665 results on '"Bedford, Trevor"'
Search Results
2. Author Correction: Positive selection underlies repeated knockout of ORF8 in SARS-CoV-2 evolution
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Wagner, Cassia, Kistler, Kathryn E., Perchetti, Garrett A., Baker, Noah, Frisbie, Lauren A., Torres, Laura Marcela, Aragona, Frank, Yun, Cory, Figgins, Marlin, Greninger, Alexander L., Cox, Alex, Oltean, Hanna N., Roychoudhury, Pavitra, and Bedford, Trevor
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- 2024
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3. Impacts of human mobility on the citywide transmission dynamics of 18 respiratory viruses in pre- and post-COVID-19 pandemic years
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Perofsky, Amanda C., Hansen, Chelsea L., Burstein, Roy, Boyle, Shanda, Prentice, Robin, Marshall, Cooper, Reinhart, David, Capodanno, Ben, Truong, Melissa, Schwabe-Fry, Kristen, Kuchta, Kayla, Pfau, Brian, Acker, Zack, Lee, Jover, Sibley, Thomas R., McDermot, Evan, Rodriguez-Salas, Leslie, Stone, Jeremy, Gamboa, Luis, Han, Peter D., Adler, Amanda, Waghmare, Alpana, Jackson, Michael L., Famulare, Michael, Shendure, Jay, Bedford, Trevor, Chu, Helen Y., Englund, Janet A., Starita, Lea M., and Viboud, Cécile
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- 2024
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4. Positive selection underlies repeated knockout of ORF8 in SARS-CoV-2 evolution
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Wagner, Cassia, Kistler, Kathryn E., Perchetti, Garrett A., Baker, Noah, Frisbie, Lauren A., Torres, Laura Marcela, Aragona, Frank, Yun, Cory, Figgins, Marlin, Greninger, Alexander L., Cox, Alex, Oltean, Hanna N., Roychoudhury, Pavitra, and Bedford, Trevor
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- 2024
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5. Changing genomic epidemiology of COVID-19 in long-term care facilities during the 2020–2022 pandemic, Washington State
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Oltean, Hanna N., Black, Allison, Lunn, Stephanie M., Smith, Nailah, Templeton, Allison, Bevers, Elyse, Kibiger, Lynae, Sixberry, Melissa, Bickel, Josina B., Hughes, James P., Lindquist, Scott, Baseman, Janet G., and Bedford, Trevor
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- 2024
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6. Fitting stochastic epidemic models to gene genealogies using linear noise approximation
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Tang, Mingwei, Dudas, Gytis, Bedford, Trevor, and Minin, Vladimir N
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Mathematical Sciences ,Statistics ,Bioengineering ,Infectious Diseases ,Emerging Infectious Diseases ,Aetiology ,2.5 Research design and methodologies (aetiology) ,Infection ,Good Health and Well Being ,Coalescent ,susceptible -infectious -recovered model ,state -space model ,phylodynamics ,Ebola virus ,Susceptible-Infectious-Recovered model ,state-space model ,Econometrics ,Statistics & Probability - Abstract
Phylodynamics is a set of population genetics tools that aim at reconstructing demographic history of a population based on molecular sequences of individuals sampled from the population of interest. One important task in phylodynamics is to estimate changes in (effective) population size. When applied to infectious disease sequences such estimation of population size trajectories can provide information about changes in the number of infections. To model changes in the number of infected individuals, current phylodynamic methods use non-parametric approaches (e.g., Bayesian curve-fitting based on change-point models or Gaussian process priors), parametric approaches (e.g., based on differential equations), and stochastic modeling in conjunction with likelihood-free Bayesian methods. The first class of methods yields results that are hard to interpret epidemiologically. The second class of methods provides estimates of important epidemiological parameters, such as infection and removal/recovery rates, but ignores variation in the dynamics of infectious disease spread. The third class of methods is the most advantageous statistically, but relies on computationally intensive particle filtering techniques that limits its applications. We propose a Bayesian model that combines phylodynamic inference and stochastic epidemic models, and achieves computational tractability by using a linear noise approximation (LNA) - a technique that allows us to approximate probability densities of stochastic epidemic model trajectories. LNA opens the door for using modern Markov chain Monte Carlo tools to approximate the joint posterior distribution of the disease transmission parameters and of high dimensional vectors describing unobserved changes in the stochastic epidemic model compartment sizes (e.g., numbers of infectious and susceptible individuals). In a simulation study, we show that our method can successfully recover parameters of stochastic epidemic models. We apply our estimation technique to Ebola genealogies estimated using viral genetic data from the 2014 epidemic in Sierra Leone and Liberia.
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- 2023
7. Age-dependent heterogeneity in the antigenic effects of mutations to influenza hemagglutinin
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Welsh, Frances C., Eguia, Rachel T., Lee, Juhye M., Haddox, Hugh K., Galloway, Jared, Van Vinh Chau, Nguyen, Loes, Andrea N., Huddleston, John, Yu, Timothy C., Quynh Le, Mai, Nhat, Nguyen T.D., Thi Le Thanh, Nguyen, Greninger, Alexander L., Chu, Helen Y., Englund, Janet A., Bedford, Trevor, Matsen, Frederick A., IV, Boni, Maciej F., and Bloom, Jesse D.
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- 2024
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8. Underdetected dispersal and extensive local transmission drove the 2022 mpox epidemic
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Paredes, Miguel I., Ahmed, Nashwa, Figgins, Marlin, Colizza, Vittoria, Lemey, Philippe, McCrone, John T., Müller, Nicola, Tran-Kiem, Cécile, and Bedford, Trevor
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- 2024
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9. State-dependent evolutionary models reveal modes of solid tumour growth
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Lewinsohn, Maya A., Bedford, Trevor, Müller, Nicola F., and Feder, Alison F.
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- 2023
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10. An atlas of continuous adaptive evolution in endemic human viruses
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Kistler, Kathryn E. and Bedford, Trevor
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- 2023
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11. Ebola Virus Transmission Initiated by Relapse of Systemic Ebola Virus Disease
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Mbala-Kingebeni, Placide, Pratt, Catherine, Ruffin, Mbusa Mutafali, Pauthner, Matthias G, Bile, Faustin, Ndaye, Antoine Nkuba, Black, Allison, Lusamaki, Eddy Kinganda, Faye, Martin, Aziza, Amuri, Diagne, Moussa M, Mukadi, Daniel, White, Bailey, Hadfield, James, Gangavarapu, Karthik, Bisento, Nella, Kazadi, Donatien, Nsunda, Bibiche, Akonga, Marceline, Tshiani, Olivier, Misasi, John, Ploquin, Aurelie, Epaso, Victor, Paka, Emilia Sana, N’kasar, Yannick Tutu Tshia, Mambu, Fabrice, Edidi, Francois, Matondo, Meris, Bula, Bula, Diallo, Boubacar, Keita, Mory, Belizaire, Marie Roseline Darnycka, Fall, Ibrahima Soce, Yam, Abdoulaye, Sabue, Mulangu, Rimion, Anne W, Salfati, Elias, Torkamani, Ali, Suchard, Marc A, Crozier, Ian, Hensley, Lisa, Rambaut, Andrew, Faye, Ousmane, Sall, Amadou, Sullivan, Nancy J, Bedford, Trevor, Andersen, Kristian G, Wiley, Michael R, Ahuka-Mundeke, Steve, and Tamfum, Jean-Jacques Muyembe
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Biodefense ,Vaccine Related ,Prevention ,Infectious Diseases ,Biotechnology ,Rare Diseases ,Emerging Infectious Diseases ,Immunization ,Infection ,Good Health and Well Being ,Adult ,Bayes Theorem ,Democratic Republic of the Congo ,Ebola Vaccines ,Ebolavirus ,Fatal Outcome ,Genome ,Viral ,Hemorrhagic Fever ,Ebola ,Humans ,Male ,Mutation ,Phylogeny ,RNA ,Viral ,Recurrence ,Medical and Health Sciences ,General & Internal Medicine - Abstract
During the 2018-2020 Ebola virus disease (EVD) outbreak in North Kivu province in the Democratic Republic of Congo, EVD was diagnosed in a patient who had received the recombinant vesicular stomatitis virus-based vaccine expressing a ZEBOV glycoprotein (rVSV-ZEBOV) (Merck). His treatment included an Ebola virus (EBOV)-specific monoclonal antibody (mAb114), and he recovered within 14 days. However, 6 months later, he presented again with severe EVD-like illness and EBOV viremia, and he died. We initiated epidemiologic and genomic investigations that showed that the patient had had a relapse of acute EVD that led to a transmission chain resulting in 91 cases across six health zones over 4 months. (Funded by the Bill and Melinda Gates Foundation and others.).
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- 2021
12. Fitting stochastic epidemic models to gene genealogies using linear noise approximation
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Tang, Mingwei, Dudas, Gytis, Bedford, Trevor, and Minin, Vladimir N.
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Quantitative Biology - Populations and Evolution ,Statistics - Methodology - Abstract
Phylodynamics is a set of population genetics tools that aim at reconstructing demographic history of a population based on molecular sequences of individuals sampled from the population of interest. One important task in phylodynamics is to estimate changes in (effective) population size. When applied to infectious disease sequences such estimation of population size trajectories can provide information about changes in the number of infections. To model changes in the number of infected individuals, current phylodynamic methods use non-parametric approaches, parametric approaches, and stochastic modeling in conjunction with likelihood-free Bayesian methods. The first class of methods yields results that are hard-to-interpret epidemiologically. The second class of methods provides estimates of important epidemiological parameters, such as infection and removal/recovery rates, but ignores variation in the dynamics of infectious disease spread. The third class of methods is the most advantageous statistically, but relies on computationally intensive particle filtering techniques that limits its applications. We propose a Bayesian model that combines phylodynamic inference and stochastic epidemic models, and achieves computational tractability by using a linear noise approximation (LNA) --- a technique that allows us to approximate probability densities of stochastic epidemic model trajectories. LNA opens the door for using modern Markov chain Monte Carlo tools to approximate the joint posterior distribution of the disease transmission parameters and of high dimensional vectors describing unobserved changes in the stochastic epidemic model compartment sizes (e.g., numbers of infectious and susceptible individuals). We apply our estimation technique to Ebola genealogies estimated using viral genetic data from the 2014 epidemic in Sierra Leone and Liberia., Comment: 43 pages, 6 figures in the main text
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- 2019
13. Cryptic transmission of SARS-CoV-2 in Washington state
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Bedford, Trevor, Greninger, Alexander L, Roychoudhury, Pavitra, Starita, Lea M, Famulare, Michael, Huang, Meei-Li, Nalla, Arun, Pepper, Gregory, Reinhardt, Adam, Xie, Hong, Shrestha, Lasata, Nguyen, Truong N, Adler, Amanda, Brandstetter, Elisabeth, Cho, Shari, Giroux, Danielle, Han, Peter D, Fay, Kairsten, Frazar, Chris D, Ilcisin, Misja, Lacombe, Kirsten, Lee, Jover, Kiavand, Anahita, Richardson, Matthew, Sibley, Thomas R, Truong, Melissa, Wolf, Caitlin R, Nickerson, Deborah A, Rieder, Mark J, Englund, Janet A, Hadfield, James, Hodcroft, Emma B, Huddleston, John, Moncla, Louise H, Müller, Nicola F, Neher, Richard A, Deng, Xianding, Gu, Wei, Federman, Scot, Chiu, Charles, Duchin, Jeffrey S, Gautom, Romesh, Melly, Geoff, Hiatt, Brian, Dykema, Philip, Lindquist, Scott, Queen, Krista, Tao, Ying, Uehara, Anna, Tong, Suxiang, MacCannell, Duncan, Armstrong, Gregory L, Baird, Geoffrey S, Chu, Helen Y, Shendure, Jay, Jerome, Keith R, Boeckh, Michael, Lutz, Barry R, Thompson, Matthew, Huang, Shichu, Jackson, Michael L, Kimball, Louise E, Logue, Jennifer, Lyon, Victoria, Newman, Kira L, and Suchsland, Monica L Zigman
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Infectious Diseases ,Pneumonia & Influenza ,Lung ,Prevention ,Emerging Infectious Diseases ,Biodefense ,Vaccine Related ,Pneumonia ,Infection ,Good Health and Well Being ,Bayes Theorem ,Betacoronavirus ,COVID-19 ,Coronavirus Infections ,Genome ,Viral ,Humans ,Likelihood Functions ,Pandemics ,Phylogeny ,Pneumonia ,Viral ,SARS-CoV-2 ,Washington ,Seattle Flu Study Investigators ,General Science & Technology - Abstract
After its emergence in Wuhan, China, in late November or early December 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus rapidly spread globally. Genome sequencing of SARS-CoV-2 allows the reconstruction of its transmission history, although this is contingent on sampling. We analyzed 453 SARS-CoV-2 genomes collected between 20 February and 15 March 2020 from infected patients in Washington state in the United States. We find that most SARS-CoV-2 infections sampled during this time derive from a single introduction in late January or early February 2020, which subsequently spread locally before active community surveillance was implemented.
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- 2020
14. Genomic surveillance reveals multiple introductions of SARS-CoV-2 into Northern California
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Deng, Xianding, Gu, Wei, Federman, Scot, du Plessis, Louis, Pybus, Oliver G, Faria, Nuno, Wang, Candace, Yu, Guixia, Bushnell, Brian, Pan, Chao-Yang, Guevara, Hugo, Sotomayor-Gonzalez, Alicia, Zorn, Kelsey, Gopez, Allan, Servellita, Venice, Hsu, Elaine, Miller, Steve, Bedford, Trevor, Greninger, Alexander L, Roychoudhury, Pavitra, Starita, Lea M, Famulare, Michael, Chu, Helen Y, Shendure, Jay, Jerome, Keith R, Anderson, Catie, Gangavarapu, Karthik, Zeller, Mark, Spencer, Emily, Andersen, Kristian G, MacCannell, Duncan, Paden, Clinton R, Li, Yan, Zhang, Jing, Tong, Suxiang, Armstrong, Gregory, Morrow, Scott, Willis, Matthew, Matyas, Bela T, Mase, Sundari, Kasirye, Olivia, Park, Maggie, Masinde, Godfred, Chan, Curtis, Yu, Alexander T, Chai, Shua J, Villarino, Elsa, Bonin, Brandon, Wadford, Debra A, and Chiu, Charles Y
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Pneumonia ,Lung ,Infectious Diseases ,Emerging Infectious Diseases ,Vaccine Related ,Pneumonia & Influenza ,Prevention ,Biodefense ,Infection ,Good Health and Well Being ,Betacoronavirus ,COVID-19 ,California ,Coronavirus Infections ,Epidemiological Monitoring ,Genome ,Viral ,Humans ,Pandemics ,Phylogeny ,Pneumonia ,Viral ,SARS-CoV-2 ,Sequence Alignment ,Ships ,Travel ,Washington ,General Science & Technology - Abstract
The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread globally, with >365,000 cases in California as of 17 July 2020. We investigated the genomic epidemiology of SARS-CoV-2 in Northern California from late January to mid-March 2020, using samples from 36 patients spanning nine counties and the Grand Princess cruise ship. Phylogenetic analyses revealed the cryptic introduction of at least seven different SARS-CoV-2 lineages into California, including epidemic WA1 strains associated with Washington state, with lack of a predominant lineage and limited transmission among communities. Lineages associated with outbreak clusters in two counties were defined by a single base substitution in the viral genome. These findings support contact tracing, social distancing, and travel restrictions to contain the spread of SARS-CoV-2 in California and other states.
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- 2020
15. SwabExpress: An end-to-end protocol for extraction-free COVID-19 testing
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Srivatsan, Sanjay, Heidl, Sarah, Pfau, Brian, Martin, Beth K, Han, Peter D, Zhong, Weizhi, van Raay, Katrina, McDermot, Evan, Opsahl, Jordan, Gamboa, Luis, Smith, Nahum, Truong, Melissa, Cho, Shari, Barrow, Kaitlyn A, Rich, Lucille M, Stone, Jeremy, Wolf, Caitlin R, McCulloch, Denise J, Kim, Ashley E, Brandstetter, Elisabeth, Sohlberg, Sarah L, Ilcisin, Misja, Geyer, Rachel E, Chen, Wei, Gehring, Jase, Investigators, Seattle Flu Study, Kosuri, Sriram, Bedford, Trevor, Rieder, Mark J, Nickerson, Deborah A, Chu, Helen Y, Konnick, Eric Q, Debley, Jason S, Shendure, Jay, Lockwood, Christina M, and Starita, Lea M
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Biomedical and Clinical Sciences ,Clinical Sciences ,Lung ,HIV/AIDS ,Clinical Research ,Good Health and Well Being ,Seattle Flu Study Investigators - Abstract
The urgent need for massively scaled clinical or surveillance testing for SARS-CoV-2 has necessitated a reconsideration of the methods by which respiratory samples are collected, transported, processed and tested. Conventional testing for SARS-CoV-2 involves collection of a clinical specimen with a nasopharyngeal swab, storage of the swab during transport in universal transport medium (UTM), extraction of RNA, and quantitative reverse transcription PCR (RT-qPCR). As testing has scaled across the world, supply chain challenges have emerged across this entire workflow. Here we sought to evaluate how eliminating the UTM storage and RNA extraction steps would impact the results of molecular testing. Using paired mid-turbinate swabs self-collected by 11 individuals with previously established SARS-CoV-2 positivity, we performed a comparison of conventional (swab → UTM → RNA extraction → RT-qPCR) vs. simplified (direct elution from dry swab → RT-qPCR) protocols. Our results suggest that dry swabs eluted directly into a simple buffered solution (TE) can support molecular detection of SARS-CoV-2 via endpoint RT-qPCR without substantially compromising sensitivity. Although further confirmation with a larger sample size and variation of other parameters is necessary, these results are encouraging for the possibility of a simplified workflow that could support massively scaled testing for COVID-19 control.
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- 2020
16. A Genomic Survey of SARS-CoV-2 Reveals Multiple Introductions into Northern California without a Predominant Lineage
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Deng, Xianding, Gu, Wei, Federman, Scot, du Plessis, Louis, Pybus, Oliver G, Faria, Nuno, Wang, Candace, Yu, Guixia, Pan, Chao-Yang, Guevara, Hugo, Sotomayor-Gonzalez, Alicia, Zorn, Kelsey, Gopez, Allan, Servellita, Venice, Hsu, Elaine, Miller, Steve, Bedford, Trevor, Greninger, Alexander L, Roychoudhury, Pavitra, Starita, Lea M, Famulare, Michael, Chu, Helen Y, Shendure, Jay, Jerome, Keith R, Anderson, Catie, Gangavarapu, Karthik, Zeller, Mark, Spencer, Emily, Andersen, Kristian G, MacCannell, Duncan, Paden, Clinton R, Li, Yan, Zhang, Jing, Tong, Suxiang, Armstrong, Gregory, Morrow, Scott, Willis, Matthew, Matyas, Bela T, Mase, Sundari, Kasirye, Olivia, Park, Maggie, Chan, Curtis, Yu, Alexander T, Chai, Shua J, Villarino, Elsa, Bonin, Brandon, Wadford, Debra A, and Chiu, Charles Y
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Medical Microbiology ,Biomedical and Clinical Sciences ,Biological Sciences ,Lung ,Infectious Diseases ,Biotechnology ,Prevention ,Biodefense ,Emerging Infectious Diseases ,Vaccine Related ,Aetiology ,2.2 Factors relating to the physical environment ,Infection ,Good Health and Well Being - Abstract
The COVID-19 pandemic caused by the novel coronavirus SARS-CoV-2 has spread globally, resulting in >300,000 reported cases worldwide as of March 21st, 2020. Here we investigate the genetic diversity and genomic epidemiology of SARS-CoV-2 in Northern California using samples from returning travelers, cruise ship passengers, and cases of community transmission with unclear infection sources. Virus genomes were sampled from 29 patients diagnosed with COVID-19 infection from Feb 3rd through Mar 15th. Phylogenetic analyses revealed at least 8 different SARS-CoV-2 lineages, suggesting multiple independent introductions of the virus into the state. Virus genomes from passengers on two consecutive excursions of the Grand Princess cruise ship clustered with those from an established epidemic in Washington State, including the WA1 genome representing the first reported case in the United States on January 19th. We also detected evidence for presumptive transmission of SARS-CoV-2 lineages from one community to another. These findings suggest that cryptic transmission of SARS-CoV-2 in Northern California to date is characterized by multiple transmission chains that originate via distinct introductions from international and interstate travel, rather than widespread community transmission of a single predominant lineage. Rapid testing and contact tracing, social distancing, and travel restrictions are measures that will help to slow SARS-CoV-2 spread in California and other regions of the USA.
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- 2020
17. Fine-scale spatial and social patterns of SARS-CoV-2 transmission from identical pathogen sequences
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Tran-Kiem, Cécile, primary, Paredes, Miguel I., additional, Perofsky, Amanda C., additional, Frisbie, Lauren A., additional, Xie, Hong, additional, Kong, Kevin, additional, Weixler, Amelia, additional, Greninger, Alexander L., additional, Roychoudhury, Pavitra, additional, Peterson, JohnAric M., additional, Delgado, Andrew, additional, Holstead, Holly, additional, MacKellar, Drew, additional, Dykema, Philip, additional, Gamboa, Luis, additional, Frazar, Chris D., additional, Ryke, Erica, additional, Stone, Jeremy, additional, Reinhart, David, additional, Starita, Lea, additional, Thibodeau, Allison, additional, Yun, Cory, additional, Aragona, Frank, additional, Black, Allison, additional, Viboud, Cécile, additional, and Bedford, Trevor, additional
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- 2024
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18. A Bayesian approach to infer recombination patterns in coronaviruses
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Müller, Nicola F., Kistler, Kathryn E., and Bedford, Trevor
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- 2022
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19. Genomic surveillance of SARS-CoV-2 Omicron variants on a university campus
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Weil, Ana A., Luiten, Kyle G., Casto, Amanda M., Bennett, Julia C., O’Hanlon, Jessica, Han, Peter D., Gamboa, Luis S., McDermot, Evan, Truong, Melissa, Gottlieb, Geoffrey S., Acker, Zack, Wolf, Caitlin R., Magedson, Ariana, Chow, Eric J., Lo, Natalie K., Pothan, Lincoln C., McDonald, Devon, Wright, Tessa C., McCaffrey, Kathryn M., Figgins, Marlin D., Englund, Janet A., Boeckh, Michael, Lockwood, Christina M., Nickerson, Deborah A., Shendure, Jay, Bedford, Trevor, Hughes, James P., Starita, Lea M., and Chu, Helen Y.
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- 2022
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20. Fitness models provide accurate short-term forecasts of SARS-CoV-2 variant frequency.
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Abousamra, Eslam, Figgins, Marlin, and Bedford, Trevor
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SARS-CoV-2 Omicron variant ,SARS-CoV-2 Delta variant ,COVID-19 pandemic ,SARS-CoV-2 ,RANDOM walks - Abstract
Genomic surveillance of pathogen evolution is essential for public health response, treatment strategies, and vaccine development. In the context of SARS-COV-2, multiple models have been developed including Multinomial Logistic Regression (MLR) describing variant frequency growth as well as Fixed Growth Advantage (FGA), Growth Advantage Random Walk (GARW) and Piantham parameterizations describing variant R
t . These models provide estimates of variant fitness and can be used to forecast changes in variant frequency. We introduce a framework for evaluating real-time forecasts of variant frequencies, and apply this framework to the evolution of SARS-CoV-2 during 2022 in which multiple new viral variants emerged and rapidly spread through the population. We compare models across representative countries with different intensities of genomic surveillance. Retrospective assessment of model accuracy highlights that most models of variant frequency perform well and are able to produce reasonable forecasts. We find that the simple MLR model provides ∼0.6% median absolute error and ∼6% mean absolute error when forecasting 30 days out for countries with robust genomic surveillance. We investigate impacts of sequence quantity and quality across countries on forecast accuracy and conduct systematic downsampling to identify that 1000 sequences per week is fully sufficient for accurate short-term forecasts. We conclude that fitness models represent a useful prognostic tool for short-term evolutionary forecasting. Author summary: Over the course of the COVID-19 pandemic, SARS-CoV-2 evolved into many different genetic variants such as the well known Alpha, Beta, Gamma and Delta variants in early 2021 and the Omicron variant in late 2021. These genetic variants could more easily spread from person to person and so outcompeted previous versions of the virus. Even if they aren't being given Greek letter names, new variants are still arising with recent waves of COVID-19 caused by variants such as XBB and JN.1. Predicting which variants will increase in frequency and which variants will decrease in frequency is important for public health, particularly in terms of updating the formulation of the annual COVID-19 vaccine. In this paper, we investigate statistical models that use observed frequencies of different variants in the past weeks to estimate the frequency of different variants today and to forecast the frequency of different variants in 30 days time. We find that in countries with sufficient amounts and timeliness of genetic sequence data, that models forecast well and can be a useful tool for public health. [ABSTRACT FROM AUTHOR]- Published
- 2024
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21. Comparative Diagnostic Utility of SARS-CoV-2 Rapid Antigen and Molecular Testing in a Community Setting.
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Kim, Ashley E, Bennett, Julia C, Luiten, Kyle, O'Hanlon, Jessica A, Wolf, Caitlin R, Magedson, Ariana, Han, Peter D, Acker, Zack, Regelbrugge, Lani, McCaffrey, Kathryn M, Stone, Jeremey, Reinhart, David, Capodanno, Benjamin J, Morse, Stephen S, Bedford, Trevor, Englund, Janet A, Boeckh, Michael, Starita, Lea M, Uyeki, Timothy M, and Carone, Marco
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REVERSE transcriptase polymerase chain reaction ,RAPID diagnostic tests ,SARS-CoV-2 Omicron variant ,ANTIGEN analysis ,SARS-CoV-2 - Abstract
Background SARS-CoV-2 antigen-detection rapid diagnostic tests (Ag-RDTs) have become widely utilized but longitudinal characterization of their community-based performance remains incompletely understood. Methods This prospective longitudinal study at a large public university in Seattle, WA utilized remote enrollment, online surveys, and self-collected nasal swab specimens to evaluate Ag-RDT performance against real-time reverse transcription polymerase chain reaction (rRT-PCR) in the context of SARS-CoV-2 Omicron. Ag-RDT sensitivity and specificity within 1 day of rRT-PCR were evaluated by symptom status throughout the illness episode and Orf1b cycle threshold (Ct). Results From February to December 2022, 5757 participants reported 17 572 Ag-RDT results and completed 12 674 rRT-PCR tests, of which 995 (7.9%) were rRT-PCR positive. Overall sensitivity and specificity were 53.0% (95% confidence interval [CI], 49.6%–56.4%) and 98.8% (95% CI, 98.5%–99.0%), respectively. Sensitivity was comparatively higher for Ag-RDTs used 1 day after rRT-PCR (69.0%), 4–7 days after symptom onset (70.1%), and Orf1b Ct ≤20 (82.7%). Serial Ag-RDT sensitivity increased with repeat testing ≥ 2 (68.5%) and ≥ 4 (75.8%) days after an initial Ag-RDT-negative result. Conclusions Ag-RDT performance varied by clinical characteristics and temporal testing patterns. Our findings support recommendations for serial testing following an initial Ag-RDT-negative result, especially among recently symptomatic persons or those at high risk for SARS-CoV-2 infection. [ABSTRACT FROM AUTHOR]
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- 2024
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22. Bayesian nonparametric clustering in phylogenetics: modeling antigenic evolution in influenza
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Cybis, Gabriela B, Sinsheimer, Janet S, Bedford, Trevor, Rambaut, Andrew, Lemey, Philippe, and Suchard, Marc A
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Infectious Diseases ,Vaccine Related ,Pneumonia & Influenza ,Biodefense ,Influenza ,Prevention ,Emerging Infectious Diseases ,Infection ,Antigens ,Viral ,Bayes Theorem ,Biostatistics ,Cluster Analysis ,Evolution ,Molecular ,Humans ,Influenza A Virus ,H1N1 Subtype ,Influenza ,Human ,Likelihood Functions ,Models ,Genetic ,Models ,Immunological ,Molecular Epidemiology ,Phylogeny ,Statistics ,Nonparametric ,Stochastic Processes ,Bayesian nonparametric mixture models ,phylodynamics ,antigenic cartography ,Statistics ,Public Health and Health Services ,Statistics & Probability - Abstract
Influenza is responsible for up to 500,000 deaths every year, and antigenic variability represents much of its epidemiological burden. To visualize antigenic differences across many viral strains, antigenic cartography methods use multidimensional scaling on binding assay data to map influenza antigenicity onto a low-dimensional space. Analysis of such assay data ideally leads to natural clustering of influenza strains of similar antigenicity that correlate with sequence evolution. To understand the dynamics of these antigenic groups, we present a framework that jointly models genetic and antigenic evolution by combining multidimensional scaling of binding assay data, Bayesian phylogenetic machinery and nonparametric clustering methods. We propose a phylogenetic Chinese restaurant process that extends the current process to incorporate the phylogenetic dependency structure between strains in the modeling of antigenic clusters. With this method, we are able to use the genetic information to better understand the evolution of antigenicity throughout epidemics, as shown in applications of this model to H1N1 influenza. Copyright © 2017 John Wiley & Sons, Ltd.
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- 2018
23. Estimating the reproduction number and transmission heterogeneity from the size distribution of clusters of identical pathogen sequences
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Tran-Kiem, Cécile, primary and Bedford, Trevor, additional
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- 2024
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24. Local-scale phylodynamics reveal differential community impact of SARS-CoV-2 in a metropolitan US county
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Paredes, Miguel I., primary, Perofsky, Amanda C., additional, Frisbie, Lauren, additional, Moncla, Louise H., additional, Roychoudhury, Pavitra, additional, Xie, Hong, additional, Bakhash, Shah A. Mohamed, additional, Kong, Kevin, additional, Arnould, Isabel, additional, Nguyen, Tien V., additional, Wendm, Seffir T., additional, Hajian, Pooneh, additional, Ellis, Sean, additional, Mathias, Patrick C., additional, Greninger, Alexander L., additional, Starita, Lea M., additional, Frazar, Chris D., additional, Ryke, Erica, additional, Zhong, Weizhi, additional, Gamboa, Luis, additional, Threlkeld, Machiko, additional, Lee, Jover, additional, Stone, Jeremy, additional, McDermot, Evan, additional, Truong, Melissa, additional, Shendure, Jay, additional, Oltean, Hanna N., additional, Viboud, Cécile, additional, Chu, Helen, additional, Müller, Nicola F., additional, and Bedford, Trevor, additional
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- 2024
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25. Integrating dynamical modeling and phylogeographic inference to characterize global influenza circulation
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Parino, Francesco, primary, Gustani-Buss, Emanuele, additional, Bedford, Trevor, additional, Suchard, Marc A., additional, Trovão, Nídia Sequeira, additional, Rambaut, Andrew, additional, Colizza, Vittoria, additional, Poletto, Chiara, additional, and Lemey, Philippe, additional
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- 2024
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26. SARS-CoV-2 diversity and transmission on a university campus across two academic years during the pandemic
- Author
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Casto, Amanda M, primary, Paredes, Miguel I, additional, Bennett, Julia Catherine, additional, Luiten, Kyle G, additional, O'Hanlon, Jessica A., additional, Han, Peter D., additional, Gamboa, Luis, additional, McDermot, Evan, additional, Truong, Melissa, additional, Gottlieb, Geoffrey S, additional, Acker, Zack, additional, Wolf, Caitlin R, additional, Magedson, Ariana, additional, Lo, Natalie K., additional, McDonald, Devon, additional, Wright, Tessa C, additional, McCaffrey, Kathryn, additional, Figgins, Marlin D, additional, Englund, Janet A, additional, Boeckh, Michael, additional, Lockwood, Christina M, additional, Nickerson, Deborah A., additional, Shendure, Jay, additional, Uyeki, Timothy M, additional, Starita, Lea M, additional, Bedford, Trevor, additional, Chu, Helen Y, additional, and Weil, Ana A, additional
- Published
- 2024
- Full Text
- View/download PDF
27. Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States
- Author
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Perofsky, Amanda C, primary, Huddleston, John, additional, Hansen, Chelsea, additional, Barnes, John R, additional, Rowe, Thomas, additional, Xu, Xiyan, additional, Kondor, Rebecca, additional, Wentworth, David E, additional, Lewis, Nicola, additional, Whittaker, Lynne, additional, Ermetal, Burcu, additional, Harvey, Ruth, additional, Galiano, Monica, additional, Daniels, Rodney Stuart, additional, McCauley, John W, additional, Fujisaki, Seiichiro, additional, Nakamura, Kazuya, additional, Kishida, Noriko, additional, Watanabe, Shinji, additional, Hasegawa, Hideki, additional, Sullivan, Sheena G, additional, Barr, Ian G, additional, Subbarao, Kanta, additional, Krammer, Florian, additional, Bedford, Trevor, additional, and Viboud, Cécile, additional
- Published
- 2024
- Full Text
- View/download PDF
28. Dimensionality reduction distills complex evolutionary relationships in seasonal influenza and SARS-CoV-2
- Author
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Nanduri, Sravani, primary, Black, Allison, additional, Bedford, Trevor, additional, and Huddleston, John, additional
- Published
- 2024
- Full Text
- View/download PDF
29. Abstract IA016: Bayesian phylodynamics to quantify cancer growth across space and time
- Author
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Lewinsohn, Maya, primary, Bedford, Trevor, additional, Müller, Nicola F., additional, and Feder, Alison, additional
- Published
- 2024
- Full Text
- View/download PDF
30. Emergence and expansion of SARS-CoV-2 B.1.526 after identification in New York
- Author
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Annavajhala, Medini K., Mohri, Hiroshi, Wang, Pengfei, Nair, Manoj, Zucker, Jason E., Sheng, Zizhang, Gomez-Simmonds, Angela, Kelley, Anne L., Tagliavia, Maya, Huang, Yaoxing, Bedford, Trevor, Ho, David D., and Uhlemann, Anne-Catrin
- Subjects
Company distribution practices ,Environmental issues ,Science and technology ,Zoology and wildlife conservation - Abstract
SARS-CoV-2 infections have surged across the globe in recent months, concomitant with considerable viral evolution.sup.1-3. Extensive mutations in the spike protein may threaten the efficacy of vaccines and therapeutic monoclonal antibodies.sup.4. Two signature spike mutations of concern are E484K, which has a crucial role in the loss of neutralizing activity of antibodies, and N501Y, a driver of rapid worldwide transmission of the B.1.1.7 lineage. Here we report the emergence of the variant lineage B.1.526 (also known as the Iota variant.sup.5), which contains E484K, and its rise to dominance in New York City in early 2021. This variant is partially or completely resistant to two therapeutic monoclonal antibodies that are in clinical use and is less susceptible to neutralization by plasma from individuals who had recovered from SARS-CoV-2 infection or serum from vaccinated individuals, posing a modest antigenic challenge. The presence of the B.1.526 lineage has now been reported in all 50 states in the United States and in many other countries. B.1.526 rapidly replaced earlier lineages in New York, with an estimated transmission advantage of 35%. These transmission dynamics, together with the relative antibody resistance of its E484K sub-lineage, are likely to have contributed to the sharp rise and rapid spread of B.1.526. Although SARS-CoV-2 B.1.526 initially outpaced B.1.1.7 in the region, its growth subsequently slowed concurrently with the rise of B.1.1.7 and ensuing variants. The dynamics of the spread of the SARS-CoV-2 variant B.1.526 suggest that resistance to neutralization by antibodies may evolve in other variants and contribute to the spread of COVID-19., Author(s): Medini K. Annavajhala [sup.1] , Hiroshi Mohri [sup.2] , Pengfei Wang [sup.2] , Manoj Nair [sup.2] , Jason E. Zucker [sup.1] , Zizhang Sheng [sup.2] , Angela Gomez-Simmonds [sup.1] [...]
- Published
- 2021
- Full Text
- View/download PDF
31. Evidence for Limited Early Spread of COVID-19 Within the United States, January–February 2020
- Author
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CDC COVID-19 Response Team, Jorden, Michelle A., Rudman, Sarah L., Villarino, Elsa, Hoferka, Stacey, Patel, Megan T., Bemis, Kelley, Simmons, Cristal R., Jespersen, Megan, Johnson, Jenna Iberg, Mytty, Elizabeth, Arends, Katherine D., Henderson, Justin J., Mathes, Robert W., Weng, Charlene X., Duchin, Jeffrey, Lenahan, Jennifer, Close, Natasha, Bedford, Trevor, Boeckh, Michael, Chu, Helen Y., Englund, Janet A., Famulare, Michael, Nickerson, Deborah A., Rieder, Mark J., Shendure, Jay, and Starita, Lea M.
- Published
- 2020
32. Explaining the geographic origins of seasonal influenza A (H3N2)
- Author
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Wen, Frank, Bedford, Trevor, and Cobey, Sarah
- Subjects
Quantitative Biology - Populations and Evolution - Abstract
Most antigenically novel and evolutionarily successful strains of seasonal influenza A (H3N2) originate in East, South, and Southeast Asia. To understand this pattern, we simulated the ecological and evolutionary dynamics of influenza in a host metapopulation representing the temperate north, tropics, and temperate south. Although seasonality and air traffic are frequently used to explain global migratory patterns of influenza, we find that other factors may have a comparable or greater impact. Notably, a region's basic reproductive number ($R_0$) strongly affects the antigenic evolution of its viral population and the probability that its strains will spread and fix globally: a 17-28% higher $R_0$ in one region can explain the observed patterns. Seasonality, in contrast, increases the probability that a tropical (less seasonal) population will export evolutionarily successful strains but alone does not predict that these strains will be antigenically advanced. The relative sizes of different host populations, their birth and death rates, and the region in which H3N2 first appears affect influenza's phylogeography in different but relatively minor ways. These results suggest general principles that dictate the spatial dynamics of antigenically evolving pathogens and offer predictions for how changes in human ecology might affect influenza evolution., Comment: Included analyses of more complex metapopulations. Added clarifications to the text
- Published
- 2016
33. Virus genomes reveal factors that spread and sustained the Ebola epidemic
- Author
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Dudas, Gytis, Carvalho, Luiz Max, Bedford, Trevor, Tatem, Andrew J, Baele, Guy, Faria, Nuno R, Park, Daniel J, Ladner, Jason T, Arias, Armando, Asogun, Danny, Bielejec, Filip, Caddy, Sarah L, Cotten, Matthew, D’Ambrozio, Jonathan, Dellicour, Simon, Di Caro, Antonino, Diclaro, Joseph W, Duraffour, Sophie, Elmore, Michael J, Fakoli, Lawrence S, Faye, Ousmane, Gilbert, Merle L, Gevao, Sahr M, Gire, Stephen, Gladden-Young, Adrianne, Gnirke, Andreas, Goba, Augustine, Grant, Donald S, Haagmans, Bart L, Hiscox, Julian A, Jah, Umaru, Kugelman, Jeffrey R, Liu, Di, Lu, Jia, Malboeuf, Christine M, Mate, Suzanne, Matthews, David A, Matranga, Christian B, Meredith, Luke W, Qu, James, Quick, Joshua, Pas, Suzan D, Phan, My VT, Pollakis, Georgios, Reusken, Chantal B, Sanchez-Lockhart, Mariano, Schaffner, Stephen F, Schieffelin, John S, Sealfon, Rachel S, Simon-Loriere, Etienne, Smits, Saskia L, Stoecker, Kilian, Thorne, Lucy, Tobin, Ekaete Alice, Vandi, Mohamed A, Watson, Simon J, West, Kendra, Whitmer, Shannon, Wiley, Michael R, Winnicki, Sarah M, Wohl, Shirlee, Wölfel, Roman, Yozwiak, Nathan L, Andersen, Kristian G, Blyden, Sylvia O, Bolay, Fatorma, Carroll, Miles W, Dahn, Bernice, Diallo, Boubacar, Formenty, Pierre, Fraser, Christophe, Gao, George F, Garry, Robert F, Goodfellow, Ian, Günther, Stephan, Happi, Christian T, Holmes, Edward C, Kargbo, Brima, Keïta, Sakoba, Kellam, Paul, Koopmans, Marion PG, Kuhn, Jens H, Loman, Nicholas J, Magassouba, N’Faly, Naidoo, Dhamari, Nichol, Stuart T, Nyenswah, Tolbert, Palacios, Gustavo, Pybus, Oliver G, Sabeti, Pardis C, Sall, Amadou, Ströher, Ute, Wurie, Isatta, Suchard, Marc A, Lemey, Philippe, and Rambaut, Andrew
- Subjects
Infectious Diseases ,Emerging Infectious Diseases ,Biodefense ,Vaccine Related ,Prevention ,Infection ,Good Health and Well Being ,Climate ,Disease Outbreaks ,Ebolavirus ,Genome ,Viral ,Geography ,Hemorrhagic Fever ,Ebola ,Humans ,Internationality ,Linear Models ,Molecular Epidemiology ,Phylogeny ,Travel ,General Science & Technology - Abstract
The 2013-2016 West African epidemic caused by the Ebola virus was of unprecedented magnitude, duration and impact. Here we reconstruct the dispersal, proliferation and decline of Ebola virus throughout the region by analysing 1,610 Ebola virus genomes, which represent over 5% of the known cases. We test the association of geography, climate and demography with viral movement among administrative regions, inferring a classic 'gravity' model, with intense dispersal between larger and closer populations. Despite attenuation of international dispersal after border closures, cross-border transmission had already sown the seeds for an international epidemic, rendering these measures ineffective at curbing the epidemic. We address why the epidemic did not spread into neighbouring countries, showing that these countries were susceptible to substantial outbreaks but at lower risk of introductions. Finally, we reveal that this large epidemic was a heterogeneous and spatially dissociated collection of transmission clusters of varying size, duration and connectivity. These insights will help to inform interventions in future epidemics.
- Published
- 2017
34. Viral factors in influenza pandemic risk assessment.
- Author
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Lipsitch, Marc, Barclay, Wendy, Raman, Rahul, Russell, Charles J, Belser, Jessica A, Cobey, Sarah, Kasson, Peter M, Lloyd-Smith, James O, Maurer-Stroh, Sebastian, Riley, Steven, Beauchemin, Catherine Aa, Bedford, Trevor, Friedrich, Thomas C, Handel, Andreas, Herfst, Sander, Murcia, Pablo R, Roche, Benjamin, Wilke, Claus O, and Russell, Colin A
- Subjects
Animals ,Humans ,Influenza A virus ,Zoonoses ,RNA Replicase ,Hemagglutinin Glycoproteins ,Influenza Virus ,Virulence Factors ,Risk Assessment ,Influenza ,Human ,Pandemics ,Epidemiological Monitoring ,epidemiology ,global health ,human ,infectious disease ,influenza A ,microbiology ,pandemic ,risk prediction ,virus ,Hemagglutinin Glycoproteins ,Influenza Virus ,Influenza ,Human ,Biochemistry and Cell Biology - Abstract
The threat of an influenza A virus pandemic stems from continual virus spillovers from reservoir species, a tiny fraction of which spark sustained transmission in humans. To date, no pandemic emergence of a new influenza strain has been preceded by detection of a closely related precursor in an animal or human. Nonetheless, influenza surveillance efforts are expanding, prompting a need for tools to assess the pandemic risk posed by a detected virus. The goal would be to use genetic sequence and/or biological assays of viral traits to identify those non-human influenza viruses with the greatest risk of evolving into pandemic threats, and/or to understand drivers of such evolution, to prioritize pandemic prevention or response measures. We describe such efforts, identify progress and ongoing challenges, and discuss three specific traits of influenza viruses (hemagglutinin receptor binding specificity, hemagglutinin pH of activation, and polymerase complex efficiency) that contribute to pandemic risk.
- Published
- 2016
35. Prediction, dynamics, and visualization of antigenic phenotypes of seasonal influenza viruses
- Author
-
Neher, Richard A., Bedford, Trevor, Daniels, Rodney S., Russell, Colin A., and Shraiman, Boris I.
- Subjects
Quantitative Biology - Populations and Evolution - Abstract
Human seasonal influenza viruses evolve rapidly, enabling the virus population to evade immunity and re-infect previously infected individuals. Antigenic properties are largely determined by the surface glycoprotein hemagglutinin (HA) and amino acid substitutions at exposed epitope sites in HA mediate loss of recognition by antibodies. Here, we show that antigenic differences measured through serological assay data are well described by a sum of antigenic changes along the path connecting viruses in a phylogenetic tree. This mapping onto the tree allows prediction of antigenicity from HA sequence data alone. The mapping can further be used to make predictions about the makeup of the future seasonal influenza virus population, and we compare predictions between models with serological and sequence data. To make timely model output readily available, we developed a web browser based application that visualizes antigenic data on a continuously updated phylogeny., Comment: visualization available at http://HI.nextflu.org
- Published
- 2015
- Full Text
- View/download PDF
36. Quantifying and mitigating the effect of preferential sampling on phylodynamic inference
- Author
-
Karcher, Michael D., Palacios, Julia A., Bedford, Trevor, Suchard, Marc A., and Minin, Vladimir N.
- Subjects
Statistics - Methodology ,Quantitative Biology - Populations and Evolution - Abstract
Phylodynamics seeks to estimate effective population size fluctuations from molecular sequences of individuals sampled from a population of interest. One way to accomplish this task formulates an observed sequence data likelihood exploiting a coalescent model for the sampled individuals' genealogy and then integrating over all possible genealogies via Monte Carlo or, less efficiently, by conditioning on one genealogy estimated from the sequence data. However, when analyzing sequences sampled serially through time, current methods implicitly assume either that sampling times are fixed deterministically by the data collection protocol or that their distribution does not depend on the size of the population. Through simulation, we first show that, when sampling times do probabilistically depend on effective population size, estimation methods may be systematically biased. To correct for this deficiency, we propose a new model that explicitly accounts for preferential sampling by modeling the sampling times as an inhomogeneous Poisson process dependent on effective population size. We demonstrate that in the presence of preferential sampling our new model not only reduces bias, but also improves estimation precision. Finally, we compare the performance of the currently used phylodynamic methods with our proposed model through clinically-relevant, seasonal human influenza examples., Comment: 30 pages, 7 figures plust 7 appendix figures
- Published
- 2015
- Full Text
- View/download PDF
37. Epidemiological and evolutionary analysis of the 2014 Ebola virus outbreak
- Author
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Łuksza, Marta, Bedford, Trevor, and Lässig, Michael
- Subjects
Quantitative Biology - Populations and Evolution - Abstract
The 2014 epidemic of the Ebola virus is governed by a genetically diverse viral population. In the early Sierra Leone outbreak, a recent study has identified new mutations that generate genetically distinct sequence clades. Here we find evidence that major Sierra Leone clades have systematic differences in growth rate and reproduction number. If this growth heterogeneity remains stable, it will generate major shifts in clade frequencies and influence the overall epidemic dynamics on time scales within the current outbreak. Our method is based on simple summary statistics of clade growth, which can be inferred from genealogical trees with an underlying clade-specific birth-death model of the infection dynamics. This method can be used to perform realtime tracking of an evolving epidemic and identify emerging clades of epidemiological or evolutionary significance.
- Published
- 2014
38. Integration of genomic sequencing into the response to the Ebola virus outbreak in Nord Kivu, Democratic Republic of the Congo
- Author
-
Kinganda-Lusamaki, Eddy, Black, Allison, Mukadi, Daniel B., Hadfield, James, Mbala-Kingebeni, Placide, Pratt, Catherine B., Aziza, Amuri, Diagne, Moussa M., White, Bailey, Bisento, Nella, Nsunda, Bibiche, Akonga, Marceline, Faye, Martin, Faye, Ousmane, Edidi-Atani, Francois, Matondo-Kuamfumu, Meris, Mambu-Mbika, Fabrice, Bulabula, Junior, Di Paola, Nicholas, Pauthner, Matthias G., Andersen, Kristian G., Palacios, Gustavo, Delaporte, Eric, Sall, Amadou Alpha, Peeters, Martine, Wiley, Michael R., Ahuka-Mundeke, Steve, Bedford, Trevor, and Tamfum, Jean-Jacques Muyembe
- Published
- 2021
- Full Text
- View/download PDF
39. Prediction, dynamics, and visualization of antigenic phenotypes of seasonal influenza viruses
- Author
-
Neher, Richard A, Bedford, Trevor, Daniels, Rodney S, Russell, Colin A, and Shraiman, Boris I
- Subjects
Influenza ,Biodefense ,Emerging Infectious Diseases ,Pneumonia & Influenza ,Vaccine Related ,Infectious Diseases ,Prevention ,Infection ,Amino Acid Sequence ,Antigenic Variation ,Antigens ,Viral ,Computer Graphics ,Computer Simulation ,Evolution ,Molecular ,Forecasting ,Hemagglutinin Glycoproteins ,Influenza Virus ,Humans ,Influenza A Virus ,H1N1 Subtype ,Influenza A Virus ,H3N2 Subtype ,Influenza Vaccines ,Influenza ,Human ,Influenzavirus B ,Models ,Immunological ,Molecular Sequence Data ,Phenotype ,Phylogeny ,Seasons ,Software ,evolution ,antigenic distance ,phylogenetic tree ,q-bio.PE - Abstract
Human seasonal influenza viruses evolve rapidly, enabling the virus population to evade immunity and reinfect previously infected individuals. Antigenic properties are largely determined by the surface glycoprotein hemagglutinin (HA), and amino acid substitutions at exposed epitope sites in HA mediate loss of recognition by antibodies. Here, we show that antigenic differences measured through serological assay data are well described by a sum of antigenic changes along the path connecting viruses in a phylogenetic tree. This mapping onto the tree allows prediction of antigenicity from HA sequence data alone. The mapping can further be used to make predictions about the makeup of the future A(H3N2) seasonal influenza virus population, and we compare predictions between models with serological and sequence data. To make timely model output readily available, we developed a web browser-based application that visualizes antigenic data on a continuously updated phylogeny.
- Published
- 2016
40. Quantifying and Mitigating the Effect of Preferential Sampling on Phylodynamic Inference.
- Author
-
Karcher, Michael D, Palacios, Julia A, Bedford, Trevor, Suchard, Marc A, and Minin, Vladimir N
- Subjects
Hemagglutinins ,Data Interpretation ,Statistical ,Models ,Statistical ,Sample Size ,Genetics ,Population ,Phylogeny ,Models ,Genetic ,Computer Simulation ,Influenza A Virus ,H3N2 Subtype ,Genetic Variation ,Biological Evolution ,Pneumonia & Influenza ,stat.ME ,q-bio.PE ,Data Interpretation ,Statistical ,Models ,Genetics ,Population ,Genetic ,Influenza A Virus ,H3N2 Subtype ,Bioinformatics ,Biological Sciences ,Information and Computing Sciences ,Mathematical Sciences - Abstract
Phylodynamics seeks to estimate effective population size fluctuations from molecular sequences of individuals sampled from a population of interest. One way to accomplish this task formulates an observed sequence data likelihood exploiting a coalescent model for the sampled individuals' genealogy and then integrating over all possible genealogies via Monte Carlo or, less efficiently, by conditioning on one genealogy estimated from the sequence data. However, when analyzing sequences sampled serially through time, current methods implicitly assume either that sampling times are fixed deterministically by the data collection protocol or that their distribution does not depend on the size of the population. Through simulation, we first show that, when sampling times do probabilistically depend on effective population size, estimation methods may be systematically biased. To correct for this deficiency, we propose a new model that explicitly accounts for preferential sampling by modeling the sampling times as an inhomogeneous Poisson process dependent on effective population size. We demonstrate that in the presence of preferential sampling our new model not only reduces bias, but also improves estimation precision. Finally, we compare the performance of the currently used phylodynamic methods with our proposed model through clinically-relevant, seasonal human influenza examples.
- Published
- 2016
41. Age-dependent heterogeneity in the antigenic effects of mutations to influenza hemagglutinin
- Author
-
Welsh, Frances C., primary, Eguia, Rachel T., additional, Lee, Juhye M., additional, Haddox, Hugh K., additional, Galloway, Jared, additional, Chau, Nguyen Van Vinh, additional, Loes, Andrea N., additional, Huddleston, John, additional, Yu, Timothy C., additional, Le, Mai Quynh, additional, Nhat, Nguyen T.D., additional, Thanh, Nguyen Thi Le, additional, Greninger, Alexander L., additional, Chu, Helen Y., additional, Englund, Janet A., additional, Bedford, Trevor, additional, Matsen, Frederick A., additional, Boni, Maciej F., additional, and Bloom, Jesse D., additional
- Published
- 2023
- Full Text
- View/download PDF
42. Human mobility impacts the transmission of common respiratory viruses: A modeling study of the Seattle metropolitan area
- Author
-
Perofsky, Amanda C., primary, Hansen, Chelsea, additional, Burstein, Roy, additional, Boyle, Shanda, additional, Prentice, Robin, additional, Marshall, Cooper, additional, Reinhart, David, additional, Capodanno, Ben, additional, Truong, Melissa, additional, Schwabe-Fry, Kristen, additional, Kuchta, Kayla, additional, Pfau, Brian, additional, Acker, Zack, additional, Lee, Jover, additional, Sibley, Thomas R., additional, McDermot, Evan, additional, Rodriguez-Salas, Leslie, additional, Stone, Jeremy, additional, Gamboa, Luis, additional, Han, Peter D., additional, Adler, Amanda, additional, Waghmare, Alpana, additional, Jackson, Michael L., additional, Famulare, Mike, additional, Shendure, Jay, additional, Bedford, Trevor, additional, Chu, Helen Y., additional, Englund, Janet A., additional, Starita, Lea M., additional, and Viboud, Cecile, additional
- Published
- 2023
- Full Text
- View/download PDF
43. Seasonality in the migration and establishment of H3N2 Influenza lineages with epidemic growth and decline
- Author
-
Zinder, Daniel, Bedford, Trevor, Baskerville, Edward B., Woods, Robert J., Roy, Manojit, and Pascual, Mercedes
- Subjects
Quantitative Biology - Populations and Evolution - Abstract
Background: Influenza A/H3N2 has been circulating in humans since 1968, causing considerable morbidity and mortality. Although H3N2 incidence is highly seasonal, how such seasonality contributes to global phylogeographic migration dynamics has not yet been established. Results: Incorporating seasonally varying migration rates improves the modeling of migration. In our global model, windows of increased immigration map to the seasonal timing of epidemic spread, while windows of increased emigration map to epidemic decline. Seasonal patterns also correlate with the probability that local lineages go extinct and fail to contribute to long term viral evolution, as measured through the trunk of the phylogeny. However, the fraction of the trunk in each community was found to be better determined by its overall human population size Conclusions: Seasonal migration and rapid turnover within regions is sustained by the invasion of 'fertile epidemic grounds' at the end of older epidemics. Thus, the current emphasis on connectivity, including air-travel, should be complemented with a better understanding of the conditions and timing required for successful establishment.Models which account for migration seasonality will improve our understanding of the seasonal drivers of influenza,enhance epidemiological predictions, and ameliorate vaccine updating by identifying strains that not only escape immunity but also have the seasonal opportunity to establish and spread. Further work is also needed on additional conditions that contribute to the persistence and long term evolution of influenza within the human population,such as spatial heterogeneity with respect to climate and seasonality, Comment: in BMC Evolutionary Biology 2014, 14
- Published
- 2014
- Full Text
- View/download PDF
44. Assessing phenotypic correlation through the multivariate phylogenetic latent liability model
- Author
-
Cybis, Gabriela B., Sinsheimer, Janet S., Bedford, Trevor, Mather, Alison E., Lemey, Philippe, and Suchard, Marc A.
- Subjects
Quantitative Biology - Populations and Evolution ,Statistics - Applications ,Statistics - Methodology - Abstract
Understanding which phenotypic traits are consistently correlated throughout evolution is a highly pertinent problem in modern evolutionary biology. Here, we propose a multivariate phylogenetic latent liability model for assessing the correlation between multiple types of data, while simultaneously controlling for their unknown shared evolutionary history informed through molecular sequences. The latent formulation enables us to consider in a single model combinations of continuous traits, discrete binary traits and discrete traits with multiple ordered and unordered states. Previous approaches have entertained a single data type generally along a fixed history, precluding estimation of correlation between traits and ignoring uncertainty in the history. We implement our model in a Bayesian phylogenetic framework, and discuss inference techniques for hypothesis testing. Finally, we showcase the method through applications to columbine flower morphology, antibiotic resistance in Salmonella and epitope evolution in influenza., Comment: Published at http://dx.doi.org/10.1214/15-AOAS821 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)
- Published
- 2014
- Full Text
- View/download PDF
45. Identifying the genetic basis of antigenic change in influenza A(H1N1)
- Author
-
Harvey, William T., Gregory, Victoria, Benton, Donald J., Hall, James P. J., Daniels, Rodney S., Bedford, Trevor, Haydon, Daniel T., Hay, Alan J., McCauley, John W., and Reeve, Richard
- Subjects
Quantitative Biology - Populations and Evolution ,Quantitative Biology - Quantitative Methods - Abstract
Determining phenotype from genetic data is a fundamental challenge. Influenza A viruses undergo rapid antigenic drift and identification of emerging antigenic variants is critical to the vaccine selection process. Using former seasonal influenza A(H1N1) viruses, hemagglutinin sequence and corresponding antigenic data were analyzed in combination with 3-D structural information. We attributed variation in hemagglutination inhibition to individual amino acid substitutions and quantified their antigenic impact, validating a subset experimentally using reverse genetics. Substitutions identified as low-impact were shown to be a critical component of influenza antigenic evolution and by including these, as well as the high-impact substitutions often focused on, the accuracy of predicting antigenic phenotypes of emerging viruses from genotype was doubled. The ability to quantify the phenotypic impact of specific amino acid substitutions should help refine techniques that predict the fitness and evolutionary success of variant viruses, leading to stronger theoretical foundations for selection of candidate vaccine viruses.
- Published
- 2014
- Full Text
- View/download PDF
46. Quantifying evolutionary constraints on B cell affinity maturation
- Author
-
McCoy, Connor O., Bedford, Trevor, Minin, Vladimir N., Bradley, Philip, Robins, Harlan, and Matsen IV, Frederick A.
- Subjects
Quantitative Biology - Populations and Evolution - Abstract
The antibody repertoire of each individual is continuously updated by the evolutionary process of B cell receptor mutation and selection. It has recently become possible to gain detailed information concerning this process through high-throughput sequencing. Here, we develop modern statistical molecular evolution methods for the analysis of B cell sequence data, and then apply them to a very deep short-read data set of B cell receptors. We find that the substitution process is conserved across individuals but varies significantly across gene segments. We investigate selection on B cell receptors using a novel method that side-steps the difficulties encountered by previous work in differentiating between selection and motif-driven mutation; this is done through stochastic mapping and empirical Bayes estimators that compare the evolution of in-frame and out-of-frame rearrangements. We use this new method to derive a per-residue map of selection, which provides a more nuanced view of the constraints on framework and variable regions., Comment: Previously entitled "Substitution and site-specific selection driving B cell affinity maturation is consistent across individuals"
- Published
- 2014
- Full Text
- View/download PDF
47. Reassortment between influenza B lineages and the emergence of a co-adapted PB1-PB2-HA gene complex
- Author
-
Dudas, Gytis, Bedford, Trevor, Lycett, Samantha, and Rambaut, Andrew
- Subjects
Quantitative Biology - Populations and Evolution - Abstract
Influenza B viruses make a considerable contribution to morbidity attributed to seasonal influenza. Currently circulating influenza B isolates are known to belong to two antigenically distinct lineages referred to as B/Victoria and B/Yamagata. Frequent exchange of genomic segments of these two lineages has been noted in the past, but the observed patterns of reassortment have not been formalized in detail. We investigate inter-lineage reassortments by comparing phylogenetic trees across genomic segments. Our analyses indicate that of the 8 segments of influenza B viruses only PB1, PB2 and HA segments maintained separate Victoria and Yamagata lineages and that currently circulating strains possess PB1, PB2 and HA segments derived entirely from one or the other lineage; other segments have repeatedly reassorted between lineages thereby reducing genetic diversity. We argue that this difference between segments is due to selection against reassortant viruses with mixed lineage PB1, PB2 and HA segments. Given sufficient time and continued recruitment to the reassortment-isolated PB1-PB2-HA gene complex, we expect influenza B viruses to eventually undergo sympatric speciation., Comment: 16 pages, 8 figures in main text, 18 pages, 17 figures in supplementary text
- Published
- 2014
48. Cross-Sectional Prevalence of SARS-CoV-2 Among Skilled Nursing Facility Employees and Residents Across Facilities in Seattle
- Author
-
Weil, Ana A., Newman, Kira L., Ong, Thuan D., Davidson, Giana H., Logue, Jennifer, Brandstetter, Elisabeth, Magedson, Ariana, McDonald, Dylan, McCulloch, Denise J., Neme, Santiago, Lewis, James, Duchin, Jeff S., Zhong, Weizhi, Starita, Lea M., Bedford, Trevor, Roxby, Alison C., and Chu, Helen Y.
- Published
- 2020
- Full Text
- View/download PDF
49. Positive Selection in CD8+ T-Cell Epitopes of Influenza Virus Nucleoprotein Revealed by a Comparative Analysis of Human and Swine Viral Lineages
- Author
-
Machkovech, Heather M, Bedford, Trevor, Suchard, Marc A, and Bloom, Jesse D
- Subjects
Emerging Infectious Diseases ,Vaccine Related ,Influenza ,Infectious Diseases ,Biodefense ,Prevention ,Pneumonia & Influenza ,2.2 Factors relating to the physical environment ,Aetiology ,Infection ,Animals ,CD8-Positive T-Lymphocytes ,Epitopes ,T-Lymphocyte ,Humans ,Influenza A Virus ,H1N1 Subtype ,Influenza A Virus ,H1N2 Subtype ,Influenza A Virus ,H3N2 Subtype ,Influenza ,Human ,Nucleocapsid Proteins ,Orthomyxoviridae Infections ,RNA-Binding Proteins ,Selection ,Genetic ,Swine ,Viral Core Proteins ,Viral Matrix Proteins ,Biological Sciences ,Agricultural and Veterinary Sciences ,Medical and Health Sciences ,Virology - Abstract
UnlabelledNumerous experimental studies have demonstrated that CD8(+) T cells contribute to immunity against influenza by limiting viral replication. It is therefore surprising that rigorous statistical tests have failed to find evidence of positive selection in the epitopes targeted by CD8(+) T cells. Here we use a novel computational approach to test for selection in CD8(+) T-cell epitopes. We define all epitopes in the nucleoprotein (NP) and matrix protein (M1) with experimentally identified human CD8(+) T-cell responses and then compare the evolution of these epitopes in parallel lineages of human and swine influenza viruses that have been diverging since roughly 1918. We find a significant enrichment of substitutions that alter human CD8(+) T-cell epitopes in NP of human versus swine influenza virus, consistent with the idea that these epitopes are under positive selection. Furthermore, we show that epitope-altering substitutions in human influenza virus NP are enriched on the trunk versus the branches of the phylogenetic tree, indicating that viruses that acquire these mutations have a selective advantage. However, even in human influenza virus NP, sites in T-cell epitopes evolve more slowly than do nonepitope sites, presumably because these epitopes are under stronger inherent functional constraint. Overall, our work demonstrates that there is clear selection from CD8(+) T cells in human influenza virus NP and illustrates how comparative analyses of viral lineages from different hosts can identify positive selection that is otherwise obscured by strong functional constraint.ImportanceThere is a strong interest in correlates of anti-influenza immunity that are protective against diverse virus strains. CD8(+) T cells provide such broad immunity, since they target conserved viral proteins. An important question is whether T-cell immunity is sufficiently strong to drive influenza virus evolution. Although many studies have shown that T cells limit viral replication in animal models and are associated with decreased symptoms in humans, no studies have proven with statistical significance that influenza virus evolves under positive selection to escape T cells. Here we use comparisons of human and swine influenza viruses to rigorously demonstrate that human influenza virus evolves under pressure to fix mutations in the nucleoprotein that promote escape from T cells. We further show that viruses with these mutations have a selective advantage since they are preferentially located on the "trunk" of the phylogenetic tree. Overall, our results show that CD8(+) T cells targeting nucleoprotein play an important role in shaping influenza virus evolution.
- Published
- 2015
50. Tracing the Origin, Spread, and Molecular Evolution of Zika Virus in Puerto Rico, 2016-2017
- Author
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Santiago, Gilberto A., Kalinich, Chaney C., Cruz-Lopez, Fabiola, Gonzalez, Glenda L., Flores, Betzabel, Hentoff, Aaron, Charriez, Keyla N., Fauver, Joseph R., Adams, Laura E., Sharp, Tyler M., Black, Allison, Bedford, Trevor, Ellis, Esther, Ellis, Brett, Waterman, Steve H., Paz-Bailey, Gabriela, Grubaugh, Nathan D., and Munoz-Jordan, Jorge L.
- Subjects
Epidemics -- Causes of -- Distribution -- Puerto Rico ,Genomics -- Research ,Molecular evolution -- Research ,Virus research ,Company distribution practices ,Health - Abstract
Puerto Rico reported the first confirmed case of Zika virus (ZIKV) disease in November 2015 and subsequently experienced epidemic transmission that peaked by mid-August 2016 (1). Despite the large number [...]
- Published
- 2021
- Full Text
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