and speed of this approach in a series of simulation experiments and apply the methodology to a dataset of HIV-1 in the USA. We also estimate the impact of non-pharmaceutical interventions for COVID-19 in England using thousands of SARS-CoV-2 sequences. By incorporating a measure of the strength of these interventions over time within the phylodynamic model, we estimate the impact of the first national lockdown in the UK on the epidemic reproduction number.
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Affiliation(s)
- Xavier Didelot
- School of Life Sciences and Department of Statistics, University of Warwick, United Kingdom
| | - Vinicius Franceschi
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, United Kingdom
| | | | - Ann Dennis
- Department of Medicine, University of North Carolina, USA
| | - Erik M Volz
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, United Kingdom
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11
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Cappello L, Kim J, Palacios JA. adaPop: Bayesian inference of dependent population dynamics in coalescent models. PLoS Comput Biol 2023; 19:e1010897. [PMID: 36940209 PMCID: PMC10063170 DOI: 10.1371/journal.pcbi.1010897] [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] [Received: 07/27/2022] [Revised: 03/30/2023] [Accepted: 01/25/2023] [Indexed: 03/21/2023] Open
Abstract
The coalescent is a powerful statistical framework that allows us to infer past population dynamics leveraging the ancestral relationships reconstructed from sampled molecular sequence data. In many biomedical applications, such as in the study of infectious diseases, cell development, and tumorgenesis, several distinct populations share evolutionary history and therefore become dependent. The inference of such dependence is a highly important, yet a challenging problem. With advances in sequencing technologies, we are well positioned to exploit the wealth of high-resolution biological data for tackling this problem. Here, we present adaPop, a probabilistic model to estimate past population dynamics of dependent populations and to quantify their degree of dependence. An essential feature of our approach is the ability to track the time-varying association between the populations while making minimal assumptions on their functional shapes via Markov random field priors. We provide nonparametric estimators, extensions of our base model that integrate multiple data sources, and fast scalable inference algorithms. We test our method using simulated data under various dependent population histories and demonstrate the utility of our model in shedding light on evolutionary histories of different variants of SARS-CoV-2.
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Affiliation(s)
- Lorenzo Cappello
- Departments of Economics and Business, Universitat Pompeu Fabra, Barcelona, Spain
| | - Jaehee Kim
- Department of Computational Biology, Cornell University, Ithaca, New York, United States of America
| | - Julia A. Palacios
- Departments of Statistics and Biomedical Data Science, Stanford University, Stanford, California, United States of America
- * E-mail:
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12
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Tang M, Dudas G, Bedford T, Minin VN. Fitting stochastic epidemic models to gene genealogies using linear noise approximation. Ann Appl Stat 2023. [DOI: 10.1214/21-aoas1583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- Mingwei Tang
- Department of Statistics, University of Washington, Seattle
| | - Gytis Dudas
- Gothenburg Global Biodiversity Centre (GGBC)
| | - Trevor Bedford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center
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13
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Duvvuri VR, Hicks JT, Damodaran L, Grunnill M, Braukmann T, Wu J, Gubbay JB, Patel SN, Bahl J. Comparing the transmission potential from sequence and surveillance data of 2009 North American influenza pandemic waves. Infect Dis Model 2023; 8:240-252. [PMID: 36844759 PMCID: PMC9944206 DOI: 10.1016/j.idm.2023.02.003] [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: 12/02/2022] [Revised: 02/10/2023] [Accepted: 02/15/2023] [Indexed: 02/18/2023] Open
Abstract
Technological advancements in phylodynamic modeling coupled with the accessibility of real-time pathogen genetic data are increasingly important for understanding the infectious disease transmission dynamics. In this study, we compare the transmission potentials of North American influenza A(H1N1)pdm09 derived from sequence data to that derived from surveillance data. The impact of the choice of tree-priors, informative epidemiological priors, and evolutionary parameters on the transmission potential estimation is evaluated. North American Influenza A(H1N1)pdm09 hemagglutinin (HA) gene sequences are analyzed using the coalescent and birth-death tree prior models to estimate the basic reproduction number (R 0 ). Epidemiological priors gathered from published literature are used to simulate the birth-death skyline models. Path-sampling marginal likelihood estimation is conducted to assess model fit. A bibliographic search to gather surveillance-based R 0 values were consistently lower (mean ≤ 1.2) when estimated by coalescent models than by the birth-death models with informative priors on the duration of infectiousness (mean ≥ 1.3 to ≤2.88 days). The user-defined informative priors for use in the birth-death model shift the directionality of epidemiological and evolutionary parameters compared to non-informative estimates. While there was no certain impact of clock rate and tree height on the R 0 estimation, an opposite relationship was observed between coalescent and birth-death tree priors. There was no significant difference (p = 0.46) between the birth-death model and surveillance R 0 estimates. This study concludes that tree-prior methodological differences may have a substantial impact on the transmission potential estimation as well as the evolutionary parameters. The study also reports a consensus between the sequence-based R 0 estimation and surveillance-based R 0 estimates. Altogether, these outcomes shed light on the potential role of phylodynamic modeling to augment existing surveillance and epidemiological activities to better assess and respond to emerging infectious diseases.
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Affiliation(s)
- Venkata R. Duvvuri
- Public Health Ontario, Toronto, Ontario, Canada,Department of Laboratory Medicine and Pathobiology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada,Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada,Center for the Ecology of Infectious Disease, Department of Infectious Diseases, Institute of Bioinformatics, University of Georgia, Athens, Georgia,Department of Epidemiology and Biostatistics, Institute of Bioinformatics, University of Georgia, Athens, Georgia,Corresponding author. Public Health Ontario, Toronto, Ontario, Canada.
| | - Joseph T. Hicks
- Center for the Ecology of Infectious Disease, Department of Infectious Diseases, Institute of Bioinformatics, University of Georgia, Athens, Georgia,Department of Epidemiology and Biostatistics, Institute of Bioinformatics, University of Georgia, Athens, Georgia
| | - Lambodhar Damodaran
- Center for the Ecology of Infectious Disease, Department of Infectious Diseases, Institute of Bioinformatics, University of Georgia, Athens, Georgia,Department of Epidemiology and Biostatistics, Institute of Bioinformatics, University of Georgia, Athens, Georgia
| | - Martin Grunnill
- Public Health Ontario, Toronto, Ontario, Canada,Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada
| | | | - Jianhong Wu
- Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada
| | - Jonathan B. Gubbay
- Public Health Ontario, Toronto, Ontario, Canada,Department of Laboratory Medicine and Pathobiology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Samir N. Patel
- Public Health Ontario, Toronto, Ontario, Canada,Department of Laboratory Medicine and Pathobiology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Justin Bahl
- Center for the Ecology of Infectious Disease, Department of Infectious Diseases, Institute of Bioinformatics, University of Georgia, Athens, Georgia,Department of Epidemiology and Biostatistics, Institute of Bioinformatics, University of Georgia, Athens, Georgia,Duke-NUS Graduate Medical School, Singapore,Corresponding author. Center for the Ecology of Infectious Disease, Department of Infectious Diseases, Institute of Bioinformatics, University of Georgia, Athens, Georgia, USA.
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14
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Measurably recombining malaria parasites. Trends Parasitol 2023; 39:17-25. [PMID: 36435688 PMCID: PMC9893849 DOI: 10.1016/j.pt.2022.11.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 11/02/2022] [Accepted: 11/02/2022] [Indexed: 11/24/2022]
Abstract
Genomic epidemiology has guided research and policy for various viral pathogens and there has been a parallel effort towards using genomic epidemiology to combat diseases that are caused by eukaryotic pathogens, such as the malaria parasite. However, the central concept of viral genomic epidemiology, namely that of measurably mutating pathogens, does not apply easily to sexually recombining parasites. Here we introduce the related but different concept of measurably recombining malaria parasites to promote convergence around a unifying theoretical framework for malaria genomic epidemiology. Akin to viral phylodynamics, we anticipate that an inferential framework developed around recombination will help guide practical research and thus realize the full public health potential of genomic epidemiology for malaria parasites and other sexually recombining pathogens.
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15
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Stockdale JE, Liu P, Colijn C. The potential of genomics for infectious disease forecasting. Nat Microbiol 2022; 7:1736-1743. [PMID: 36266338 DOI: 10.1038/s41564-022-01233-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 08/18/2022] [Indexed: 11/09/2022]
Abstract
Genomic technologies have led to tremendous gains in understanding how pathogens function, evolve and interact. Pathogen diversity is now measurable at high precision and resolution, in part because over the past decade, sequencing technologies have increased in speed and capacity, at decreased cost. Alongside this, the use of models that can forecast emergence and size of infectious disease outbreaks has risen, highlighted by the coronavirus disease 2019 pandemic but also due to modelling advances that allow for rapid estimates in emerging outbreaks to inform monitoring, coordination and resource deployment. However, genomics studies have remained largely retrospective. While they contain high-resolution views of pathogen diversification and evolution in the context of selection, they are often not aligned with designing interventions. This is a missed opportunity because pathogen diversification is at the core of the most pressing infectious public health challenges, and interventions need to take the mechanisms of virulence and understanding of pathogen diversification into account. In this Perspective, we assess these converging fields, discuss current challenges facing both surveillance specialists and modellers who want to harness genomic data, and propose next steps for integrating longitudinally sampled genomic data with statistical learning and interpretable modelling to make reliable predictions into the future.
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Affiliation(s)
- Jessica E Stockdale
- Department of Mathematics, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Pengyu Liu
- Department of Mathematics, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Caroline Colijn
- Department of Mathematics, Simon Fraser University, Burnaby, British Columbia, Canada.
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16
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Didelot X, Parkhill J. A scalable analytical approach from bacterial genomes to epidemiology. Philos Trans R Soc Lond B Biol Sci 2022; 377:20210246. [PMID: 35989600 PMCID: PMC9393561 DOI: 10.1098/rstb.2021.0246] [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: 10/04/2021] [Accepted: 02/17/2022] [Indexed: 12/21/2022] Open
Abstract
Recent years have seen a remarkable increase in the practicality of sequencing whole genomes from large numbers of bacterial isolates. The availability of this data has huge potential to deliver new insights into the evolution and epidemiology of bacterial pathogens, but the scalability of the analytical methodology has been lagging behind that of the sequencing technology. Here we present a step-by-step approach for such large-scale genomic epidemiology analyses, from bacterial genomes to epidemiological interpretations. A central component of this approach is the dated phylogeny, which is a phylogenetic tree with branch lengths measured in units of time. The construction of dated phylogenies from bacterial genomic data needs to account for the disruptive effect of recombination on phylogenetic relationships, and we describe how this can be achieved. Dated phylogenies can then be used to perform fine-scale or large-scale epidemiological analyses, depending on the proportion of cases for which genomes are available. A key feature of this approach is computational scalability and in particular the ability to process hundreds or thousands of genomes within a matter of hours. This is a clear advantage of the step-by-step approach described here. We discuss other advantages and disadvantages of the approach, as well as potential improvements and avenues for future research. This article is part of a discussion meeting issue 'Genomic population structures of microbial pathogens'.
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Affiliation(s)
- Xavier Didelot
- School of Life Sciences and Department of Statistics, University of Warwick, Coventry CV4 7AL, UK
| | - Julian Parkhill
- Department of Veterinary Medicine, University of Cambridge, Cambridge CB3 0ES, UK
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17
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Nova N, Athni TS, Childs ML, Mandle L, Mordecai EA. Global Change and Emerging Infectious Diseases. ANNUAL REVIEW OF RESOURCE ECONOMICS 2022; 14:333-354. [PMID: 38371741 PMCID: PMC10871673 DOI: 10.1146/annurev-resource-111820-024214] [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: 02/20/2024]
Abstract
Our world is undergoing rapid planetary changes driven by human activities, often mediated by economic incentives and resource management, affecting all life on Earth. Concurrently, many infectious diseases have recently emerged or spread into new populations. Mounting evidence suggests that global change-including climate change, land-use change, urbanization, and global movement of individuals, species, and goods-may be accelerating disease emergence by reshaping ecological systems in concert with socioeconomic factors. Here, we review insights, approaches, and mechanisms by which global change drives disease emergence from a disease ecology perspective. We aim to spur more interdisciplinary collaboration with economists and identification of more effective and sustainable interventions to prevent disease emergence. While almost all infectious diseases change in response to global change, the mechanisms and directions of these effects are system specific, requiring new, integrated approaches to disease control that recognize linkages between environmental and economic sustainability and human and planetary health.
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Affiliation(s)
- Nicole Nova
- Department of Biology, Stanford University, Stanford, California, USA
| | - Tejas S Athni
- Department of Biology, Stanford University, Stanford, California, USA
| | - Marissa L Childs
- Emmett Interdisciplinary Program in Environment and Resources, Stanford University, Stanford, California, USA
| | - Lisa Mandle
- Department of Biology, Stanford University, Stanford, California, USA
- Natural Capital Project, Stanford University, Stanford, California, USA
- Woods Institute for the Environment, Stanford University, Stanford, California, USA
| | - Erin A Mordecai
- Department of Biology, Stanford University, Stanford, California, USA
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18
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Franzo G, Dundon WG, De Villiers M, De Villiers L, Coetzee LM, Khaiseb S, Cattoli G, Molini U. Phylodynamic and phylogeographic reconstruction of beak and feather disease virus epidemiology and its implications for the international exotic bird trade. Transbound Emerg Dis 2022; 69:e2677-e2687. [PMID: 35695014 PMCID: PMC9795873 DOI: 10.1111/tbed.14618] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 05/21/2022] [Accepted: 06/05/2022] [Indexed: 12/30/2022]
Abstract
Beak and feather disease virus (BFDV) infects domestic and wild psittacine species and is able to cause progressive beak, claw and feather malformation and necrosis. In addition to having an impact on the health and welfare of domesticated birds, BFDV represents a significant threat to wild endangered species. Understanding the epidemiology, dynamics, viral migration rate, interaction between wild and domestic animals and the effect of implemented control strategies is fundamental in controlling the spread of the disease. With this in mind, a phylodynamic and phylogeographic analysis has been performed on a database of more than 400 replication-associated protein (Rep) gene (ORF1) sequences downloaded from Genbank including some recently generated sequences from fifteen samples collected in Namibia. The results allowed us to reconstruct the variation of viral population size and demonstrated the effect of enforced international bans on these dynamics. A good correlation was found between viral migration rate and the intensity of animal trade between regions over time. A dominant flux of viral strains was observed from wild to domestic populations, highlighting the directionality of viral transmission and the risk associated with the capturing and trade of wild birds. Nevertheless, the flow of viruses from domestic to wild species was not negligible and should be considered as a threat to biodiversity. Therefore, considering the strong relationship demonstrated in this study between animal trade and BFDV viral fluxes more effort should be made to prevent contact opportunities between wild and domestic populations from different countries in order to control disease spread.
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Affiliation(s)
- Giovanni Franzo
- Department of Animal MedicineProduction and HealthUniversity of PadovaViale dell'UniversitàLegnaroItaly
| | - William G. Dundon
- Animal Production and Health LaboratoryAnimal Production and Health SectionJoint FAO/IAEA DivisionDepartment of Nuclear Sciences and ApplicationsInternational Atomic Energy AgencyViennaAustria
| | - Mari De Villiers
- School of Veterinary MedicineFaculty of Health Sciences and Veterinary MedicineUniversity of NamibiaNeudamm CampusWindhoekNamibia
| | - Lourens De Villiers
- School of Veterinary MedicineFaculty of Health Sciences and Veterinary MedicineUniversity of NamibiaNeudamm CampusWindhoekNamibia
| | | | | | - Giovanni Cattoli
- Animal Production and Health LaboratoryAnimal Production and Health SectionJoint FAO/IAEA DivisionDepartment of Nuclear Sciences and ApplicationsInternational Atomic Energy AgencyViennaAustria
| | - Umberto Molini
- School of Veterinary MedicineFaculty of Health Sciences and Veterinary MedicineUniversity of NamibiaNeudamm CampusWindhoekNamibia,Central Veterinary Laboratory (CVL)WindhoekNamibia
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19
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Shchur V, Spirin V, Sirotkin D, Burovski E, De Maio N, Corbett-Detig R. VGsim: Scalable viral genealogy simulator for global pandemic. PLoS Comput Biol 2022; 18:e1010409. [PMID: 36001646 PMCID: PMC9447924 DOI: 10.1371/journal.pcbi.1010409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 09/06/2022] [Accepted: 07/18/2022] [Indexed: 11/24/2022] Open
Abstract
Accurate simulation of complex biological processes is an essential component of developing and validating new technologies and inference approaches. As an effort to help contain the COVID-19 pandemic, large numbers of SARS-CoV-2 genomes have been sequenced from most regions in the world. More than 5.5 million viral sequences are publicly available as of November 2021. Many studies estimate viral genealogies from these sequences, as these can provide valuable information about the spread of the pandemic across time and space. Additionally such data are a rich source of information about molecular evolutionary processes including natural selection, for example allowing the identification of new variants with transmissibility and immunity evasion advantages. To our knowledge, there is no framework that is both efficient and flexible enough to simulate the pandemic to approximate world-scale scenarios and generate viral genealogies of millions of samples. Here, we introduce a new fast simulator VGsim which addresses the problem of simulation genealogies under epidemiological models. The simulation process is split into two phases. During the forward run the algorithm generates a chain of population-level events reflecting the dynamics of the pandemic using an hierarchical version of the Gillespie algorithm. During the backward run a coalescent-like approach generates a tree genealogy of samples conditioning on the population-level events chain generated during the forward run. Our software can model complex population structure, epistasis and immunity escape. We develop a fast and flexible simulation software package VGsim for modeling epidemiological processes and generating genealogies of large pathogen samples. The software takes into account host population structure, pathogen evolution, host immunity and some other epidemiological aspects. The computational efficiency of the package allows to simulate genealogies of tens of millions of samples, which is important, e.g., for SARS-CoV-2 genome studies.
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Affiliation(s)
- Vladimir Shchur
- International laboratory of statistical and computational genomics, HSE University, Moscow, Russia
- * E-mail:
| | - Vadim Spirin
- International laboratory of statistical and computational genomics, HSE University, Moscow, Russia
| | - Dmitry Sirotkin
- International laboratory of statistical and computational genomics, HSE University, Moscow, Russia
| | | | - Nicola De Maio
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, United Kingdom
| | - Russell Corbett-Detig
- Department of Biomolecular Engineering and Genomics Institute, UC Santa Cruz, California, United States of America
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20
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Bueno de Mesquita J. Airborne Transmission and Control of Influenza and Other Respiratory Pathogens. Infect Dis (Lond) 2022. [DOI: 10.5772/intechopen.106446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
Despite uncertainty about the specific transmission risk posed by airborne, spray-borne, and contact modes for influenza, SARS-CoV-2, and other respiratory viruses, there is evidence that airborne transmission via inhalation is important and often predominates. An early study of influenza transmission via airborne challenge quantified infectious doses as low as one influenza virion leading to illness characterized by cough and sore throat. Other studies that challenged via intranasal mucosal exposure observed high doses required for similarly symptomatic respiratory illnesses. Analysis of the Evaluating Modes of Influenza Transmission (EMIT) influenza human-challenge transmission trial—of 52 H3N2 inoculated viral donors and 75 sero-susceptible exposed individuals—quantifies airborne transmission and provides context and insight into methodology related to airborne transmission. Advances in aerosol sampling and epidemiologic studies examining the role of masking, and engineering-based air hygiene strategies provide a foundation for understanding risk and directions for new work.
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21
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Voznica J, Zhukova A, Boskova V, Saulnier E, Lemoine F, Moslonka-Lefebvre M, Gascuel O. Deep learning from phylogenies to uncover the epidemiological dynamics of outbreaks. Nat Commun 2022; 13:3896. [PMID: 35794110 PMCID: PMC9258765 DOI: 10.1038/s41467-022-31511-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 06/21/2022] [Indexed: 12/03/2022] Open
Abstract
Widely applicable, accurate and fast inference methods in phylodynamics are needed to fully profit from the richness of genetic data in uncovering the dynamics of epidemics. Standard methods, including maximum-likelihood and Bayesian approaches, generally rely on complex mathematical formulae and approximations, and do not scale with dataset size. We develop a likelihood-free, simulation-based approach, which combines deep learning with (1) a large set of summary statistics measured on phylogenies or (2) a complete and compact representation of trees, which avoids potential limitations of summary statistics and applies to any phylodynamics model. Our method enables both model selection and estimation of epidemiological parameters from very large phylogenies. We demonstrate its speed and accuracy on simulated data, where it performs better than the state-of-the-art methods. To illustrate its applicability, we assess the dynamics induced by superspreading individuals in an HIV dataset of men-having-sex-with-men in Zurich. Our tool PhyloDeep is available on github.com/evolbioinfo/phylodeep .
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Affiliation(s)
- J Voznica
- Institut Pasteur, Université Paris Cité, Unité Bioinformatique Evolutive, Paris, France.
- Université de Paris, Paris, France.
- Institut de Biologie de l'École Normale Supérieure, Ecole Normale Supérieure, CNRS, INSERM, Université Paris Sciences et Lettres, Paris, France.
| | - A Zhukova
- Institut Pasteur, Université Paris Cité, Unité Bioinformatique Evolutive, Paris, France.
- Institut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, Paris, France.
- Institut Pasteur, Université Paris Cité, Epidemiology and Modelling of Antibiotic Evasion, Paris, France.
- Université Paris-Saclay, UVSQ, Inserm, CESP, Villejuif, France.
| | - V Boskova
- Center for Integrative Bioinformatics Vienna, Max Perutz Labs, University of Vienna and Medical University of Vienna, Vienna, Austria
| | - E Saulnier
- Institut Pasteur, Université Paris Cité, Unité Bioinformatique Evolutive, Paris, France
| | - F Lemoine
- Institut Pasteur, Université Paris Cité, Unité Bioinformatique Evolutive, Paris, France
- Institut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, Paris, France
| | - M Moslonka-Lefebvre
- Institut Pasteur, Université Paris Cité, Unité Bioinformatique Evolutive, Paris, France
| | - O Gascuel
- Institut Pasteur, Université Paris Cité, Unité Bioinformatique Evolutive, Paris, France.
- Institut de Systématique, Evolution, Biodiversité (UMR 7205 - CNRS, Muséum National d'Histoire Naturelle, SU, EPHE, UA), Paris, France.
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22
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Olesen SW. Uses of mathematical modeling to estimate the impact of mass drug administration of antibiotics on antimicrobial resistance within and between communities. Infect Dis Poverty 2022; 11:75. [PMID: 35773748 PMCID: PMC9245243 DOI: 10.1186/s40249-022-00997-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 06/09/2022] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Antibiotics are a key part of modern healthcare, but their use has downsides, including selecting for antibiotic resistance, both in the individuals treated with antibiotics and in the community at large. When evaluating the benefits and costs of mass administration of azithromycin to reduce childhood mortality, effects of antibiotic use on antibiotic resistance are important but difficult to measure, especially when evaluating resistance that "spills over" from antibiotic-treated individuals to other members of their community. The aim of this scoping review was to identify how the existing literature on antibiotic resistance modeling could be better leveraged to understand the effect of mass drug administration (MDA) on antibiotic resistance. MAIN TEXT Mathematical models of antibiotic use and resistance may be useful for estimating the expected effects of different MDA implementations on different populations, as well as aiding interpretation of existing data and guiding future experimental design. Here, strengths and limitations of models of antibiotic resistance are reviewed, and possible applications of those models in the context of mass drug administration with azithromycin are discussed. CONCLUSIONS Statistical models of antibiotic use and resistance may provide robust and relevant estimates of the possible effects of MDA on resistance. Mechanistic models of resistance, while able to more precisely estimate the effects of different implementations of MDA on resistance, may require more data from MDA trials to be accurately parameterized.
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Affiliation(s)
- Scott W Olesen
- Department of Immunology and Infectious Diseases, Harvard Chan School, Boston, MA, USA.
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23
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Carson J, Ledda A, Ferretti L, Keeling M, Didelot X. The bounded coalescent model: Conditioning a genealogy on a minimum root date. J Theor Biol 2022; 548:111186. [PMID: 35697144 DOI: 10.1016/j.jtbi.2022.111186] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 05/05/2022] [Accepted: 06/02/2022] [Indexed: 01/27/2023]
Abstract
The coalescent model represents how individuals sampled from a population may have originated from a last common ancestor. The bounded coalescent model is obtained by conditioning the coalescent model such that the last common ancestor must have existed after a certain date. This conditioned model arises in a variety of applications, such as speciation, horizontal gene transfer or transmission analysis, and yet the bounded coalescent model has not been previously analysed in detail. Here we describe a new algorithm to simulate from this model directly, without resorting to rejection sampling. We show that this direct simulation algorithm is more computationally efficient than the rejection sampling approach. We also show how to calculate the probability of the last common ancestor occurring after a given date, which is required to compute the probability density of realisations under the bounded coalescent model. Our results are applicable in both the isochronous (when all samples have the same date) and heterochronous (where samples can have different dates) settings. We explore the effect of setting a bound on the date of the last common ancestor, and show that it affects a number of properties of the resulting phylogenies. All our methods are implemented in a new R package called BoundedCoalescent which is freely available online.
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Affiliation(s)
- Jake Carson
- Mathematics Institute, University of Warwick, United Kingdom
| | - Alice Ledda
- HCAI, Fungal, AMR, AMU & Sepsis Division, UK Health Security Agency, United Kingdom
| | - Luca Ferretti
- Big Data Institute, University of Oxford, United Kingdom
| | - Matt Keeling
- Mathematics Institute, University of Warwick, United Kingdom
| | - Xavier Didelot
- Department of Statistics and School of Life Sciences, University of Warwick, United Kingdom
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24
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Featherstone LA, Zhang JM, Vaughan TG, Duchene S. Epidemiological inference from pathogen genomes: A review of phylodynamic models and applications. Virus Evol 2022; 8:veac045. [PMID: 35775026 PMCID: PMC9241095 DOI: 10.1093/ve/veac045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 05/23/2022] [Accepted: 06/02/2022] [Indexed: 11/24/2022] Open
Abstract
Phylodynamics requires an interdisciplinary understanding of phylogenetics, epidemiology, and statistical inference. It has also experienced more intense application than ever before amid the SARS-CoV-2 pandemic. In light of this, we present a review of phylodynamic models beginning with foundational models and assumptions. Our target audience is public health researchers, epidemiologists, and biologists seeking a working knowledge of the links between epidemiology, evolutionary models, and resulting epidemiological inference. We discuss the assumptions linking evolutionary models of pathogen population size to epidemiological models of the infected population size. We then describe statistical inference for phylodynamic models and list how output parameters can be rearranged for epidemiological interpretation. We go on to cover more sophisticated models and finish by highlighting future directions.
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Affiliation(s)
- Leo A Featherstone
- Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC 3000, Australia
| | - Joshua M Zhang
- Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC 3000, Australia
| | - Timothy G Vaughan
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland
- Swiss Institute of Bioinformatics, Geneva 1015, Switzerland
| | - Sebastian Duchene
- Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC 3000, Australia
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25
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Cappello L, Kim J, Liu S, Palacios JA. Statistical Challenges in Tracking the Evolution of SARS-CoV-2. Stat Sci 2022; 37:162-182. [PMID: 36034090 PMCID: PMC9409356 DOI: 10.1214/22-sts853] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Genomic surveillance of SARS-CoV-2 has been instrumental in tracking the spread and evolution of the virus during the pandemic. The availability of SARS-CoV-2 molecular sequences isolated from infected individuals, coupled with phylodynamic methods, have provided insights into the origin of the virus, its evolutionary rate, the timing of introductions, the patterns of transmission, and the rise of novel variants that have spread through populations. Despite enormous global efforts of governments, laboratories, and researchers to collect and sequence molecular data, many challenges remain in analyzing and interpreting the data collected. Here, we describe the models and methods currently used to monitor the spread of SARS-CoV-2, discuss long-standing and new statistical challenges, and propose a method for tracking the rise of novel variants during the epidemic.
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Affiliation(s)
- Lorenzo Cappello
- Departments of Economics and Business, Universitat Pompeu Fabra, 08005, Spain
| | - Jaehee Kim
- Department of Computational Biology, Cornell University, Ithaca, New York 14853, USA\
| | - Sifan Liu
- Department of Statistics, Stanford University, Stanford, California 94305, USA
| | - Julia A Palacios
- Departments of Statistics and Biomedical Data Sciences, Stanford University, Stanford, California 94305, USA
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26
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Xi X, Spencer SEF, Hall M, Grabowski MK, Kagaayi J, Ratmann O. Inferring the sources of HIV infection in Africa from deep‐sequence data with semi‐parametric Bayesian Poisson flow models. J R Stat Soc Ser C Appl Stat 2022. [DOI: 10.1111/rssc.12544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Xiaoyue Xi
- Department of MathematicsImperial College London LondonUK
| | | | - Matthew Hall
- Big Data Institute, Nuffield Department of MedicineUniversity of Oxford OxfordUK
| | - M. Kate Grabowski
- Department of PathologyJohns Hopkins University BaltimoreMDUSA
- Rakai Health Sciences Program KalisizoUganda
| | - Joseph Kagaayi
- Rakai Health Sciences Program KalisizoUganda
- Makerere University School of Public Health KampalaUganda
| | - Oliver Ratmann
- Department of MathematicsImperial College London LondonUK
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27
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Evolution during primary HIV infection does not require adaptive immune selection. Proc Natl Acad Sci U S A 2022; 119:2109172119. [PMID: 35145025 PMCID: PMC8851487 DOI: 10.1073/pnas.2109172119] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/16/2021] [Indexed: 01/20/2023] Open
Abstract
Modern HIV research depends crucially on both viral sequencing and population measurements. To directly link mechanistic biological processes and evolutionary dynamics during HIV infection, we developed multiple within-host phylodynamic models of HIV primary infection for comparative validation against viral load and evolutionary dynamics data. The optimal model of primary infection required no positive selection, suggesting that the host adaptive immune system reduces viral load but surprisingly does not drive observed viral evolution. Rather, the fitness (infectivity) of mutant variants is drawn from an exponential distribution in which most variants are slightly less infectious than their parents (nearly neutral evolution). This distribution was not largely different from either in vivo fitness distributions recorded beyond primary infection or in vitro distributions that are observed without adaptive immunity, suggesting the intrinsic viral fitness distribution may drive evolution. Simulated phylogenetic trees also agree with independent data and illuminate how phylogenetic inference must consider viral and immune-cell population dynamics to gain accurate mechanistic insights.
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28
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Methods Combining Genomic and Epidemiological Data in the Reconstruction of Transmission Trees: A Systematic Review. Pathogens 2022; 11:pathogens11020252. [PMID: 35215195 PMCID: PMC8875843 DOI: 10.3390/pathogens11020252] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 02/08/2022] [Accepted: 02/11/2022] [Indexed: 11/17/2022] Open
Abstract
In order to better understand transmission dynamics and appropriately target control and preventive measures, studies have aimed to identify who-infected-whom in actual outbreaks. Numerous reconstruction methods exist, each with their own assumptions, types of data, and inference strategy. Thus, selecting a method can be difficult. Following PRISMA guidelines, we systematically reviewed the literature for methods combing epidemiological and genomic data in transmission tree reconstruction. We identified 22 methods from the 41 selected articles. We defined three families according to how genomic data was handled: a non-phylogenetic family, a sequential phylogenetic family, and a simultaneous phylogenetic family. We discussed methods according to the data needed as well as the underlying sequence mutation, within-host evolution, transmission, and case observation. In the non-phylogenetic family consisting of eight methods, pairwise genetic distances were estimated. In the phylogenetic families, transmission trees were inferred from phylogenetic trees either simultaneously (nine methods) or sequentially (five methods). While a majority of methods (17/22) modeled the transmission process, few (8/22) took into account imperfect case detection. Within-host evolution was generally (7/8) modeled as a coalescent process. These practical and theoretical considerations were highlighted in order to help select the appropriate method for an outbreak.
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29
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Tay JH, Porter AF, Wirth W, Duchene S. The Emergence of SARS-CoV-2 Variants of Concern Is Driven by Acceleration of the Substitution Rate. Mol Biol Evol 2022; 39:msac013. [PMID: 35038741 PMCID: PMC8807201 DOI: 10.1093/molbev/msac013] [Citation(s) in RCA: 57] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The ongoing SARS-CoV-2 pandemic has seen an unprecedented amount of rapidly generated genome data. These data have revealed the emergence of lineages with mutations associated to transmissibility and antigenicity, known as variants of concern (VOCs). A striking aspect of VOCs is that many of them involve an unusually large number of defining mutations. Current phylogenetic estimates of the substitution rate of SARS-CoV-2 suggest that its genome accrues around two mutations per month. However, VOCs can have 15 or more defining mutations and it is hypothesized that they emerged over the course of a few months, implying that they must have evolved faster for a period of time. We analyzed genome sequence data from the GISAID database to assess whether the emergence of VOCs can be attributed to changes in the substitution rate of the virus and whether this pattern can be detected at a phylogenetic level using genome data. We fit a range of molecular clock models and assessed their statistical performance. Our analyses indicate that the emergence of VOCs is driven by an episodic increase in the substitution rate of around 4-fold the background phylogenetic rate estimate that may have lasted several weeks or months. These results underscore the importance of monitoring the molecular evolution of the virus as a means of understanding the circumstances under which VOCs may emerge.
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Affiliation(s)
- John H Tay
- Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC, Australia
| | - Ashleigh F Porter
- Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC, Australia
| | - Wytamma Wirth
- Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC, Australia
| | - Sebastian Duchene
- Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC, Australia
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30
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Zarebski AE, du Plessis L, Parag KV, Pybus OG. A computationally tractable birth-death model that combines phylogenetic and epidemiological data. PLoS Comput Biol 2022; 18:e1009805. [PMID: 35148311 PMCID: PMC8903285 DOI: 10.1371/journal.pcbi.1009805] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 03/08/2022] [Accepted: 01/05/2022] [Indexed: 11/19/2022] Open
Abstract
Inferring the dynamics of pathogen transmission during an outbreak is an important problem in infectious disease epidemiology. In mathematical epidemiology, estimates are often informed by time series of confirmed cases, while in phylodynamics genetic sequences of the pathogen, sampled through time, are the primary data source. Each type of data provides different, and potentially complementary, insight. Recent studies have recognised that combining data sources can improve estimates of the transmission rate and the number of infected individuals. However, inference methods are typically highly specialised and field-specific and are either computationally prohibitive or require intensive simulation, limiting their real-time utility. We present a novel birth-death phylogenetic model and derive a tractable analytic approximation of its likelihood, the computational complexity of which is linear in the size of the dataset. This approach combines epidemiological and phylodynamic data to produce estimates of key parameters of transmission dynamics and the unobserved prevalence. Using simulated data, we show (a) that the approximation agrees well with existing methods, (b) validate the claim of linear complexity and (c) explore robustness to model misspecification. This approximation facilitates inference on large datasets, which is increasingly important as large genomic sequence datasets become commonplace.
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Affiliation(s)
| | - Louis du Plessis
- Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Kris Varun Parag
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom
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31
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KING AARONA, LIN QIANYING, IONIDES EDWARDL. Markov genealogy processes. Theor Popul Biol 2022; 143:77-91. [PMID: 34896438 PMCID: PMC8846264 DOI: 10.1016/j.tpb.2021.11.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 11/19/2021] [Accepted: 11/22/2021] [Indexed: 02/03/2023]
Abstract
We construct a family of genealogy-valued Markov processes that are induced by a continuous-time Markov population process. We derive exact expressions for the likelihood of a given genealogy conditional on the history of the underlying population process. These lead to a nonlinear filtering equation which can be used to design efficient Monte Carlo inference algorithms. We demonstrate these calculations with several examples. Existing full-information approaches for phylodynamic inference are special cases of the theory.
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Affiliation(s)
- AARON A. KING
- Department of Ecology & Evolutionary Biology, Center for the Study of Complex Systems, Center for Computational Medicine & Biology, and Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI 48109 USA
| | - QIANYING LIN
- Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI 48109 USA
| | - EDWARD L. IONIDES
- Department of Statistics and Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI 48109 USA
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32
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Abstract
The field of molecular epidemiology responded to the SARS-CoV-2 pandemic with an unrivaled amount of whole viral genome sequencing. By the time this sentence is published we will have well surpassed 1.5 million whole genomes, more than 4 times the number of all microbial whole genomes deposited in GenBank and 35 times the total number of viral genomes. This extraordinary dataset that accrued in near real time has also given us an opportunity to chart the global and local evolution of a virus as it moves through the world population. The data itself presents challenges that have never been dealt with in molecular epidemiology, and tracking a virus that is changing so rapidly means that we are often running to catch up. Here we review what is known about the evolution of the virus, and the critical impact that whole genomes have had on our ability to trace back and track forward the spread of lineages of SARS-CoV-2. We then review what whole genomes have told us about basic biological properties of the virus such as transmissibility, virulence, and immune escape with a special emphasis on pediatric disease. We couch this discussion within the framework of systematic biology and phylogenetics, disciplines that have proven their worth again and again for identifying and deciphering the spread of epidemics, though they were largely developed in areas far removed from infectious disease and medicine.
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Affiliation(s)
- Ahmed M Moustafa
- Division of Pediatric Infectious Diseases, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Division of Gastroenterology, Hepatology, and Nutrition, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Paul J Planet
- Division of Pediatric Infectious Diseases, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Pediatrics, Perelman College of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Sackler Institute for Comparative Genomics, American Museum of Natural History, New York, New York, USA
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33
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Chindelevitch L, Hayati M, Poon AFY, Colijn C. Network science inspires novel tree shape statistics. PLoS One 2021; 16:e0259877. [PMID: 34941890 PMCID: PMC8699983 DOI: 10.1371/journal.pone.0259877] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 10/28/2021] [Indexed: 11/18/2022] Open
Abstract
The shape of phylogenetic trees can be used to gain evolutionary insights. A tree’s shape specifies the connectivity of a tree, while its branch lengths reflect either the time or genetic distance between branching events; well-known measures of tree shape include the Colless and Sackin imbalance, which describe the asymmetry of a tree. In other contexts, network science has become an important paradigm for describing structural features of networks and using them to understand complex systems, ranging from protein interactions to social systems. Network science is thus a potential source of many novel ways to characterize tree shape, as trees are also networks. Here, we tailor tools from network science, including diameter, average path length, and betweenness, closeness, and eigenvector centrality, to summarize phylogenetic tree shapes. We thereby propose tree shape summaries that are complementary to both asymmetry and the frequencies of small configurations. These new statistics can be computed in linear time and scale well to describe the shapes of large trees. We apply these statistics, alongside some conventional tree statistics, to phylogenetic trees from three very different viruses (HIV, dengue fever and measles), from the same virus in different epidemiological scenarios (influenza A and HIV) and from simulation models known to produce trees with different shapes. Using mutual information and supervised learning algorithms, we find that the statistics adapted from network science perform as well as or better than conventional statistics. We describe their distributions and prove some basic results about their extreme values in a tree. We conclude that network science-based tree shape summaries are a promising addition to the toolkit of tree shape features. All our shape summaries, as well as functions to select the most discriminating ones for two sets of trees, are freely available as an R package at http://github.com/Leonardini/treeCentrality.
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Affiliation(s)
- Leonid Chindelevitch
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom
- * E-mail:
| | - Maryam Hayati
- School of Computing Science, Simon Fraser University, Burnaby, BC, Canada
| | - Art F. Y. Poon
- Department of Pathology & Laboratory Medicine, University of Western Ontario, London, ON, Canada
| | - Caroline Colijn
- Department of Mathematics, Simon Fraser University, Burnaby, BC, Canada
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34
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Cappello L, Palacios JA. Adaptive Preferential Sampling in Phylodynamics With an Application to SARS-CoV-2. J Comput Graph Stat 2021; 31:541-552. [PMID: 36035966 PMCID: PMC9409340 DOI: 10.1080/10618600.2021.1987256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 07/27/2021] [Accepted: 09/16/2021] [Indexed: 11/22/2022]
Abstract
Longitudinal molecular data of rapidly evolving viruses and pathogens provide information about disease spread and complement traditional surveillance approaches based on case count data. The coalescent is used to model the genealogy that represents the sample ancestral relationships. The basic assumption is that coalescent events occur at a rate inversely proportional to the effective population size N e (t), a time-varying measure of genetic diversity. When the sampling process (collection of samples over time) depends on N e (t), the coalescent and the sampling processes can be jointly modeled to improve estimation of N e (t). Failing to do so can lead to bias due to model misspecification. However, the way that the sampling process depends on the effective population size may vary over time. We introduce an approach where the sampling process is modeled as an inhomogeneous Poisson process with rate equal to the product of N e (t) and a time-varying coefficient, making minimal assumptions on their functional shapes via Markov random field priors. We provide efficient algorithms for inference, show the model performance vis-a-vis alternative methods in a simulation study, and apply our model to SARS-CoV-2 sequences from Los Angeles and Santa Clara counties. The methodology is implemented and available in the R package adapref. Supplementary files for this article are available online.
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Affiliation(s)
| | - Julia A. Palacios
- Department of Statistics, Stanford University, Stanford, CA
- Department of Biomedical Data Science, Stanford Medicine, Stanford, CA
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35
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Smith MR, Trofimova M, Weber A, Duport Y, Kühnert D, von Kleist M. Rapid incidence estimation from SARS-CoV-2 genomes reveals decreased case detection in Europe during summer 2020. Nat Commun 2021; 12:6009. [PMID: 34650062 PMCID: PMC8517019 DOI: 10.1038/s41467-021-26267-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 09/24/2021] [Indexed: 12/24/2022] Open
Abstract
By October 2021, 230 million SARS-CoV-2 diagnoses have been reported. Yet, a considerable proportion of cases remains undetected. Here, we propose GInPipe, a method that rapidly reconstructs SARS-CoV-2 incidence profiles solely from publicly available, time-stamped viral genomes. We validate GInPipe against simulated outbreaks and elaborate phylodynamic analyses. Using available sequence data, we reconstruct incidence histories for Denmark, Scotland, Switzerland, and Victoria (Australia) and demonstrate, how to use the method to investigate the effects of changing testing policies on case ascertainment. Specifically, we find that under-reporting was highest during summer 2020 in Europe, coinciding with more liberal testing policies at times of low testing capacities. Due to the increased use of real-time sequencing, it is envisaged that GInPipe can complement established surveillance tools to monitor the SARS-CoV-2 pandemic. In post-pandemic times, when diagnostic efforts are decreasing, GInPipe may facilitate the detection of hidden infection dynamics.
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Affiliation(s)
- Maureen Rebecca Smith
- Systems Medicine of Infectious Disease (P5), Robert Koch Institute, Berlin, Germany.
- Bioinformatics (MF1), Robert Koch Institute, Berlin, Germany.
| | - Maria Trofimova
- Systems Medicine of Infectious Disease (P5), Robert Koch Institute, Berlin, Germany
- Bioinformatics (MF1), Robert Koch Institute, Berlin, Germany
| | - Ariane Weber
- Transmission, Infection, Diversification and Evolution Group, Max-Planck Institute for the Science of Human History, Jena, Germany
| | - Yannick Duport
- Systems Medicine of Infectious Disease (P5), Robert Koch Institute, Berlin, Germany
- Bioinformatics (MF1), Robert Koch Institute, Berlin, Germany
| | - Denise Kühnert
- Transmission, Infection, Diversification and Evolution Group, Max-Planck Institute for the Science of Human History, Jena, Germany
- German COVID Omics Initiative (deCOI), Bonn, Germany
| | - Max von Kleist
- Systems Medicine of Infectious Disease (P5), Robert Koch Institute, Berlin, Germany.
- Bioinformatics (MF1), Robert Koch Institute, Berlin, Germany.
- German COVID Omics Initiative (deCOI), Bonn, Germany.
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36
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Coalescent models derived from birth-death processes. Theor Popul Biol 2021; 142:1-11. [PMID: 34563554 DOI: 10.1016/j.tpb.2021.09.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 09/08/2021] [Accepted: 09/09/2021] [Indexed: 11/21/2022]
Abstract
A coalescent model of a sample of size n is derived from a birth-death process that originates at a random time in the past from a single founder individual. Over time, the descendants of the founder evolve into a population of large (infinite) size from which a sample of size n is taken. The parameters and time of the birth-death process are scaled in N0, the size of the present-day population, while letting N0→∞, similarly to how the standard Kingman coalescent process arises from the Wright-Fisher model. The model is named the Limit Birth-Death (LBD) coalescent model. Simulations from the LBD coalescent model with sample size n are computationally slow compared to standard coalescent models. Therefore, we suggest different approximations to the LBD coalescent model assuming the population size is a deterministic function of time rather than a stochastic process. Furthermore, we introduce a hybrid LBD coalescent model, that combines the exactness of the LBD coalescent model model with the speed of the approximations.
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37
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Wagner CE, Saad-Roy CM, Morris SE, Baker RE, Mina MJ, Farrar J, Holmes EC, Pybus OG, Graham AL, Emanuel EJ, Levin SA, Metcalf CJE, Grenfell BT. Vaccine nationalism and the dynamics and control of SARS-CoV-2. Science 2021; 373:eabj7364. [PMID: 34404735 PMCID: PMC9835930 DOI: 10.1126/science.abj7364] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 08/09/2021] [Indexed: 01/16/2023]
Abstract
Vaccines provide powerful tools to mitigate the enormous public health and economic costs that the ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic continues to exert globally, yet vaccine distribution remains unequal among countries. To examine the potential epidemiological and evolutionary impacts of “vaccine nationalism,” we extend previous models to include simple scenarios of stockpiling between two regions. In general, when vaccines are widely available and the immunity they confer is robust, sharing doses minimizes total cases across regions. A number of subtleties arise when the populations and transmission rates in each region differ, depending on evolutionary assumptions and vaccine availability. When the waning of natural immunity contributes most to evolutionary potential, sustained transmission in low-access regions results in an increased potential for antigenic evolution, which may result in the emergence of novel variants that affect epidemiological characteristics globally. Overall, our results stress the importance of rapid, equitable vaccine distribution for global control of the pandemic.
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Affiliation(s)
- Caroline E. Wagner
- Department of Bioengineering, McGill University, Montreal, QC H3A 0C3, Canada
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Chadi M. Saad-Roy
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA
- Princeton High Meadows Environmental Institute, Princeton University, Princeton, NJ 08540, USA
| | - Sinead E. Morris
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY 10032, USA
| | - Rachel E. Baker
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08540, USA
- Princeton High Meadows Environmental Institute, Princeton University, Princeton, NJ 08540, USA
| | - Michael J. Mina
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Jeremy Farrar
- Department of Bioengineering, McGill University, Montreal, QC H3A 0C3, Canada
- The Wellcome Trust, London, UK
| | - Edward C. Holmes
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08540, USA
- Marie Bashir Institute for Infectious Diseases and Biosecurity, The University of Sydney, Sydney, NSW, Australia
- School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW, Australia
- School of Medical Sciences, The University of Sydney, Sydney, NSW, Australia
| | - Oliver G. Pybus
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY 10032, USA
- Department of Zoology, University of Oxford, Oxford, UK
| | - Andrea L. Graham
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08540, USA
- Princeton High Meadows Environmental Institute, Princeton University, Princeton, NJ 08540, USA
| | - Ezekiel J. Emanuel
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Simon A. Levin
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08540, USA
- Princeton High Meadows Environmental Institute, Princeton University, Princeton, NJ 08540, USA
| | - C. Jessica E. Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08540, USA
- Princeton High Meadows Environmental Institute, Princeton University, Princeton, NJ 08540, USA
- Princeton School of Public and International Affairs, Princeton University, Princeton, NJ 08540, USA
| | - Bryan T. Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08540, USA
- Princeton High Meadows Environmental Institute, Princeton University, Princeton, NJ 08540, USA
- Princeton School of Public and International Affairs, Princeton University, Princeton, NJ 08540, USA
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38
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Didelot X, Geidelberg L, Volz EM. Model design for non-parametric phylodynamic inference and applications to pathogen surveillance. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021:2021.01.18.427056. [PMID: 34426812 PMCID: PMC8382123 DOI: 10.1101/2021.01.18.427056] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Inference of effective population size from genomic data can provide unique information about demographic history, and when applied to pathogen genetic data can also provide insights into epidemiological dynamics. The combination of non-parametric models for population dynamics with molecular clock models which relate genetic data to time has enabled phylodynamic inference based on large sets of time-stamped genetic sequence data. The methodology for non-parametric inference of effective population size is well-developed in the Bayesian setting, but here we develop a frequentist approach based on non-parametric latent process models of population size dynamics. We appeal to statistical principles based on out-of-sample prediction accuracy in order to optimize parameters that control shape and smoothness of the population size over time. We demonstrate the flexibility and speed of this approach in a series of simulation experiments, and apply the methodology to reconstruct the previously described waves in the seventh pandemic of cholera. We also estimate the impact of non-pharmaceutical interventions for COVID-19 in England using thousands of SARS-CoV-2 sequences. By incorporating a measure of the strength of these interventions over time within the phylodynamic model, we estimate the impact of the first national lockdown in the UK on the epidemic reproduction number.
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Affiliation(s)
- Xavier Didelot
- School of Life Sciences and Department of Statistics, University of Warwick, United Kingdom
| | - Lily Geidelberg
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, United Kingdom
| | | | - Erik M Volz
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, United Kingdom
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39
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Marquioni VM, de Aguiar MAM. Modeling neutral viral mutations in the spread of SARS-CoV-2 epidemics. PLoS One 2021; 16:e0255438. [PMID: 34324605 PMCID: PMC8321105 DOI: 10.1371/journal.pone.0255438] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 07/16/2021] [Indexed: 11/18/2022] Open
Abstract
Although traditional models of epidemic spreading focus on the number of infected, susceptible and recovered individuals, a lot of attention has been devoted to integrate epidemic models with population genetics. Here we develop an individual-based model for epidemic spreading on networks in which viruses are explicitly represented by finite chains of nucleotides that can mutate inside the host. Under the hypothesis of neutral evolution we compute analytically the average pairwise genetic distance between all infecting viruses over time. We also derive a mean-field version of this equation that can be added directly to compartmental models such as SIR or SEIR to estimate the genetic evolution. We compare our results with the inferred genetic evolution of SARS-CoV-2 at the beginning of the epidemic in China and found good agreement with the analytical solution of our model. Finally, using genetic distance as a proxy for different strains, we use numerical simulations to show that the lower the connectivity between communities, e.g., cities, the higher the probability of reinfection.
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Affiliation(s)
- Vitor M. Marquioni
- Instituto de Física “Gleb Wataghin”, Universidade Estadual de Campinas - UNICAMP, Campinas, SP, Brazil
| | - Marcus A. M. de Aguiar
- Instituto de Física “Gleb Wataghin”, Universidade Estadual de Campinas - UNICAMP, Campinas, SP, Brazil
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40
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MacPherson A, Louca S, McLaughlin A, Joy JB, Pennell MW. Unifying Phylogenetic Birth-Death Models in Epidemiology and Macroevolution. Syst Biol 2021; 71:172-189. [PMID: 34165577 PMCID: PMC8972974 DOI: 10.1093/sysbio/syab049] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 06/09/2021] [Accepted: 06/21/2021] [Indexed: 11/13/2022] Open
Abstract
Birth–death stochastic processes are the foundations of many phylogenetic models and are
widely used to make inferences about epidemiological and macroevolutionary dynamics. There
are a large number of birth–death model variants that have been developed; these impose
different assumptions about the temporal dynamics of the parameters and about the sampling
process. As each of these variants was individually derived, it has been difficult to
understand the relationships between them as well as their precise biological and
mathematical assumptions. Without a common mathematical foundation, deriving new models is
nontrivial. Here, we unify these models into a single framework, prove that many
previously developed epidemiological and macroevolutionary models are all special cases of
a more general model, and illustrate the connections between these variants. This
unification includes both models where the process is the same for all lineages and those
in which it varies across types. We also outline a straightforward procedure for deriving
likelihood functions for arbitrarily complex birth–death(-sampling) models that will
hopefully allow researchers to explore a wider array of scenarios than was previously
possible. By rederiving existing single-type birth–death sampling models, we clarify and
synthesize the range of explicit and implicit assumptions made by these models.
[Birth–death processes; epidemiology; macroevolution; phylogenetics; statistical
inference.]
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Affiliation(s)
- Ailene MacPherson
- Department of Zoology and Biodiversity Research Centre, University of British Columbia, Vancouver, Canada.,Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Canada
| | - Stilianos Louca
- Department of Biology, University of Oregon, USA.,Institute of Ecology and Evolution, University of Oregon, USA
| | - Angela McLaughlin
- British Columbia Centre for Excellence in HIV/AIDS, Vancouver, Canada.,Bioinformatics, University of British Columbia, Vancouver, Canada
| | - Jeffrey B Joy
- British Columbia Centre for Excellence in HIV/AIDS, Vancouver, Canada.,Bioinformatics, University of British Columbia, Vancouver, Canada.,Department of Medicine, University of British Columbia, Vancouver, Canada
| | - Matthew W Pennell
- Department of Zoology and Biodiversity Research Centre, University of British Columbia, Vancouver, Canada
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41
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Rothstein AP, Byrne AQ, Knapp RA, Briggs CJ, Voyles J, Richards-Zawacki CL, Rosenblum EB. Divergent regional evolutionary histories of a devastating global amphibian pathogen. Proc Biol Sci 2021; 288:20210782. [PMID: 34157877 PMCID: PMC8220259 DOI: 10.1098/rspb.2021.0782] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
Emerging infectious diseases are a pressing threat to global biological diversity. Increased incidence and severity of novel pathogens underscores the need for methodological advances to understand pathogen emergence and spread. Here, we use genetic epidemiology to test, and challenge, key hypotheses about a devastating zoonotic disease impacting amphibians globally. Using an amplicon-based sequencing method and non-invasive samples we retrospectively explore the history of the fungal pathogen Batrachochytrium dendrobatidis (Bd) in two emblematic amphibian systems: the Sierra Nevada of California and Central Panama. The hypothesis in both regions is the hypervirulent Global Panzootic Lineage of Bd (BdGPL) was recently introduced and spread rapidly in a wave-like pattern. Our data challenge this hypothesis by demonstrating similar epizootic signatures can have radically different underlying evolutionary histories. In Central Panama, our genetic data confirm a recent and rapid pathogen spread. However, BdGPL in the Sierra Nevada has remarkable spatial structuring, high genetic diversity and a relatively older history inferred from time-dated phylogenies. Thus, this deadly pathogen lineage may have a longer history in some regions than assumed, providing insights into its origin and spread. Overall, our results highlight the importance of integrating observed wildlife die-offs with genetic data to more accurately reconstruct pathogen outbreaks.
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Affiliation(s)
- Andrew P Rothstein
- Department of Environmental Science, Policy, and Management, University of California Berkeley, Berkeley, CA, USA.,Museum of Vertebrate Zoology, University of California Berkeley, Berkeley, CA, USA
| | - Allison Q Byrne
- Department of Environmental Science, Policy, and Management, University of California Berkeley, Berkeley, CA, USA.,Museum of Vertebrate Zoology, University of California Berkeley, Berkeley, CA, USA.,Center for Conservation Genomics, Smithsonian Conservation Biology Institute, National Zoological Park, Washington, DC, USA
| | - Roland A Knapp
- Sierra Nevada Aquatic Research Laboratory, University of California, Mammoth Lakes, CA, USA.,Earth Research Institute, University of California, Santa Barbara, CA, USA
| | - Cheryl J Briggs
- Earth Research Institute, University of California, Santa Barbara, CA, USA.,Department of Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, CA, USA
| | - Jamie Voyles
- Department of Biology, University of Nevada, Reno, NV, USA
| | | | - Erica Bree Rosenblum
- Department of Environmental Science, Policy, and Management, University of California Berkeley, Berkeley, CA, USA.,Museum of Vertebrate Zoology, University of California Berkeley, Berkeley, CA, USA
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42
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Featherstone LA, Di Giallonardo F, Holmes EC, Vaughan TG, Duchêne S. Infectious disease phylodynamics with occurrence data. Methods Ecol Evol 2021. [DOI: 10.1111/2041-210x.13620] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Leo A. Featherstone
- Department of Microbiology and Immunology Peter Doherty Institute for Infection and Immunity University of Melbourne Melbourne Vic. Australia
| | | | - Edward C. Holmes
- Marie Bashir Institute for Infectious Diseases and BiosecurityThe University of Sydney Sydney NSW Australia
- Charles Perkins Centre School of Life and Environmental Sciences The University of Sydney Sydney NSW Australia
- School of Medical Sciences The University of Sydney Sydney NSW Australia
| | - Timothy G. Vaughan
- Department of Biosystems Science and Engineering ETH Zurich Basel Switzerland
- Swiss Institute of Bioinformatics (SIB) Lausanne Switzerland
| | - Sebastián Duchêne
- Department of Microbiology and Immunology Peter Doherty Institute for Infection and Immunity University of Melbourne Melbourne Vic. Australia
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43
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Saad-Roy CM, Morris SE, Metcalf CJE, Mina MJ, Baker RE, Farrar J, Holmes EC, Pybus OG, Graham AL, Levin SA, Grenfell BT, Wagner CE. Epidemiological and evolutionary considerations of SARS-CoV-2 vaccine dosing regimes. Science 2021; 372:363-370. [PMID: 33688062 PMCID: PMC8128287 DOI: 10.1126/science.abg8663] [Citation(s) in RCA: 127] [Impact Index Per Article: 42.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 03/04/2021] [Indexed: 12/11/2022]
Abstract
Given vaccine dose shortages and logistical challenges, various deployment strategies are being proposed to increase population immunity levels to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Two critical issues arise: How timing of delivery of the second dose will affect infection dynamics and how it will affect prospects for the evolution of viral immune escape via a buildup of partially immune individuals. Both hinge on the robustness of the immune response elicited by a single dose as compared with natural and two-dose immunity. Building on an existing immuno-epidemiological model, we find that in the short term, focusing on one dose generally decreases infections, but that longer-term outcomes depend on this relative immune robustness. We then explore three scenarios of selection and find that a one-dose policy may increase the potential for antigenic evolution under certain conditions of partial population immunity. We highlight the critical need to test viral loads and quantify immune responses after one vaccine dose and to ramp up vaccination efforts globally.
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Affiliation(s)
- Chadi M Saad-Roy
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA.
| | - Sinead E Morris
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY 10032, USA
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
- Princeton School of Public and International Affairs, Princeton University, Princeton, NJ 08544, USA
| | - Michael J Mina
- Departments of Epidemiology and Immunology and Infectious Diseases, Harvard School of Public Health, Boston, MA 02115, USA
| | - Rachel E Baker
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
- High Meadows Environmental Institute, Princeton University, Princeton, NJ 08544, USA
| | | | - Edward C Holmes
- Marie Bashir Institute for Infectious Diseases and Biosecurity, School of Life and Environmental Sciences, and School of Medical Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Oliver G Pybus
- Department of Zoology, University of Oxford, Oxford OX1 3SZ, UK
| | - Andrea L Graham
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
| | - Simon A Levin
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA.
- Princeton School of Public and International Affairs, Princeton University, Princeton, NJ 08544, USA
- Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Caroline E Wagner
- Department of Bioengineering, McGill University, Montreal, QC H3A 0C3, Canada.
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44
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Hufsky F, Lamkiewicz K, Almeida A, Aouacheria A, Arighi C, Bateman A, Baumbach J, Beerenwinkel N, Brandt C, Cacciabue M, Chuguransky S, Drechsel O, Finn RD, Fritz A, Fuchs S, Hattab G, Hauschild AC, Heider D, Hoffmann M, Hölzer M, Hoops S, Kaderali L, Kalvari I, von Kleist M, Kmiecinski R, Kühnert D, Lasso G, Libin P, List M, Löchel HF, Martin MJ, Martin R, Matschinske J, McHardy AC, Mendes P, Mistry J, Navratil V, Nawrocki EP, O’Toole ÁN, Ontiveros-Palacios N, Petrov AI, Rangel-Pineros G, Redaschi N, Reimering S, Reinert K, Reyes A, Richardson L, Robertson DL, Sadegh S, Singer JB, Theys K, Upton C, Welzel M, Williams L, Marz M. Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research. Brief Bioinform 2021; 22:642-663. [PMID: 33147627 PMCID: PMC7665365 DOI: 10.1093/bib/bbaa232] [Citation(s) in RCA: 78] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 07/28/2020] [Accepted: 08/26/2020] [Indexed: 12/16/2022] Open
Abstract
SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causes the infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformatics tools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection, understanding and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to get insight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for the routine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemic and evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets and development of therapeutic strategies. For each tool, we briefly describe its use case and how it advances research specifically for SARS-CoV-2. All tools are free to use and available online, either through web applications or public code repositories. Contact:evbc@unj-jena.de.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Christian Brandt
- Institute of Infectious Disease and Infection Control at Jena University Hospital, Germany
| | - Marco Cacciabue
- Consejo Nacional de Investigaciones Científicas y Tócnicas (CONICET) working on FMDV virology at the Instituto de Agrobiotecnología y Biología Molecular (IABiMo, INTA-CONICET) and at the Departamento de Ciencias Básicas, Universidad Nacional de Luján (UNLu), Argentina
| | | | - Oliver Drechsel
- bioinformatics department at the Robert Koch-Institute, Germany
| | | | - Adrian Fritz
- Computational Biology of Infection Research group of Alice C. McHardy at the Helmholtz Centre for Infection Research, Germany
| | - Stephan Fuchs
- bioinformatics department at the Robert Koch-Institute, Germany
| | - Georges Hattab
- Bioinformatics Division at Philipps-University Marburg, Germany
| | | | - Dominik Heider
- Data Science in Biomedicine at the Philipps-University of Marburg, Germany
| | | | | | - Stefan Hoops
- Biocomplexity Institute and Initiative at the University of Virginia, USA
| | - Lars Kaderali
- Bioinformatics and head of the Institute of Bioinformatics at University Medicine Greifswald, Germany
| | | | - Max von Kleist
- bioinformatics department at the Robert Koch-Institute, Germany
| | - Renó Kmiecinski
- bioinformatics department at the Robert Koch-Institute, Germany
| | | | - Gorka Lasso
- Chandran Lab, Albert Einstein College of Medicine, USA
| | | | | | | | | | | | | | - Alice C McHardy
- Computational Biology of Infection Research Lab at the Helmholtz Centre for Infection Research in Braunschweig, Germany
| | - Pedro Mendes
- Center for Quantitative Medicine of the University of Connecticut School of Medicine, USA
| | | | - Vincent Navratil
- Bioinformatics and Systems Biology at the Rhône Alpes Bioinformatics core facility, Universitó de Lyon, France
| | | | | | | | | | | | - Nicole Redaschi
- Development of the Swiss-Prot group at the SIB for UniProt and SIB resources that cover viral biology (ViralZone)
| | - Susanne Reimering
- Computational Biology of Infection Research group of Alice C. McHardy at the Helmholtz Centre for Infection Research
| | | | | | | | | | - Sepideh Sadegh
- Chair of Experimental Bioinformatics at Technical University of Munich, Germany
| | - Joshua B Singer
- MRC-University of Glasgow Centre for Virus Research, Glasgow, Scotland, UK
| | | | - Chris Upton
- Department of Biochemistry and Microbiology, University of Victoria, Canada
| | | | | | - Manja Marz
- Friedrich Schiller University Jena, Germany
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45
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Wu JT, Leung K, Lam TTY, Ni MY, Wong CKH, Peiris JSM, Leung GM. Nowcasting epidemics of novel pathogens: lessons from COVID-19. Nat Med 2021; 27:388-395. [PMID: 33723452 DOI: 10.1038/s41591-021-01278-w] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 02/03/2021] [Indexed: 12/28/2022]
Abstract
Epidemic nowcasting broadly refers to assessing the current state by understanding key pathogenic, epidemiologic, clinical and socio-behavioral characteristics of an ongoing outbreak. Its primary objective is to provide situational awareness and inform decisions on control responses. In the event of large-scale sustained emergencies, such as the COVID-19 pandemic, scientists need to constantly update their aims and analytics with respect to the rapidly evolving emergence of new questions, data and findings in order to synthesize real-time evidence for policy decisions. In this Perspective, we share our views on the functional aims, rationale, data requirements and challenges of nowcasting at different stages of an epidemic, drawing on the ongoing COVID-19 experience. We highlight how recent advances in the computational and laboratory sciences could be harnessed to complement traditional approaches to enhance the scope, timeliness, reliability and utility of epidemic nowcasting.
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Affiliation(s)
- Joseph T Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China. .,Laboratory of Data Discovery for Health (D24H), Hong Kong, China.
| | - Kathy Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.,Laboratory of Data Discovery for Health (D24H), Hong Kong, China
| | - Tommy T Y Lam
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.,Laboratory of Data Discovery for Health (D24H), Hong Kong, China.,State Key Laboratory of Emerging Infectious Diseases, School of Public Health, The University of Hong Kong, Hong Kong, China.,Joint Institute of Virology (Shantou University and The University of Hong Kong), Guangdong-Hongkong Joint Laboratory of Emerging Infectious Diseases, Shantou University, Shantou, China
| | - Michael Y Ni
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.,The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China.,Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Hong Kong, China
| | - Carlos K H Wong
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.,Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - J S Malik Peiris
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.,HKU-Pasteur Research Pole, The University of Hong Kong, Hong Kong, China
| | - Gabriel M Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.,Laboratory of Data Discovery for Health (D24H), Hong Kong, China
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46
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Ramazzotti D, Angaroni F, Maspero D, Gambacorti-Passerini C, Antoniotti M, Graudenzi A, Piazza R. VERSO: A comprehensive framework for the inference of robust phylogenies and the quantification of intra-host genomic diversity of viral samples. PATTERNS (NEW YORK, N.Y.) 2021; 2:100212. [PMID: 33728416 PMCID: PMC7953447 DOI: 10.1016/j.patter.2021.100212] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 11/30/2020] [Accepted: 01/22/2021] [Indexed: 12/22/2022]
Abstract
We introduce VERSO, a two-step framework for the characterization of viral evolution from sequencing data of viral genomes, which is an improvement on phylogenomic approaches for consensus sequences. VERSO exploits an efficient algorithmic strategy to return robust phylogenies from clonal variant profiles, also in conditions of sampling limitations. It then leverages variant frequency patterns to characterize the intra-host genomic diversity of samples, revealing undetected infection chains and pinpointing variants likely involved in homoplasies. On simulations, VERSO outperforms state-of-the-art tools for phylogenetic inference. Notably, the application to 6,726 amplicon and RNA sequencing samples refines the estimation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) evolution, while co-occurrence patterns of minor variants unveil undetected infection paths, which are validated with contact tracing data. Finally, the analysis of SARS-CoV-2 mutational landscape uncovers a temporal increase of overall genomic diversity and highlights variants transiting from minor to clonal state and homoplastic variants, some of which fall on the spike gene. Available at: https://github.com/BIMIB-DISCo/VERSO.
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Affiliation(s)
- Daniele Ramazzotti
- Department of Medicine and Surgery, Università degli Studi di Milano-Bicocca, Monza, Italy
| | - Fabrizio Angaroni
- Department of Informatics, Systems and Communication, Università degli Studi di Milano-Bicocca, Milan, Italy
| | - Davide Maspero
- Department of Informatics, Systems and Communication, Università degli Studi di Milano-Bicocca, Milan, Italy
- Inst. of Molecular Bioimaging and Physiology, Consiglio Nazionale delle Ricerche (IBFM-CNR), Segrate, Milan, Italy
| | | | - Marco Antoniotti
- Department of Informatics, Systems and Communication, Università degli Studi di Milano-Bicocca, Milan, Italy
- Bicocca Bioinformatics, Biostatistics and Bioimaging Centre – B4, Milan, Italy
| | - Alex Graudenzi
- Inst. of Molecular Bioimaging and Physiology, Consiglio Nazionale delle Ricerche (IBFM-CNR), Segrate, Milan, Italy
- Bicocca Bioinformatics, Biostatistics and Bioimaging Centre – B4, Milan, Italy
| | - Rocco Piazza
- Department of Medicine and Surgery, Università degli Studi di Milano-Bicocca, Monza, Italy
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47
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Saad-Roy CM, Morris SE, Metcalf CJE, Mina MJ, Baker RE, Farrar J, Holmes EC, Pybus OG, Graham AL, Levin SA, Grenfell BT, Wagner CE. Epidemiological and evolutionary considerations of SARS-CoV-2 vaccine dosing regimes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2021.02.01.21250944. [PMID: 33564785 PMCID: PMC7872380 DOI: 10.1101/2021.02.01.21250944] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
As the threat of Covid-19 continues and in the face of vaccine dose shortages and logistical challenges, various deployment strategies are being proposed to increase population immunity levels. How timing of delivery of the second dose affects infection burden but also prospects for the evolution of viral immune escape are critical questions. Both hinge on the strength and duration (i.e. robustness) of the immune response elicited by a single dose, compared to natural and two-dose immunity. Building on an existing immuno-epidemiological model, we find that in the short-term, focusing on one dose generally decreases infections, but longer-term outcomes depend on this relative immune robustness. We then explore three scenarios of selection, evaluating how different second dose delays might drive immune escape via a build-up of partially immune individuals. Under certain scenarios, we find that a one-dose policy may increase the potential for antigenic evolution. We highlight the critical need to test viral loads and quantify immune responses after one vaccine dose, and to ramp up vaccination efforts throughout the world.
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Affiliation(s)
- Chadi M. Saad-Roy
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton NJ 08540, USA
| | - Sinead E. Morris
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York NY 10032, USA
| | - C. Jessica E. Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton NJ 08540, USA
- Princeton School of Public and International Affairs, Princeton University, Princeton NJ 08540, USA
| | - Michael J. Mina
- Departments of Epidemiology and Immunology and Infectious Diseases, Harvard School of Public Health, Boston MA 02115, USA
| | - Rachel E. Baker
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton NJ 08540, USA
- Princeton Environmental Institute, Princeton University, Princeton NJ 08540, USA
| | | | - Edward C. Holmes
- Marie Bashir Institute for Infectious Diseases and Biosecurity, School of Life and Environmental Sciences and School of Medical Sciences, The University of Sydney, Sydney, NSW, Australia
| | | | - Andrea L. Graham
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton NJ 08540, USA
| | - Simon A. Levin
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton NJ 08540, USA
| | - Bryan T. Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton NJ 08540, USA
- Princeton School of Public and International Affairs, Princeton University, Princeton NJ 08540, USA
- Fogarty International Center, National Institutes of Health, Bethesda MD 20892, USA
| | - Caroline E. Wagner
- Department of Bioengineering, McGill University, Montreal QC H3A 0C3, Canada
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48
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Watson OJ, Okell LC, Hellewell J, Slater HC, Unwin HJT, Omedo I, Bejon P, Snow RW, Noor AM, Rockett K, Hubbart C, Nankabirwa JI, Greenhouse B, Chang HH, Ghani AC, Verity R. Evaluating the Performance of Malaria Genetics for Inferring Changes in Transmission Intensity Using Transmission Modeling. Mol Biol Evol 2021; 38:274-289. [PMID: 32898225 PMCID: PMC7783189 DOI: 10.1093/molbev/msaa225] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Substantial progress has been made globally to control malaria, however there is a growing need for innovative new tools to ensure continued progress. One approach is to harness genetic sequencing and accompanying methodological approaches as have been used in the control of other infectious diseases. However, to utilize these methodologies for malaria, we first need to extend the methods to capture the complex interactions between parasites, human and vector hosts, and environment, which all impact the level of genetic diversity and relatedness of malaria parasites. We develop an individual-based transmission model to simulate malaria parasite genetics parameterized using estimated relationships between complexity of infection and age from five regions in Uganda and Kenya. We predict that cotransmission and superinfection contribute equally to within-host parasite genetic diversity at 11.5% PCR prevalence, above which superinfections dominate. Finally, we characterize the predictive power of six metrics of parasite genetics for detecting changes in transmission intensity, before grouping them in an ensemble statistical model. The model predicted malaria prevalence with a mean absolute error of 0.055. Different assumptions about the availability of sample metadata were considered, with the most accurate predictions of malaria prevalence made when the clinical status and age of sampled individuals is known. Parasite genetics may provide a novel surveillance tool for estimating the prevalence of malaria in areas in which prevalence surveys are not feasible. However, the findings presented here reinforce the need for patient metadata to be recorded and made available within all future attempts to use parasite genetics for surveillance.
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Affiliation(s)
- Oliver J Watson
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Lucy C Okell
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Joel Hellewell
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Hannah C Slater
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - H Juliette T Unwin
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Irene Omedo
- KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research-Coast, Kilifi, Kenya
| | - Philip Bejon
- KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research-Coast, Kilifi, Kenya
| | - Robert W Snow
- Population Health Unit, Kenya Medical Research Institute—Wellcome Trust Research Programme, Nairobi, Kenya
- Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom
| | | | - Kirk Rockett
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Christina Hubbart
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Joaniter I Nankabirwa
- Infectious Diseases Research Collaboration, Kampala, Uganda
- Makerere University College of Health Sciences, Kampala, Uganda
| | - Bryan Greenhouse
- Department of Medicine, University of California, San Francisco, San Francisco, CA
| | - Hsiao-Han Chang
- Center for Communicable Disease Dynamics, Harvard TH Chan School of Public Health, Boston, MA
| | - Azra C Ghani
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Robert Verity
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
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49
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López MG, Chiner-Oms Á, García de Viedma D, Ruiz-Rodriguez P, Bracho MA, Cancino-Muñoz I, D’Auria G, de Marco G, García-González N, Goig GA, Gómez-Navarro I, Jiménez-Serrano S, Martinez-Priego L, Ruiz-Hueso P, Ruiz-Roldán L, Torres-Puente M, Alberola J, Albert E, Aranzamendi Zaldumbide M, Bea-Escudero MP, Boga JA, Bordoy AE, Canut-Blasco A, Carvajal A, Cilla Eguiluz G, Cordón Rodríguez ML, Costa-Alcalde JJ, de Toro M, de Toro Peinado I, del Pozo JL, Duchêne S, Fernández-Pinero J, Fuster Escrivá B, Gimeno Cardona C, González Galán V, Gonzalo Jiménez N, Hernáez Crespo S, Herranz M, Lepe JA, López-Causapé C, López-Hontangas JL, Martín V, Martró E, Milagro Beamonte A, Montes Ros M, Moreno-Muñoz R, Navarro D, Navarro-Marí JM, Not A, Oliver A, Palop-Borrás B, Parra Grande M, Pedrosa-Corral I, Pérez González MC, Pérez-Lago L, Pérez-Ruiz M, Piñeiro Vázquez L, Rabella N, Rezusta A, Robles Fonseca L, Rodríguez-Villodres Á, Sanbonmatsu-Gámez S, Sicilia J, Soriano A, Tirado Balaguer MD, Torres I, Tristancho A, Marimón JM, Coscolla M, González-Candelas F, Comas I. The first wave of the COVID-19 epidemic in Spain was associated with early introductions and fast spread of a dominating genetic variant. Nat Genet 2021; 53:1405-1414. [PMID: 34594042 PMCID: PMC8481935 DOI: 10.1038/s41588-021-00936-6] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 08/11/2021] [Indexed: 02/08/2023]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has affected the world radically since 2020. Spain was one of the European countries with the highest incidence during the first wave. As a part of a consortium to monitor and study the evolution of the epidemic, we sequenced 2,170 samples, diagnosed mostly before lockdown measures. Here, we identified at least 500 introductions from multiple international sources and documented the early rise of two dominant Spanish epidemic clades (SECs), probably amplified by superspreading events. Both SECs were related closely to the initial Asian variants of SARS-CoV-2 and spread widely across Spain. We inferred a substantial reduction in the effective reproductive number of both SECs due to public-health interventions (Re < 1), also reflected in the replacement of SECs by a new variant over the summer of 2020. In summary, we reveal a notable difference in the initial genetic makeup of SARS-CoV-2 in Spain compared with other European countries and show evidence to support the effectiveness of lockdown measures in controlling virus spread, even for the most successful genetic variants.
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Affiliation(s)
- Mariana G. López
- grid.466828.60000 0004 1793 8484Instituto de Biomedicina de Valencia (IBV-CSIC), Valencia, Spain
| | - Álvaro Chiner-Oms
- grid.466828.60000 0004 1793 8484Instituto de Biomedicina de Valencia (IBV-CSIC), Valencia, Spain
| | - Darío García de Viedma
- grid.410526.40000 0001 0277 7938Servicio de Microbiología Clínica y Enfermedades Infecciosas, Hospital General Universitario Gregorio Marañón, Madrid, Spain ,grid.410526.40000 0001 0277 7938Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain ,grid.413448.e0000 0000 9314 1427CIBER Enfermedades Respiratorias (CIBERES), Bunyola, Spain
| | - Paula Ruiz-Rodriguez
- grid.5338.d0000 0001 2173 938XInstituto de Biología Integrativa de Sistemas, I2SysBio (CSIC-Universitat de València), Valencia, Spain
| | - Maria Alma Bracho
- grid.5338.d0000 0001 2173 938XJoint Research Unit Infection and Public Health FISABIO-University of Valencia I2SysBio, Valencia, Spain ,grid.413448.e0000 0000 9314 1427Ciber en Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Irving Cancino-Muñoz
- grid.466828.60000 0004 1793 8484Instituto de Biomedicina de Valencia (IBV-CSIC), Valencia, Spain
| | - Giuseppe D’Auria
- grid.413448.e0000 0000 9314 1427Ciber en Epidemiología y Salud Pública (CIBERESP), Madrid, Spain ,grid.428862.2FISABIO, Servicio de Secuenciación, València, Spain
| | | | - Neris García-González
- grid.5338.d0000 0001 2173 938XJoint Research Unit Infection and Public Health FISABIO-University of Valencia I2SysBio, Valencia, Spain
| | - Galo Adrian Goig
- grid.416786.a0000 0004 0587 0574Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Basel, Switzerland
| | - Inmaculada Gómez-Navarro
- grid.466828.60000 0004 1793 8484Instituto de Biomedicina de Valencia (IBV-CSIC), Valencia, Spain
| | - Santiago Jiménez-Serrano
- grid.466828.60000 0004 1793 8484Instituto de Biomedicina de Valencia (IBV-CSIC), Valencia, Spain
| | | | - Paula Ruiz-Hueso
- grid.428862.2FISABIO, Servicio de Secuenciación, València, Spain
| | - Lidia Ruiz-Roldán
- grid.5338.d0000 0001 2173 938XJoint Research Unit Infection and Public Health FISABIO-University of Valencia I2SysBio, Valencia, Spain
| | - Manuela Torres-Puente
- grid.466828.60000 0004 1793 8484Instituto de Biomedicina de Valencia (IBV-CSIC), Valencia, Spain
| | - Juan Alberola
- grid.411289.70000 0004 1770 9825Servicio de Microbiología. Hospital Dr Peset, Valencia, Spain ,grid.424970.c0000 0001 2353 2112Conselleria de Sanitat i Consum, Generalitat Valenciana, Valencia, Spain ,grid.5338.d0000 0001 2173 938XDepartamento Microbiología, Facultad de Medicina, Universitat de València, Valencia, Spain
| | - Eliseo Albert
- grid.411308.fMicrobiology Service, Hospital Clínico Universitario, INCLIVA Research Institute, Valencia, Spain
| | - Maitane Aranzamendi Zaldumbide
- grid.411232.70000 0004 1767 5135Servicio de Microbiología, Hospital Universitario Cruces, Bilbao, Spain ,Grupo de Microbiología y Control de Infección, Instituto de Investigación Sanitaria Biocruces Bizkaia, Barakaldo, Spain
| | - María Pilar Bea-Escudero
- grid.460738.ePlataforma de Genómica y Bioinformática, Centro de Investigación Biomédica de La Rioja (CIBIR), Logroño, Spain
| | - Jose Antonio Boga
- grid.411052.30000 0001 2176 9028Servicio de Microbiología, Hospital Universitario Central de Asturias, Oviedo, Spain ,grid.511562.4Grupo de Microbiología Traslacional, Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Asturias, Spain
| | - Antoni E. Bordoy
- grid.411438.b0000 0004 1767 6330Servicio de Microbiología, Laboratori Clínic Metropolitana Nord, Hospital Universitari Germans Trias i Pujol, Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol (IGTP), Badalona, Barcelona, Spain
| | - Andrés Canut-Blasco
- grid.426049.d0000 0004 1793 9479Servicio de Microbiología, Hospital Universitario de Álava, Osakidetza-Servicio Vasco de Salud, Vitoria-Gasteiz (Álava), Spain
| | - Ana Carvajal
- grid.4807.b0000 0001 2187 3167Animal Health Department, Universidad de León, León, Spain
| | - Gustavo Cilla Eguiluz
- grid.414651.3Servicio de MicrobiologíaBiodonostia, Osakidetza, Hospital Universitario Donostia, San Sebastián, Spain
| | - Maria Luz Cordón Rodríguez
- grid.426049.d0000 0004 1793 9479Servicio de Microbiología, Hospital Universitario de Álava, Osakidetza-Servicio Vasco de Salud, Vitoria-Gasteiz (Álava), Spain
| | - José J. Costa-Alcalde
- grid.411048.80000 0000 8816 6945Hospital Clínico Universitario de Santiago de Compostela, Santiago de Compostela, Spain
| | - María de Toro
- grid.460738.ePlataforma de Genómica y Bioinformática, Centro de Investigación Biomédica de La Rioja (CIBIR), Logroño, Spain
| | | | - Jose Luis del Pozo
- grid.411730.00000 0001 2191 685XServicio de Enfermedades Infecciosas y Microbiología clínica, Clínica Universidad de Navarra, Pamplona, Spain
| | - Sebastián Duchêne
- grid.1008.90000 0001 2179 088XDepartment of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Victoria Australia
| | - Jovita Fernández-Pinero
- grid.419190.40000 0001 2300 669XInstituto Nacional de Investigación y Tecnología Agraria y Alimentaria, O.A., M.P. – INIA, Madrid, Spain
| | - Begoña Fuster Escrivá
- grid.5338.d0000 0001 2173 938XDepartamento Microbiología, Facultad de Medicina, Universitat de València, Valencia, Spain ,grid.106023.60000 0004 1770 977XServicio de Microbiología, Consorcio Hospital General Universitario de Valencia, Valencia, Spain
| | - Concepción Gimeno Cardona
- grid.106023.60000 0004 1770 977XServicio de Microbiología, Consorcio Hospital General Universitario de Valencia, Valencia, Spain
| | - Verónica González Galán
- grid.411109.c0000 0000 9542 1158Servicio de Microbiología UCEIMP, Hospital Universitario Virgen del Rocío, Sevilla, Spain
| | - Nieves Gonzalo Jiménez
- grid.411093.e0000 0004 0399 7977Servicio Microbiología, Departamento de Salud de Elche-Hospital General, Elche, Alicante, Spain
| | - Silvia Hernáez Crespo
- grid.426049.d0000 0004 1793 9479Servicio de Microbiología, Hospital Universitario de Álava, Osakidetza-Servicio Vasco de Salud, Vitoria-Gasteiz (Álava), Spain
| | - Marta Herranz
- grid.410526.40000 0001 0277 7938Servicio de Microbiología Clínica y Enfermedades Infecciosas, Hospital General Universitario Gregorio Marañón, Madrid, Spain ,grid.410526.40000 0001 0277 7938Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain ,grid.413448.e0000 0000 9314 1427CIBER Enfermedades Respiratorias (CIBERES), Bunyola, Spain
| | - José Antonio Lepe
- grid.411109.c0000 0000 9542 1158Servicio de Microbiología UCEIMP, Hospital Universitario Virgen del Rocío, Sevilla, Spain
| | - Carla López-Causapé
- grid.411164.70000 0004 1796 5984Servicio de Microbiología, Hospital Universitario Son Espases, Palma de Mallorca, Spain
| | - José Luis López-Hontangas
- grid.84393.350000 0001 0360 9602Hospital Universitario y Politécnico La Fe, Servicio de Microbiología, Valencia, Spain
| | - Vicente Martín
- grid.413448.e0000 0000 9314 1427Ciber en Epidemiología y Salud Pública (CIBERESP), Madrid, Spain ,grid.4807.b0000 0001 2187 3167Research Group on Gene-Environment Interactions and Health. Institute of Biomedicine (IBIOMED), Universidad de León, León, Spain
| | - Elisa Martró
- grid.413448.e0000 0000 9314 1427Ciber en Epidemiología y Salud Pública (CIBERESP), Madrid, Spain ,grid.411438.b0000 0004 1767 6330Servicio de Microbiología, Laboratori Clínic Metropolitana Nord, Hospital Universitari Germans Trias i Pujol, Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol (IGTP), Badalona, Barcelona, Spain
| | - Ana Milagro Beamonte
- grid.411106.30000 0000 9854 2756Servicio de Microbiología Clínica, Hospital Universitario Miguel Servet, Zaragoza, Spain
| | - Milagrosa Montes Ros
- grid.414651.3Servicio de MicrobiologíaBiodonostia, Osakidetza, Hospital Universitario Donostia, San Sebastián, Spain
| | | | - David Navarro
- grid.5338.d0000 0001 2173 938XDepartamento Microbiología, Facultad de Medicina, Universitat de València, Valencia, Spain ,grid.411308.fMicrobiology Service, Hospital Clínico Universitario, INCLIVA Research Institute, Valencia, Spain
| | - José María Navarro-Marí
- grid.411380.f0000 0000 8771 3783Servicio de Microbiología, Hospital Universitario Virgen de las Nieves, Granada, Spain ,grid.411380.f0000 0000 8771 3783Hospital Universitario Virgen de las Nieves, Instituto de Investigación Biosanitaria ibs, Granada, Spain
| | - Anna Not
- grid.411438.b0000 0004 1767 6330Servicio de Microbiología, Laboratori Clínic Metropolitana Nord, Hospital Universitari Germans Trias i Pujol, Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol (IGTP), Badalona, Barcelona, Spain
| | - Antonio Oliver
- grid.411164.70000 0004 1796 5984Servicio de Microbiología, Hospital Universitario Son Espases, Palma de Mallorca, Spain ,Instituto de Investigación Sanitaria de las Islas Baleares, Palma, Spain
| | - Begoña Palop-Borrás
- grid.411457.2Servicio de Microbiologia, Hospital Regional Universitario de Málaga, Málaga, Spain
| | - Mónica Parra Grande
- grid.507938.0Laboratorio de Microbiología, Hospital Marina Baixa, Villajoyosa, Spain
| | - Irene Pedrosa-Corral
- grid.411380.f0000 0000 8771 3783Servicio de Microbiología, Hospital Universitario Virgen de las Nieves, Granada, Spain ,grid.411380.f0000 0000 8771 3783Hospital Universitario Virgen de las Nieves, Instituto de Investigación Biosanitaria ibs, Granada, Spain
| | - Maria Carmen Pérez González
- grid.411250.30000 0004 0399 7109Hospital Universitario de Gran Canaria Dr. Negrin, Las Palmas de Gran Canaria, Spain
| | - Laura Pérez-Lago
- grid.410526.40000 0001 0277 7938Servicio de Microbiología Clínica y Enfermedades Infecciosas, Hospital General Universitario Gregorio Marañón, Madrid, Spain ,grid.410526.40000 0001 0277 7938Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
| | - Mercedes Pérez-Ruiz
- grid.411457.2Servicio de Microbiologia, Hospital Regional Universitario de Málaga, Málaga, Spain
| | - Luis Piñeiro Vázquez
- grid.414651.3Servicio de MicrobiologíaBiodonostia, Osakidetza, Hospital Universitario Donostia, San Sebastián, Spain
| | - Nuria Rabella
- grid.413396.a0000 0004 1768 8905Servei de Microbiologia, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain ,CREPIMC, Institut d’Investigació Biomèdica Sant Pau, Barcelona, Spain ,grid.7080.fDepartament de Genètica i Microbiologia, Universitat Autònoma de Barcelona, Cerdanyola, Spain
| | - Antonio Rezusta
- grid.411106.30000 0000 9854 2756Servicio de Microbiología Clínica, Hospital Universitario Miguel Servet, Zaragoza, Spain ,grid.488737.70000000463436020Instituto de Investigación Sanitaria de Aragón, Centro de Investigación Biomédica de Aragón (CIBA), Zaragoza, Spain ,grid.11205.370000 0001 2152 8769Facultad de Medicina, Universidad de Zaragoza, Zaragoza, Spain
| | - Lorena Robles Fonseca
- grid.411094.90000 0004 0506 8127Hospital General Universitario de Albacete, Albacete, Spain
| | - Ángel Rodríguez-Villodres
- grid.411109.c0000 0000 9542 1158Servicio de Microbiología UCEIMP, Hospital Universitario Virgen del Rocío, Sevilla, Spain
| | - Sara Sanbonmatsu-Gámez
- grid.411380.f0000 0000 8771 3783Servicio de Microbiología, Hospital Universitario Virgen de las Nieves, Granada, Spain ,grid.411380.f0000 0000 8771 3783Hospital Universitario Virgen de las Nieves, Instituto de Investigación Biosanitaria ibs, Granada, Spain
| | - Jon Sicilia
- grid.410526.40000 0001 0277 7938Servicio de Microbiología Clínica y Enfermedades Infecciosas, Hospital General Universitario Gregorio Marañón, Madrid, Spain ,grid.410526.40000 0001 0277 7938Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
| | - Alex Soriano
- grid.410458.c0000 0000 9635 9413Servicio de Enfermedades Infecciosas, Hospital Clínic de Barcelona, Barcelona, Spain
| | | | - Ignacio Torres
- grid.411308.fMicrobiology Service, Hospital Clínico Universitario, INCLIVA Research Institute, Valencia, Spain
| | - Alexander Tristancho
- grid.411106.30000 0000 9854 2756Servicio de Microbiología Clínica, Hospital Universitario Miguel Servet, Zaragoza, Spain ,grid.488737.70000000463436020Instituto de Investigación Sanitaria de Aragón, Centro de Investigación Biomédica de Aragón (CIBA), Zaragoza, Spain
| | - José María Marimón
- grid.414651.3Servicio de MicrobiologíaBiodonostia, Osakidetza, Hospital Universitario Donostia, San Sebastián, Spain
| | | | - Mireia Coscolla
- grid.5338.d0000 0001 2173 938XInstituto de Biología Integrativa de Sistemas, I2SysBio (CSIC-Universitat de València), Valencia, Spain
| | - Fernando González-Candelas
- grid.5338.d0000 0001 2173 938XJoint Research Unit Infection and Public Health FISABIO-University of Valencia I2SysBio, Valencia, Spain ,grid.413448.e0000 0000 9314 1427Ciber en Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Iñaki Comas
- grid.466828.60000 0004 1793 8484Instituto de Biomedicina de Valencia (IBV-CSIC), Valencia, Spain ,grid.413448.e0000 0000 9314 1427Ciber en Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
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Lorenzo-Redondo R, Ozer EA, Achenbach CJ, D'Aquila RT, Hultquist JF. Molecular epidemiology in the HIV and SARS-CoV-2 pandemics. Curr Opin HIV AIDS 2021; 16:11-24. [PMID: 33186230 PMCID: PMC7723008 DOI: 10.1097/coh.0000000000000660] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
PURPOSE OF REVIEW The aim of this review was to compare and contrast the application of molecular epidemiology approaches for the improved management and understanding of the HIV versus SARS-CoV-2 epidemics. RECENT FINDINGS Molecular biology approaches, including PCR and whole genome sequencing (WGS), have become powerful tools for epidemiological investigation. PCR approaches form the basis for many high-sensitivity diagnostic tests and can supplement traditional contact tracing and surveillance strategies to define risk networks and transmission patterns. WGS approaches can further define the causative agents of disease, trace the origins of the pathogen, and clarify routes of transmission. When coupled with clinical datasets, such as electronic medical record data, these approaches can investigate co-correlates of disease and pathogenesis. In the ongoing HIV epidemic, these approaches have been effectively deployed to identify treatment gaps, transmission clusters and risk factors, though significant barriers to rapid or real-time implementation remain critical to overcome. Likewise, these approaches have been successful in addressing some questions of SARS-CoV-2 transmission and pathogenesis, but the nature and rapid spread of the virus have posed additional challenges. SUMMARY Overall, molecular epidemiology approaches offer unique advantages and challenges that complement traditional epidemiological tools for the improved understanding and management of epidemics.
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Affiliation(s)
- Ramon Lorenzo-Redondo
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
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