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Jung SM, Loo SL, Howerton E, Contamin L, Smith CP, Carcelén EC, Yan K, Bents SJ, Levander J, Espino J, Lemaitre JC, Sato K, McKee CD, Hill AL, Chinazzi M, Davis JT, Mu K, Vespignani A, Rosenstrom ET, Rodriguez-Cartes SA, Ivy JS, Mayorga ME, Swann JL, España G, Cavany S, Moore SM, Perkins TA, Chen S, Paul R, Janies D, Thill JC, Srivastava A, Aawar MA, Bi K, Bandekar SR, Bouchnita A, Fox SJ, Meyers LA, Porebski P, Venkatramanan S, Adiga A, Hurt B, Klahn B, Outten J, Chen J, Mortveit H, Wilson A, Hoops S, Bhattacharya P, Machi D, Vullikanti A, Lewis B, Marathe M, Hochheiser H, Runge MC, Shea K, Truelove S, Viboud C, Lessler J. Potential impact of annual vaccination with reformulated COVID-19 vaccines: Lessons from the US COVID-19 scenario modeling hub. PLoS Med 2024; 21:e1004387. [PMID: 38630802 PMCID: PMC11062554 DOI: 10.1371/journal.pmed.1004387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 05/01/2024] [Accepted: 03/27/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Coronavirus Disease 2019 (COVID-19) continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear. Here, we present projections of COVID-19 hospitalizations and deaths in the United States for the next 2 years under 2 plausible assumptions about immune escape (20% per year and 50% per year) and 3 possible CDC recommendations for the use of annually reformulated vaccines (no recommendation, vaccination for those aged 65 years and over, vaccination for all eligible age groups based on FDA approval). METHODS AND FINDINGS The COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023 and April 15, 2025 under 6 scenarios representing the intersection of considered levels of immune escape and vaccination. Annually reformulated vaccines are assumed to be 65% effective against symptomatic infection with strains circulating on June 15 of each year and to become available on September 1. Age- and state-specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster. State and national projections from 8 modeling teams were ensembled to produce projections for each scenario and expected reductions in disease outcomes due to vaccination over the projection period. From April 15, 2023 to April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November to January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% projection interval (PI) [1,438,000, 4,270,000]) hospitalizations and 209,000 (90% PI [139,000, 461,000]) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% confidence interval (CI) [104,000, 355,000]) fewer hospitalizations and 33,000 (95% CI [12,000, 54,000]) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000-598,000) fewer hospitalizations and 49,000 (95% CI [29,000, 69,000]) fewer deaths. CONCLUSIONS COVID-19 is projected to be a significant public health threat over the coming 2 years. Broad vaccination has the potential to substantially reduce the burden of this disease, saving tens of thousands of lives each year.
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Affiliation(s)
- Sung-mok Jung
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Sara L. Loo
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Emily Howerton
- The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Lucie Contamin
- University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Claire P. Smith
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Erica C. Carcelén
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Katie Yan
- The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Samantha J. Bents
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
| | - John Levander
- University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Jessi Espino
- University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Joseph C. Lemaitre
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Koji Sato
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Clifton D. McKee
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Alison L. Hill
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Matteo Chinazzi
- Northeastern University, Boston, Massachusetts, United States of America
| | - Jessica T. Davis
- Northeastern University, Boston, Massachusetts, United States of America
| | - Kunpeng Mu
- Northeastern University, Boston, Massachusetts, United States of America
| | | | - Erik T. Rosenstrom
- North Carolina State University, Raleigh, North Carolina, United States of America
| | | | - Julie S. Ivy
- North Carolina State University, Raleigh, North Carolina, United States of America
| | - Maria E. Mayorga
- North Carolina State University, Raleigh, North Carolina, United States of America
| | - Julie L. Swann
- North Carolina State University, Raleigh, North Carolina, United States of America
| | - Guido España
- University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Sean Cavany
- University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Sean M. Moore
- University of Notre Dame, Notre Dame, Indiana, United States of America
| | - T. Alex Perkins
- University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Shi Chen
- University of North Carolina at Charlotte, Charlotte, North Carolina, United States of America
| | - Rajib Paul
- University of North Carolina at Charlotte, Charlotte, North Carolina, United States of America
| | - Daniel Janies
- University of North Carolina at Charlotte, Charlotte, North Carolina, United States of America
| | - Jean-Claude Thill
- University of North Carolina at Charlotte, Charlotte, North Carolina, United States of America
| | - Ajitesh Srivastava
- University of Southern California, Los Angeles, California, United States of America
| | - Majd Al Aawar
- University of Southern California, Los Angeles, California, United States of America
| | - Kaiming Bi
- University of Texas at Austin, Austin, Texas, United States of America
| | | | - Anass Bouchnita
- University of Texas at El Paso, El Paso, Texas, United States of America
| | - Spencer J. Fox
- University of Georgia, Athens, Georgia, United States of America
| | | | | | | | - Aniruddha Adiga
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Benjamin Hurt
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Brian Klahn
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Joseph Outten
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Jiangzhuo Chen
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Henning Mortveit
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Amanda Wilson
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Stefan Hoops
- University of Virginia, Charlottesville, Virginia, United States of America
| | | | - Dustin Machi
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Anil Vullikanti
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Bryan Lewis
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Madhav Marathe
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Harry Hochheiser
- University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Michael C. Runge
- U.S. Geological Survey, Laurel, Maryland, United States of America
| | - Katriona Shea
- The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Shaun Truelove
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Justin Lessler
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
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Howerton E, Contamin L, Mullany LC, Qin M, Reich NG, Bents S, Borchering RK, Jung SM, Loo SL, Smith CP, Levander J, Kerr J, Espino J, van Panhuis WG, Hochheiser H, Galanti M, Yamana T, Pei S, Shaman J, Rainwater-Lovett K, Kinsey M, Tallaksen K, Wilson S, Shin L, Lemaitre JC, Kaminsky J, Hulse JD, Lee EC, McKee CD, Hill A, Karlen D, Chinazzi M, Davis JT, Mu K, Xiong X, Pastore Y Piontti A, Vespignani A, Rosenstrom ET, Ivy JS, Mayorga ME, Swann JL, España G, Cavany S, Moore S, Perkins A, Hladish T, Pillai A, Ben Toh K, Longini I, Chen S, Paul R, Janies D, Thill JC, Bouchnita A, Bi K, Lachmann M, Fox SJ, Meyers LA, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Hurt B, Chen J, Mortveit H, Wilson A, Marathe M, Hoops S, Bhattacharya P, Machi D, Cadwell BL, Healy JM, Slayton RB, Johansson MA, Biggerstaff M, Truelove S, Runge MC, Shea K, Viboud C, Lessler J. Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty. Nat Commun 2023; 14:7260. [PMID: 37985664 PMCID: PMC10661184 DOI: 10.1038/s41467-023-42680-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 10/17/2023] [Indexed: 11/22/2023] Open
Abstract
Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections.
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Affiliation(s)
- Emily Howerton
- The Pennsylvania State University, University Park, PA, USA.
| | | | - Luke C Mullany
- Johns Hopkins University Applied Physics Lab, Laurel, MD, USA
| | | | | | - Samantha Bents
- National Institutes of Health Fogarty International Center, Bethesda, MD, USA
| | - Rebecca K Borchering
- The Pennsylvania State University, University Park, PA, USA
- Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Sung-Mok Jung
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Sara L Loo
- Johns Hopkins University, Baltimore, MD, USA
| | | | | | | | - J Espino
- University of Pittsburgh, Pittsburgh, PA, USA
| | | | | | | | | | - Sen Pei
- Columbia University, New York, NY, USA
| | | | | | - Matt Kinsey
- Johns Hopkins University Applied Physics Lab, Laurel, MD, USA
| | - Kate Tallaksen
- Johns Hopkins University Applied Physics Lab, Laurel, MD, USA
| | - Shelby Wilson
- Johns Hopkins University Applied Physics Lab, Laurel, MD, USA
| | - Lauren Shin
- Johns Hopkins University Applied Physics Lab, Laurel, MD, USA
| | | | | | | | | | | | - Alison Hill
- Johns Hopkins University, Baltimore, MD, USA
| | - Dean Karlen
- University of Victoria, Victoria, BC, Canada
| | | | | | - Kunpeng Mu
- Northeastern University, Boston, MA, USA
| | | | | | | | | | - Julie S Ivy
- North Carolina State University, Raleigh, NC, USA
| | | | | | | | - Sean Cavany
- University of Notre Dame, Notre Dame, IN, USA
| | - Sean Moore
- University of Notre Dame, Notre Dame, IN, USA
| | | | | | | | | | | | - Shi Chen
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Rajib Paul
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Daniel Janies
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | | | | | - Kaiming Bi
- University of Texas at Austin, Austin, TX, USA
| | | | | | | | | | | | | | | | - Bryan Lewis
- University of Virginia, Charlottesville, VA, USA
| | - Brian Klahn
- University of Virginia, Charlottesville, VA, USA
| | | | | | | | | | | | | | - Stefan Hoops
- University of Virginia, Charlottesville, VA, USA
| | | | - Dustin Machi
- University of Virginia, Charlottesville, VA, USA
| | - Betsy L Cadwell
- Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Jessica M Healy
- Centers for Disease Control and Prevention, Atlanta, GA, USA
| | | | | | | | | | - Michael C Runge
- U.S. Geological Survey Eastern Ecological Science Center, Laurel, MD, USA
| | - Katriona Shea
- The Pennsylvania State University, University Park, PA, USA
| | - Cécile Viboud
- National Institutes of Health Fogarty International Center, Bethesda, MD, USA.
| | - Justin Lessler
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Johns Hopkins University, Baltimore, MD, USA.
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Jung SM, Loo SL, Howerton E, Contamin L, Smith CP, Carcelén EC, Yan K, Bents SJ, Levander J, Espino J, Lemaitre JC, Sato K, McKee CD, Hill AL, Chinazzi M, Davis JT, Mu K, Vespignani A, Rosenstrom ET, Rodriguez-Cartes SA, Ivy JS, Mayorga ME, Swann JL, España G, Cavany S, Moore SM, Perkins A, Chen S, Paul R, Janies D, Thill JC, Srivastava A, Al Aawar M, Bi K, Bandekar SR, Bouchnita A, Fox SJ, Meyers LA, Porebski P, Venkatramanan S, Adiga A, Hurt B, Klahn B, Outten J, Chen J, Mortveit H, Wilson A, Hoops S, Bhattacharya P, Machi D, Vullikanti A, Lewis B, Marathe M, Hochheiser H, Runge MC, Shea K, Truelove S, Viboud C, Lessler J. Potential impact of annual vaccination with reformulated COVID-19 vaccines: lessons from the U.S. COVID-19 Scenario Modeling Hub. medRxiv 2023:2023.10.26.23297581. [PMID: 37961207 PMCID: PMC10635209 DOI: 10.1101/2023.10.26.23297581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Importance COVID-19 continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear. Objective To project COVID-19 hospitalizations and deaths from April 2023-April 2025 under two plausible assumptions about immune escape (20% per year and 50% per year) and three possible CDC recommendations for the use of annually reformulated vaccines (no vaccine recommendation, vaccination for those aged 65+, vaccination for all eligible groups). Design The COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023-April 15, 2025 under six scenarios representing the intersection of considered levels of immune escape and vaccination. State and national projections from eight modeling teams were ensembled to produce projections for each scenario. Setting The entire United States. Participants None. Exposure Annually reformulated vaccines assumed to be 65% effective against strains circulating on June 15 of each year and to become available on September 1. Age and state specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster. Main outcomes and measures Ensemble estimates of weekly and cumulative COVID-19 hospitalizations and deaths. Expected relative and absolute reductions in hospitalizations and deaths due to vaccination over the projection period. Results From April 15, 2023-April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November-January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% PI: 1,438,000-4,270,000) hospitalizations and 209,000 (90% PI: 139,000-461,000) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% CI: 104,000-355,000) fewer hospitalizations and 33,000 (95% CI: 12,000-54,000) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000-598,000) fewer hospitalizations and 49,000 (95% CI: 29,000-69,000) fewer deaths. Conclusion and Relevance COVID-19 is projected to be a significant public health threat over the coming two years. Broad vaccination has the potential to substantially reduce the burden of this disease.
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Affiliation(s)
- Sung-mok Jung
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Sara L. Loo
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Emily Howerton
- The Pennsylvania State University, State College, Pennsylvania
| | | | - Claire P. Smith
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Erica C. Carcelén
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Katie Yan
- The Pennsylvania State University, State College, Pennsylvania
| | - Samantha J. Bents
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland
| | | | - Jessi Espino
- University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Joseph C. Lemaitre
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Koji Sato
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Clif D. McKee
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Alison L. Hill
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | | | | | - Kunpeng Mu
- University of Massachusetts Amherst, Amherst, Massachusetts
| | | | | | | | - Julie S. Ivy
- North Carolina State University, Raleigh, North Carolina
| | | | - Julie L. Swann
- North Carolina State University, Raleigh, North Carolina
| | | | - Sean Cavany
- University of Notre Dame, Notre Dame, Indiana
| | | | | | - Shi Chen
- University of North Carolina at Charlotte, Charlotte, North Carolina
| | - Rajib Paul
- University of North Carolina at Charlotte, Charlotte, North Carolina
| | - Daniel Janies
- University of North Carolina at Charlotte, Charlotte, North Carolina
| | - Jean-Claude Thill
- University of North Carolina at Charlotte, Charlotte, North Carolina
| | | | - Majd Al Aawar
- University of Southern California, Los Angeles, California
| | - Kaiming Bi
- University of Texas at Austin, Austin, Texas
| | | | | | | | | | | | | | | | | | - Brian Klahn
- University of Virginia, Charlottesville, Virginia
| | | | | | | | | | - Stefan Hoops
- University of Virginia, Charlottesville, Virginia
| | | | - Dustin Machi
- University of Virginia, Charlottesville, Virginia
| | | | - Bryan Lewis
- University of Virginia, Charlottesville, Virginia
| | | | | | | | - Katriona Shea
- The Pennsylvania State University, State College, Pennsylvania
| | - Shaun Truelove
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland
| | - Justin Lessler
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
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4
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Howerton E, Contamin L, Mullany LC, Qin M, Reich NG, Bents S, Borchering RK, Jung SM, Loo SL, Smith CP, Levander J, Kerr J, Espino J, van Panhuis WG, Hochheiser H, Galanti M, Yamana T, Pei S, Shaman J, Rainwater-Lovett K, Kinsey M, Tallaksen K, Wilson S, Shin L, Lemaitre JC, Kaminsky J, Hulse JD, Lee EC, McKee C, Hill A, Karlen D, Chinazzi M, Davis JT, Mu K, Xiong X, Piontti APY, Vespignani A, Rosenstrom ET, Ivy JS, Mayorga ME, Swann JL, España G, Cavany S, Moore S, Perkins A, Hladish T, Pillai A, Toh KB, Longini I, Chen S, Paul R, Janies D, Thill JC, Bouchnita A, Bi K, Lachmann M, Fox S, Meyers LA, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Hurt B, Chen J, Mortveit H, Wilson A, Marathe M, Hoops S, Bhattacharya P, Machi D, Cadwell BL, Healy JM, Slayton RB, Johansson MA, Biggerstaff M, Truelove S, Runge MC, Shea K, Viboud C, Lessler J. Informing pandemic response in the face of uncertainty. An evaluation of the U.S. COVID-19 Scenario Modeling Hub. medRxiv 2023:2023.06.28.23291998. [PMID: 37461674 PMCID: PMC10350156 DOI: 10.1101/2023.06.28.23291998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/24/2023]
Abstract
Our ability to forecast epidemics more than a few weeks into the future is constrained by the complexity of disease systems, our limited ability to measure the current state of an epidemic, and uncertainties in how human action will affect transmission. Realistic longer-term projections (spanning more than a few weeks) may, however, be possible under defined scenarios that specify the future state of critical epidemic drivers, with the additional benefit that such scenarios can be used to anticipate the comparative effect of control measures. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make 6-month ahead projections of the number of SARS-CoV-2 cases, hospitalizations and deaths. The SMH released nearly 1.8 million national and state-level projections between February 2021 and November 2022. SMH performance varied widely as a function of both scenario validity and model calibration. Scenario assumptions were periodically invalidated by the arrival of unanticipated SARS-CoV-2 variants, but SMH still provided projections on average 22 weeks before changes in assumptions (such as virus transmissibility) invalidated scenarios and their corresponding projections. During these periods, before emergence of a novel variant, a linear opinion pool ensemble of contributed models was consistently more reliable than any single model, and projection interval coverage was near target levels for the most plausible scenarios (e.g., 79% coverage for 95% projection interval). SMH projections were used operationally to guide planning and policy at different stages of the pandemic, illustrating the value of the hub approach for long-term scenario projections.
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Affiliation(s)
| | | | | | | | | | - Samantha Bents
- National Institutes of Health Fogarty International Center (NIH)
| | | | | | - Sara L Loo
- Johns Hopkins University Infectious Disease Dynamics (JHU-IDD)
| | - Claire P Smith
- Johns Hopkins University Infectious Disease Dynamics (JHU-IDD)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Shi Chen
- University of North Carolina at Charlotte (UNCC)
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5
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Bhattacharya P, Chen J, Hoops S, Machi D, Lewis B, Venkatramanan S, Wilson ML, Klahn B, Adiga A, Hurt B, Outten J, Adiga A, Warren A, Baek YY, Porebski P, Marathe A, Xie D, Swarup S, Vullikanti A, Mortveit H, Eubank S, Barrett CL, Marathe M. Data-driven scalable pipeline using national agent-based models for real-time pandemic response and decision support. Int J High Perform Comput Appl 2023; 37:4-27. [PMID: 38603425 PMCID: PMC9596688 DOI: 10.1177/10943420221127034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Abstract
This paper describes an integrated, data-driven operational pipeline based on national agent-based models to support federal and state-level pandemic planning and response. The pipeline consists of (i) an automatic semantic-aware scheduling method that coordinates jobs across two separate high performance computing systems; (ii) a data pipeline to collect, integrate and organize national and county-level disaggregated data for initialization and post-simulation analysis; (iii) a digital twin of national social contact networks made up of 288 Million individuals and 12.6 Billion time-varying interactions covering the US states and DC; (iv) an extension of a parallel agent-based simulation model to study epidemic dynamics and associated interventions. This pipeline can run 400 replicates of national runs in less than 33 h, and reduces the need for human intervention, resulting in faster turnaround times and higher reliability and accuracy of the results. Scientifically, the work has led to significant advances in real-time epidemic sciences.
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Affiliation(s)
- Parantapa Bhattacharya
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Jiangzhuo Chen
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Stefan Hoops
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Dustin Machi
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Bryan Lewis
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | | | - Mandy L Wilson
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Brian Klahn
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Aniruddha Adiga
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Benjamin Hurt
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Joseph Outten
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Abhijin Adiga
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Andrew Warren
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Young Yun Baek
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Przemyslaw Porebski
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Achla Marathe
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
- Dept. of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Dawen Xie
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Samarth Swarup
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Anil Vullikanti
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
- Dept. of Computer Science, University of Virginia, Charlottesville, VA, USA
| | - Henning Mortveit
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
- Dept. of Eng. Systems and Environment, University of Virginia, Charlottesville, VA, USA
| | - Stephen Eubank
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
- Dept. of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Christopher L Barrett
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
- Dept. of Computer Science, University of Virginia, Charlottesville, VA, USA
| | - Madhav Marathe
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
- Dept. of Computer Science, University of Virginia, Charlottesville, VA, USA
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6
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Borchering RK, Mullany LC, Howerton E, Chinazzi M, Smith CP, Qin M, Reich NG, Contamin L, Levander J, Kerr J, Espino J, Hochheiser H, Lovett K, Kinsey M, Tallaksen K, Wilson S, Shin L, Lemaitre JC, Hulse JD, Kaminsky J, Lee EC, Hill AL, Davis JT, Mu K, Xiong X, Pastore y Piontti A, Vespignani A, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Hurt B, Chen J, Mortveit H, Wilson A, Marathe M, Hoops S, Bhattacharya P, Machi D, Chen S, Paul R, Janies D, Thill JC, Galanti M, Yamana T, Pei S, Shaman J, España G, Cavany S, Moore S, Perkins A, Healy JM, Slayton RB, Johansson MA, Biggerstaff M, Shea K, Truelove SA, Runge MC, Viboud C, Lessler J. Impact of SARS-CoV-2 vaccination of children ages 5-11 years on COVID-19 disease burden and resilience to new variants in the United States, November 2021-March 2022: A multi-model study. Lancet Reg Health Am 2023; 17:100398. [PMID: 36437905 PMCID: PMC9679449 DOI: 10.1016/j.lana.2022.100398] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 09/21/2022] [Accepted: 10/27/2022] [Indexed: 11/23/2022]
Abstract
Background The COVID-19 Scenario Modeling Hub convened nine modeling teams to project the impact of expanding SARS-CoV-2 vaccination to children aged 5-11 years on COVID-19 burden and resilience against variant strains. Methods Teams contributed state- and national-level weekly projections of cases, hospitalizations, and deaths in the United States from September 12, 2021 to March 12, 2022. Four scenarios covered all combinations of 1) vaccination (or not) of children aged 5-11 years (starting November 1, 2021), and 2) emergence (or not) of a variant more transmissible than the Delta variant (emerging November 15, 2021). Individual team projections were linearly pooled. The effect of childhood vaccination on overall and age-specific outcomes was estimated using meta-analyses. Findings Assuming that a new variant would not emerge, all-age COVID-19 outcomes were projected to decrease nationally through mid-March 2022. In this setting, vaccination of children 5-11 years old was associated with reductions in projections for all-age cumulative cases (7.2%, mean incidence ratio [IR] 0.928, 95% confidence interval [CI] 0.880-0.977), hospitalizations (8.7%, mean IR 0.913, 95% CI 0.834-0.992), and deaths (9.2%, mean IR 0.908, 95% CI 0.797-1.020) compared with scenarios without childhood vaccination. Vaccine benefits increased for scenarios including a hypothesized more transmissible variant, assuming similar vaccine effectiveness. Projected relative reductions in cumulative outcomes were larger for children than for the entire population. State-level variation was observed. Interpretation Given the scenario assumptions (defined before the emergence of Omicron), expanding vaccination to children 5-11 years old would provide measurable direct benefits, as well as indirect benefits to the all-age U.S. population, including resilience to more transmissible variants. Funding Various (see acknowledgments).
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Affiliation(s)
| | - Luke C. Mullany
- Johns Hopkins University Applied Physics Laboratories Laurel, MD, USA
| | - Emily Howerton
- The Pennsylvania State University, University Park, PA, USA
| | | | | | | | | | | | | | | | - J. Espino
- University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Kaitlin Lovett
- Johns Hopkins University Applied Physics Laboratories Laurel, MD, USA
| | - Matt Kinsey
- Johns Hopkins University Applied Physics Laboratories Laurel, MD, USA
| | - Kate Tallaksen
- Johns Hopkins University Applied Physics Laboratories Laurel, MD, USA
| | - Shelby Wilson
- Johns Hopkins University Applied Physics Laboratories Laurel, MD, USA
| | - Lauren Shin
- Johns Hopkins University Applied Physics Laboratories Laurel, MD, USA
| | | | | | | | | | | | | | - Kunpeng Mu
- Northeastern University, Boston, MA, USA
| | | | | | | | | | | | | | | | - Bryan Lewis
- University of Virginia, Charlottesville, VA, USA
| | - Brian Klahn
- University of Virginia, Charlottesville, VA, USA
| | | | | | | | | | | | | | - Stefan Hoops
- University of Virginia, Charlottesville, VA, USA
| | | | - Dustin Machi
- University of Virginia, Charlottesville, VA, USA
| | - Shi Chen
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Rajib Paul
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Daniel Janies
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | | | | | | | - Sen Pei
- Columbia University, New York, NY, USA
| | | | | | - Sean Cavany
- University of Notre Dame, Notre Dame, IN, USA
| | - Sean Moore
- University of Notre Dame, Notre Dame, IN, USA
| | | | - Jessica M. Healy
- CDC COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Rachel B. Slayton
- CDC COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Michael A. Johansson
- CDC COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Matthew Biggerstaff
- CDC COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Katriona Shea
- The Pennsylvania State University, University Park, PA, USA
| | | | | | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Justin Lessler
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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7
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Olson RD, Assaf R, Brettin T, Conrad N, Cucinell C, Davis J, Dempsey D, Dickerman A, Dietrich E, Kenyon R, Kuscuoglu M, Lefkowitz E, Lu J, Machi D, Macken C, Mao C, Niewiadomska A, Nguyen M, Olsen G, Overbeek J, Parrello B, Parrello V, Porter J, Pusch G, Shukla M, Singh I, Stewart L, Tan G, Thomas C, VanOeffelen M, Vonstein V, Wallace Z, Warren A, Wattam A, Xia F, Yoo H, Zhang Y, Zmasek C, Scheuermann R, Stevens R. Introducing the Bacterial and Viral Bioinformatics Resource Center (BV-BRC): a resource combining PATRIC, IRD and ViPR. Nucleic Acids Res 2022; 51:D678-D689. [PMID: 36350631 PMCID: PMC9825582 DOI: 10.1093/nar/gkac1003] [Citation(s) in RCA: 172] [Impact Index Per Article: 86.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/12/2022] [Accepted: 10/19/2022] [Indexed: 11/10/2022] Open
Abstract
The National Institute of Allergy and Infectious Diseases (NIAID) established the Bioinformatics Resource Center (BRC) program to assist researchers with analyzing the growing body of genome sequence and other omics-related data. In this report, we describe the merger of the PAThosystems Resource Integration Center (PATRIC), the Influenza Research Database (IRD) and the Virus Pathogen Database and Analysis Resource (ViPR) BRCs to form the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) https://www.bv-brc.org/. The combined BV-BRC leverages the functionality of the bacterial and viral resources to provide a unified data model, enhanced web-based visualization and analysis tools, bioinformatics services, and a powerful suite of command line tools that benefit the bacterial and viral research communities.
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Affiliation(s)
- Robert D Olson
- Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL 60637, USA,Division of Data Science and Learning, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Rida Assaf
- Department of Computer Science, American University of Beirut, Beirut, Lebanon
| | - Thomas Brettin
- Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL 60637, USA,Computing Environment and Life Sciences, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Neal Conrad
- Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL 60637, USA,Division of Data Science and Learning, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Clark Cucinell
- University of Virginia Biocomplexity Institute, Charlottesville, VA 22904, USA
| | - James J Davis
- To whom correspondence should be addressed. Tel: +1 630 252 1190;
| | - Donald M Dempsey
- Department of Microbiology, University of Alabama at Birmingham School of Medicine, Birmingham, AL 35294, USA
| | - Allan Dickerman
- University of Virginia Biocomplexity Institute, Charlottesville, VA 22904, USA
| | - Emily M Dietrich
- Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL 60637, USA,Division of Data Science and Learning, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Ronald W Kenyon
- University of Virginia Biocomplexity Institute, Charlottesville, VA 22904, USA
| | - Mehmet Kuscuoglu
- Department of Informatics, J. Craig Venter Institute, La Jolla, CA 92037, USA
| | - Elliot J Lefkowitz
- Department of Microbiology, University of Alabama at Birmingham School of Medicine, Birmingham, AL 35294, USA
| | - Jian Lu
- J. Craig Venter Institute, Rockville, MD 20850, USA
| | - Dustin Machi
- University of Virginia Biocomplexity Institute, Charlottesville, VA 22904, USA
| | - Catherine Macken
- Department of Statistics, University of Auckland, Auckland, New Zealand
| | - Chunhong Mao
- University of Virginia Biocomplexity Institute, Charlottesville, VA 22904, USA
| | - Anna Niewiadomska
- Department of Informatics, J. Craig Venter Institute, La Jolla, CA 92037, USA
| | - Marcus Nguyen
- Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL 60637, USA,Division of Data Science and Learning, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Gary J Olsen
- Department of Microbiology, University of Illinois, Urbana, IL 61801, USA
| | - Jamie C Overbeek
- Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL 60637, USA,Division of Data Science and Learning, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Bruce Parrello
- Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL 60637, USA,Fellowship for Interpretation of Genomes, Burr Ridge, IL 60527, USA
| | | | - Jacob S Porter
- Computing Environment and Life Sciences, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Gordon D Pusch
- Fellowship for Interpretation of Genomes, Burr Ridge, IL 60527, USA
| | - Maulik Shukla
- Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL 60637, USA,Division of Data Science and Learning, Argonne National Laboratory, Argonne, IL 60439, USA
| | | | - Lucy Stewart
- Department of Informatics, J. Craig Venter Institute, La Jolla, CA 92037, USA
| | - Gene Tan
- Department of Informatics, J. Craig Venter Institute, La Jolla, CA 92037, USA
| | - Chris Thomas
- Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL 60637, USA,Division of Data Science and Learning, Argonne National Laboratory, Argonne, IL 60439, USA
| | | | | | - Zachary S Wallace
- Department of Microbiology, University of Alabama at Birmingham School of Medicine, Birmingham, AL 35294, USA,Department of Computer Science and Engineering, University of California, San Diego, CA 92039, USA
| | - Andrew S Warren
- University of Virginia Biocomplexity Institute, Charlottesville, VA 22904, USA
| | - Alice R Wattam
- University of Virginia Biocomplexity Institute, Charlottesville, VA 22904, USA
| | - Fangfang Xia
- Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL 60637, USA,Division of Data Science and Learning, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Hyunseung Yoo
- Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL 60637, USA,Division of Data Science and Learning, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Yun Zhang
- Department of Informatics, J. Craig Venter Institute, La Jolla, CA 92037, USA
| | - Christian M Zmasek
- Department of Informatics, J. Craig Venter Institute, La Jolla, CA 92037, USA
| | - Richard H Scheuermann
- Department of Informatics, J. Craig Venter Institute, La Jolla, CA 92037, USA,Department of Pathology, University of California, San Diego, CA 92093, USA,Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, CA 92037, USA,Global Virus Network, Baltimore, MD 21201, USA
| | - Rick L Stevens
- Computing Environment and Life Sciences, Argonne National Laboratory, Argonne, IL 60439, USA,Department of Computer Science, University of Chicago, Chicago, IL 60637, USA
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8
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Thakur M, Zhou R, Mohan M, Marathe A, Chen J, Hoops S, Machi D, Lewis B, Vullikanti A. COVID's collateral damage: likelihood of measles resurgence in the United States. BMC Infect Dis 2022; 22:743. [PMID: 36127637 PMCID: PMC9487857 DOI: 10.1186/s12879-022-07703-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Accepted: 08/25/2022] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND Lockdowns imposed throughout the US to control the COVID-19 pandemic led to a decline in all routine immunizations rates, including the MMR (measles, mumps, rubella) vaccine. It is feared that post-lockdown, these reduced MMR rates will lead to a resurgence of measles. METHODS To measure the potential impact of reduced MMR vaccination rates on measles outbreak, this research examines several counterfactual scenarios in pre-COVID-19 and post-COVID-19 era. An agent-based modeling framework is used to simulate the spread of measles on a synthetic yet realistic social network of Virginia. The change in vulnerability of various communities to measles due to reduced MMR rate is analyzed. RESULTS Results show that a decrease in vaccination rate [Formula: see text] has a highly non-linear effect on the number of measles cases and this effect grows exponentially beyond a threshold [Formula: see text]. At low vaccination rates, faster isolation of cases and higher compliance to home-isolation are not enough to control the outbreak. The overall impact on urban and rural counties is proportional to their population size but the younger children, African Americans and American Indians are disproportionately infected and hence are more vulnerable to the reduction in the vaccination rate. CONCLUSIONS At low vaccination rates, broader interventions are needed to control the outbreak. Identifying the cause of the decline in vaccination rates (e.g., low income) can help design targeted interventions which can dampen the disproportional impact on more vulnerable populations and reduce disparities in health. Per capita burden of the potential measles resurgence is equivalent in the rural and the urban communities and hence proportionally equitable public health resources should be allocated to rural regions.
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Affiliation(s)
- Mugdha Thakur
- Biocomplexity Institute, Town Center Four, 994 Research Park Boulevard, Charlottesville, VA, 22904, USA.
| | - Richard Zhou
- Biocomplexity Institute, Town Center Four, 994 Research Park Boulevard, Charlottesville, VA 22904 USA
| | - Mukundan Mohan
- Biocomplexity Institute, Town Center Four, 994 Research Park Boulevard, Charlottesville, VA 22904 USA
| | - Achla Marathe
- Biocomplexity Institute, Town Center Four, 994 Research Park Boulevard, Charlottesville, VA 22904 USA
| | - Jiangzhuo Chen
- Biocomplexity Institute, Town Center Four, 994 Research Park Boulevard, Charlottesville, VA 22904 USA
| | - Stefan Hoops
- Biocomplexity Institute, Town Center Four, 994 Research Park Boulevard, Charlottesville, VA 22904 USA
| | - Dustin Machi
- Biocomplexity Institute, Town Center Four, 994 Research Park Boulevard, Charlottesville, VA 22904 USA
| | - Bryan Lewis
- Biocomplexity Institute, Town Center Four, 994 Research Park Boulevard, Charlottesville, VA 22904 USA
| | - Anil Vullikanti
- Biocomplexity Institute, Town Center Four, 994 Research Park Boulevard, Charlottesville, VA 22904 USA
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9
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Truelove S, Smith CP, Qin M, Mullany LC, Borchering RK, Lessler J, Shea K, Howerton E, Contamin L, Levander J, Kerr J, Hochheiser H, Kinsey M, Tallaksen K, Wilson S, Shin L, Rainwater-Lovett K, Lemairtre JC, Dent J, Kaminsky J, Lee EC, Perez-Saez J, Hill A, Karlen D, Chinazzi M, Davis JT, Mu K, Xiong X, Pastore y Piontti A, Vespignani A, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Orr M, Harrison G, Hurt B, Chen J, Vullikanti A, Marathe M, Hoops S, Bhattacharya P, Machi D, Chen S, Paul R, Janies D, Thill JC, Galanti M, Yamana TK, Pei S, Shaman JL, Healy JM, Slayton RB, Biggerstaff M, Johansson MA, Runge MC, Viboud C. Projected resurgence of COVID-19 in the United States in July-December 2021 resulting from the increased transmissibility of the Delta variant and faltering vaccination. eLife 2022; 11:e73584. [PMID: 35726851 PMCID: PMC9232215 DOI: 10.7554/elife.73584] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 06/03/2022] [Indexed: 01/01/2023] Open
Abstract
In Spring 2021, the highly transmissible SARS-CoV-2 Delta variant began to cause increases in cases, hospitalizations, and deaths in parts of the United States. At the time, with slowed vaccination uptake, this novel variant was expected to increase the risk of pandemic resurgence in the US in summer and fall 2021. As part of the COVID-19 Scenario Modeling Hub, an ensemble of nine mechanistic models produced 6-month scenario projections for July-December 2021 for the United States. These projections estimated substantial resurgences of COVID-19 across the US resulting from the more transmissible Delta variant, projected to occur across most of the US, coinciding with school and business reopening. The scenarios revealed that reaching higher vaccine coverage in July-December 2021 reduced the size and duration of the projected resurgence substantially, with the expected impacts was largely concentrated in a subset of states with lower vaccination coverage. Despite accurate projection of COVID-19 surges occurring and timing, the magnitude was substantially underestimated 2021 by the models compared with the of the reported cases, hospitalizations, and deaths occurring during July-December, highlighting the continued challenges to predict the evolving COVID-19 pandemic. Vaccination uptake remains critical to limiting transmission and disease, particularly in states with lower vaccination coverage. Higher vaccination goals at the onset of the surge of the new variant were estimated to avert over 1.5 million cases and 21,000 deaths, although may have had even greater impacts, considering the underestimated resurgence magnitude from the model.
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Affiliation(s)
- Shaun Truelove
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUnited States
| | - Claire P Smith
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUnited States
| | - Michelle Qin
- Harvard UniversityCambridge, MassachusettsUnited States
| | - Luke C Mullany
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUnited States
- Johns Hopkins University Applied Physics LaboratoryLaurelUnited States
| | | | - Justin Lessler
- University of North Carolina at Chapel HillChapel HillUnited States
| | - Katriona Shea
- Pennsylvania State UniversityUniversity ParkUnited States
| | - Emily Howerton
- Pennsylvania State UniversityUniversity ParkUnited States
| | | | | | | | | | - Matt Kinsey
- Johns Hopkins University Applied Physics LaboratoryLaurelUnited States
| | - Kate Tallaksen
- Johns Hopkins University Applied Physics LaboratoryLaurelUnited States
| | - Shelby Wilson
- Johns Hopkins University Applied Physics LaboratoryLaurelUnited States
| | - Lauren Shin
- Johns Hopkins University Applied Physics LaboratoryLaurelUnited States
| | | | | | - Juan Dent
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUnited States
| | - Joshua Kaminsky
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUnited States
| | - Elizabeth C Lee
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUnited States
| | - Javier Perez-Saez
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUnited States
| | - Alison Hill
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUnited States
| | | | | | | | - Kunpeng Mu
- Northeastern UniversityBostonUnited States
| | | | | | | | | | | | | | | | - Bryan Lewis
- University of VirginiaCharlottesvilleUnited States
| | - Brian Klahn
- University of VirginiaCharlottesvilleUnited States
| | | | - Mark Orr
- University of VirginiaCharlottesvilleUnited States
| | | | | | | | | | | | - Stefan Hoops
- University of VirginiaCharlottesvilleUnited States
| | | | - Dustin Machi
- University of VirginiaCharlottesvilleUnited States
| | - Shi Chen
- University of North Carolina at CharlotteCharlotteUnited States
| | - Rajib Paul
- University of North Carolina at CharlotteCharlotteUnited States
| | - Daniel Janies
- University of North Carolina at CharlotteCharlotteUnited States
| | | | | | | | - Sen Pei
- Columbia UniversityNew YorkUnited States
| | | | | | | | | | | | | | - Cecile Viboud
- Fogarty International Center, National Institutes of HealthBethesdaUnited States
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10
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Borchering RK, Mullany LC, Howerton E, Chinazzi M, Smith CP, Qin M, Reich NG, Contamin L, Levander J, Kerr J, Espino J, Hochheiser H, Lovett K, Kinsey M, Tallaksen K, Wilson S, Shin L, Lemaitre JC, Hulse JD, Kaminsky J, Lee EC, Davis JT, Mu K, Xiong X, Piontti APY, Vespignani A, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Hurt B, Chen J, Mortveit H, Wilson A, Marathe M, Hoops S, Bhattacharya P, Machi D, Chen S, Paul R, Janies D, Thill JC, Galanti M, Yamana T, Pei S, Shaman J, Espana G, Cavany S, Moore S, Perkins A, Healy JM, Slayton RB, Johansson MA, Biggerstaff M, Shea K, Truelove SA, Runge MC, Viboud C, Lessler J. Impact of SARS-CoV-2 vaccination of children ages 5-11 years on COVID-19 disease burden and resilience to new variants in the United States, November 2021-March 2022: a multi-model study. medRxiv 2022:2022.03.08.22271905. [PMID: 35313593 PMCID: PMC8936106 DOI: 10.1101/2022.03.08.22271905] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Background SARS-CoV-2 vaccination of persons aged 12 years and older has reduced disease burden in the United States. The COVID-19 Scenario Modeling Hub convened multiple modeling teams in September 2021 to project the impact of expanding vaccine administration to children 5-11 years old on anticipated COVID-19 burden and resilience against variant strains. Methods Nine modeling teams contributed state- and national-level projections for weekly counts of cases, hospitalizations, and deaths in the United States for the period September 12, 2021 to March 12, 2022. Four scenarios covered all combinations of: 1) presence vs. absence of vaccination of children ages 5-11 years starting on November 1, 2021; and 2) continued dominance of the Delta variant vs. emergence of a hypothetical more transmissible variant on November 15, 2021. Individual team projections were combined using linear pooling. The effect of childhood vaccination on overall and age-specific outcomes was estimated by meta-analysis approaches. Findings Absent a new variant, COVID-19 cases, hospitalizations, and deaths among all ages were projected to decrease nationally through mid-March 2022. Under a set of specific assumptions, models projected that vaccination of children 5-11 years old was associated with reductions in all-age cumulative cases (7.2%, mean incidence ratio [IR] 0.928, 95% confidence interval [CI] 0.880-0.977), hospitalizations (8.7%, mean IR 0.913, 95% CI 0.834-0.992), and deaths (9.2%, mean IR 0.908, 95% CI 0.797-1.020) compared with scenarios where children were not vaccinated. This projected effect of vaccinating children 5-11 years old increased in the presence of a more transmissible variant, assuming no change in vaccine effectiveness by variant. Larger relative reductions in cumulative cases, hospitalizations, and deaths were observed for children than for the entire U.S. population. Substantial state-level variation was projected in epidemic trajectories, vaccine benefits, and variant impacts. Conclusions Results from this multi-model aggregation study suggest that, under a specific set of scenario assumptions, expanding vaccination to children 5-11 years old would provide measurable direct benefits to this age group and indirect benefits to the all-age U.S. population, including resilience to more transmissible variants.
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Affiliation(s)
| | | | | | | | - Claire P Smith
- Johns Hopkins University Infectious Disease Dynamics (JHU-IDD)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Shi Chen
- University of North Carolina at Charlotte (UNCC)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Cécile Viboud
- National Institutes of Health Fogarty International Center
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11
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VanOeffelen M, Nguyen M, Aytan-Aktug D, Brettin T, Dietrich EM, Kenyon RW, Machi D, Mao C, Olson R, Pusch GD, Shukla M, Stevens R, Vonstein V, Warren AS, Wattam AR, Yoo H, Davis JJ. A genomic data resource for predicting antimicrobial resistance from laboratory-derived antimicrobial susceptibility phenotypes. Brief Bioinform 2021; 22:bbab313. [PMID: 34379107 PMCID: PMC8575023 DOI: 10.1093/bib/bbab313] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 06/18/2021] [Accepted: 07/20/2021] [Indexed: 11/14/2022] Open
Abstract
Antimicrobial resistance (AMR) is a major global health threat that affects millions of people each year. Funding agencies worldwide and the global research community have expended considerable capital and effort tracking the evolution and spread of AMR by isolating and sequencing bacterial strains and performing antimicrobial susceptibility testing (AST). For the last several years, we have been capturing these efforts by curating data from the literature and data resources and building a set of assembled bacterial genome sequences that are paired with laboratory-derived AST data. This collection currently contains AST data for over 67 000 genomes encompassing approximately 40 genera and over 100 species. In this paper, we describe the characteristics of this collection, highlighting areas where sampling is comparatively deep or shallow, and showing areas where attention is needed from the research community to improve sampling and tracking efforts. In addition to using the data to track the evolution and spread of AMR, it also serves as a useful starting point for building machine learning models for predicting AMR phenotypes. We demonstrate this by describing two machine learning models that are built from the entire dataset to show where the predictive power is comparatively high or low. This AMR metadata collection is freely available and maintained on the Bacterial and Viral Bioinformatics Center (BV-BRC) FTP site ftp://ftp.bvbrc.org/RELEASE_NOTES/PATRIC_genomes_AMR.txt.
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Affiliation(s)
| | - Marcus Nguyen
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, USA
- Data Science and Learning Division, Argonne National Laboratory, Argonne, IL, USA
| | - Derya Aytan-Aktug
- National Food Institute, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Thomas Brettin
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, USA
- Computing Environment and Life Sciences, Argonne National Laboratory, Argonne, IL, USA
| | - Emily M Dietrich
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, USA
- Computing Environment and Life Sciences, Argonne National Laboratory, Argonne, IL, USA
| | - Ronald W Kenyon
- Biocomplexity Institute and Initiative, University of Virginia, Virginia, USA
| | - Dustin Machi
- Biocomplexity Institute and Initiative, University of Virginia, Virginia, USA
| | - Chunhong Mao
- Biocomplexity Institute and Initiative, University of Virginia, Virginia, USA
| | - Robert Olson
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, USA
- Data Science and Learning Division, Argonne National Laboratory, Argonne, IL, USA
| | - Gordon D Pusch
- Fellowship for Interpretation of Genomes, Burr Ridge, IL, USA
| | - Maulik Shukla
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, USA
- Data Science and Learning Division, Argonne National Laboratory, Argonne, IL, USA
| | - Rick Stevens
- Computing Environment and Life Sciences, Argonne National Laboratory, Argonne, IL, USA
- Department of Computer Science, University of Chicago, Chicago, IL, USA
| | | | - Andrew S Warren
- Biocomplexity Institute and Initiative, University of Virginia, Virginia, USA
| | - Alice R Wattam
- Data Science and Learning Division, Argonne National Laboratory, Argonne, IL, USA
- Biocomplexity Institute and Initiative, University of Virginia, Virginia, USA
| | - Hyunseung Yoo
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, USA
- Data Science and Learning Division, Argonne National Laboratory, Argonne, IL, USA
| | - James J Davis
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, USA
- Data Science and Learning Division, Argonne National Laboratory, Argonne, IL, USA
- Northwestern Argonne Institute for Science and Engineering, Evanston, IL, USA
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12
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Truelove S, Smith CP, Qin M, Mullany LC, Borchering RK, Lessler J, Shea K, Howerton E, Contamin L, Levander J, Salerno J, Hochheiser H, Kinsey M, Tallaksen K, Wilson S, Shin L, Rainwater-Lovett K, Lemaitre JC, Dent J, Kaminsky J, Lee EC, Perez-Saez J, Hill A, Karlen D, Chinazzi M, Davis JT, Mu K, Xiong X, Piontti APY, Vespignani A, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Schlitt J, Corbett P, Telionis PA, Wang L, Peddireddy AS, Hurt B, Chen J, Vullikanti A, Marathe M, Hoops S, Bhattacharya P, Machi D, Chen S, Paul R, Janies D, Thill JC, Galanti M, Yamana T, Pei S, Shaman J, Reich NG, Healy JM, Slayton RB, Biggerstaff M, Johansson MA, Runge MC, Viboud C. Projected resurgence of COVID-19 in the United States in July-December 2021 resulting from the increased transmissibility of the Delta variant and faltering vaccination. medRxiv 2021:2021.08.28.21262748. [PMID: 34494030 PMCID: PMC8423228 DOI: 10.1101/2021.08.28.21262748] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
WHAT IS ALREADY KNOWN ABOUT THIS TOPIC? The highly transmissible SARS-CoV-2 Delta variant has begun to cause increases in cases, hospitalizations, and deaths in parts of the United States. With slowed vaccination uptake, this novel variant is expected to increase the risk of pandemic resurgence in the US in July-December 2021. WHAT IS ADDED BY THIS REPORT? Data from nine mechanistic models project substantial resurgences of COVID-19 across the US resulting from the more transmissible Delta variant. These resurgences, which have now been observed in most states, were projected to occur across most of the US, coinciding with school and business reopening. Reaching higher vaccine coverage in July-December 2021 reduces the size and duration of the projected resurgence substantially. The expected impact of the outbreak is largely concentrated in a subset of states with lower vaccination coverage. WHAT ARE THE IMPLICATIONS FOR PUBLIC HEALTH PRACTICE? Renewed efforts to increase vaccination uptake are critical to limiting transmission and disease, particularly in states with lower current vaccination coverage. Reaching higher vaccination goals in the coming months can potentially avert 1.5 million cases and 21,000 deaths and improve the ability to safely resume social contacts, and educational and business activities. Continued or renewed non-pharmaceutical interventions, including masking, can also help limit transmission, particularly as schools and businesses reopen.
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Affiliation(s)
- Shaun Truelove
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Claire P Smith
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | | | - Luke C Mullany
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Johns Hopkins University Applied Physics Laboratories, Laurel, Maryland
| | | | | | - Katriona Shea
- The Pennsylvania State University, State College, Pennsylvania
| | - Emily Howerton
- The Pennsylvania State University, State College, Pennsylvania
| | | | | | | | | | - Matt Kinsey
- Johns Hopkins University Applied Physics Laboratories, Laurel, Maryland
| | - Kate Tallaksen
- Johns Hopkins University Applied Physics Laboratories, Laurel, Maryland
| | - Shelby Wilson
- Johns Hopkins University Applied Physics Laboratories, Laurel, Maryland
| | - Lauren Shin
- Johns Hopkins University Applied Physics Laboratories, Laurel, Maryland
| | | | | | - Juan Dent
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Joshua Kaminsky
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Elizabeth C Lee
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Javier Perez-Saez
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Alison Hill
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Dean Karlen
- University of Victoria, Victoria, British Columbia, Canada
| | | | | | - Kunpeng Mu
- Northeastern University, Boston, Massachusetts
| | | | | | | | | | | | | | | | - Bryan Lewis
- University of Virginia, Charlottesville, Virginia
| | - Brian Klahn
- University of Virginia, Charlottesville, Virginia
| | | | | | | | | | - Lijing Wang
- University of Virginia, Charlottesville, Virginia
| | | | | | | | | | | | - Stefan Hoops
- University of Virginia, Charlottesville, Virginia
| | | | - Dustin Machi
- University of Virginia, Charlottesville, Virginia
| | - Shi Chen
- University of North Carolina at Charlotte, Charlotte, North Carolina
| | - Rajib Paul
- University of North Carolina at Charlotte, Charlotte, North Carolina
| | - Daniel Janies
- University of North Carolina at Charlotte, Charlotte, North Carolina
| | - Jean-Claude Thill
- University of North Carolina at Charlotte, Charlotte, North Carolina
| | | | | | | | | | | | | | | | | | | | | | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland
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13
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Machi D, Bhattacharya P, Hoops S, Chen J, Mortveit H, Venkatramanan S, Lewis B, Wilson M, Fadikar A, Maiden T, Barrett CL, Marathe MV. Scalable Epidemiological Workflows to Support COVID-19 Planning and Response. medRxiv 2021:2021.02.23.21252325. [PMID: 33655263 PMCID: PMC7924288 DOI: 10.1101/2021.02.23.21252325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The COVID-19 global outbreak represents the most significant epidemic event since the 1918 influenza pandemic. Simulations have played a crucial role in supporting COVID-19 planning and response efforts. Developing scalable workflows to provide policymakers quick responses to important questions pertaining to logistics, resource allocation, epidemic forecasts and intervention analysis remains a challenging computational problem. In this work, we present scalable high performance computing-enabled workflows for COVID-19 pandemic planning and response. The scalability of our methodology allows us to run fine-grained simulations daily, and to generate county-level forecasts and other counter-factual analysis for each of the 50 states (and DC), 3140 counties across the USA. Our workflows use a hybrid cloud/cluster system utilizing a combination of local and remote cluster computing facilities, and using over 20,000 CPU cores running for 6-9 hours every day to meet this objective. Our state (Virginia), state hospital network, our university, the DOD and the CDC use our models to guide their COVID-19 planning and response efforts. We began executing these pipelines March 25, 2020, and have delivered and briefed weekly updates to these stakeholders for over 30 weeks without interruption.
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14
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Cedeno-Mieles V, Hu Z, Ren Y, Deng X, Adiga A, Barrett C, Contractor N, Ekanayake S, Epstein JM, Goode BJ, Korkmaz G, Kuhlman CJ, Machi D, Macy MW, Marathe MV, Ramakrishnan N, Ravi SS, Saraf P, Self N. Networked experiments and modeling for producing collective identity in a group of human subjects using an iterative abduction framework. Soc Netw Anal Min 2020. [DOI: 10.1007/s13278-019-0620-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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15
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Cedeno-Mieles V, Hu Z, Ren Y, Deng X, Contractor N, Ekanayake S, Epstein JM, Goode BJ, Korkmaz G, Kuhlman CJ, Machi D, Macy M, Marathe MV, Ramakrishnan N, Saraf P, Self N. Data analysis and modeling pipelines for controlled networked social science experiments. PLoS One 2020; 15:e0242453. [PMID: 33232347 PMCID: PMC7685486 DOI: 10.1371/journal.pone.0242453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Accepted: 11/03/2020] [Indexed: 11/19/2022] Open
Abstract
There is large interest in networked social science experiments for understanding human behavior at-scale. Significant effort is required to perform data analytics on experimental outputs and for computational modeling of custom experiments. Moreover, experiments and modeling are often performed in a cycle, enabling iterative experimental refinement and data modeling to uncover interesting insights and to generate/refute hypotheses about social behaviors. The current practice for social analysts is to develop tailor-made computer programs and analytical scripts for experiments and modeling. This often leads to inefficiencies and duplication of effort. In this work, we propose a pipeline framework to take a significant step towards overcoming these challenges. Our contribution is to describe the design and implementation of a software system to automate many of the steps involved in analyzing social science experimental data, building models to capture the behavior of human subjects, and providing data to test hypotheses. The proposed pipeline framework consists of formal models, formal algorithms, and theoretical models as the basis for the design and implementation. We propose a formal data model, such that if an experiment can be described in terms of this model, then our pipeline software can be used to analyze data efficiently. The merits of the proposed pipeline framework is elaborated by several case studies of networked social science experiments.
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Affiliation(s)
- Vanessa Cedeno-Mieles
- Department of Computer Science, Virginia Tech, Blacksburg, VA, United States of America
- Escuela Superior Politécnica del Litoral, ESPOL, Guayaquil, Ecuador
| | - Zhihao Hu
- Department of Statistics, Virginia Tech, Blacksburg, VA, United States of America
| | - Yihui Ren
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY, United States of America
| | - Xinwei Deng
- Department of Statistics, Virginia Tech, Blacksburg, VA, United States of America
| | - Noshir Contractor
- Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, United States of America
| | - Saliya Ekanayake
- Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
| | - Joshua M. Epstein
- Department of Epidemiology, New York University, New York, NY, United States of America
| | - Brian J. Goode
- Biocomplexity Institute, Virginia Tech, Blacksburg, VA, United States of America
| | - Gizem Korkmaz
- Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, VA, United States of America
| | - Chris J. Kuhlman
- Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, VA, United States of America
| | - Dustin Machi
- Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, VA, United States of America
| | - Michael Macy
- Department of Sociology, Cornell University, Ithaca, NY, United States of America
| | - Madhav V. Marathe
- Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, VA, United States of America
- Department of Computer Science, University of Virginia, Charlottesville, VA, United States of America
| | - Naren Ramakrishnan
- Department of Computer Science, Virginia Tech, Blacksburg, VA, United States of America
- Discovery Analytics Center, Virginia Tech, Blacksburg, VA, United States of America
| | - Parang Saraf
- Discovery Analytics Center, Virginia Tech, Blacksburg, VA, United States of America
| | - Nathan Self
- Discovery Analytics Center, Virginia Tech, Blacksburg, VA, United States of America
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16
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Peddireddy AS, Xie D, Patil P, Wilson ML, Machi D, Venkatramanan S, Klahn B, Porebski P, Bhattacharya P, Dumbre S, Raymond E, Marathe M. From 5Vs to 6Cs: Operationalizing Epidemic Data Management with COVID-19 Surveillance. medRxiv 2020:2020.10.27.20220830. [PMID: 33140060 PMCID: PMC7605571 DOI: 10.1101/2020.10.27.20220830] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
The COVID-19 pandemic brought to the forefront an unprecedented need for experts, as well as citizens, to visualize spatio-temporal disease surveillance data. Web application dashboards were quickly developed to fill this gap, including those built by JHU, WHO, and CDC, but all of these dashboards supported a particular niche view of the pandemic (ie, current status or specific regions). In this paper, we describe our work developing our own COVID-19 Surveillance Dashboard, available at https://nssac.bii.virginia.edu/covid-19/dashboard/, which offers a universal view of the pandemic while also allowing users to focus on the details that interest them. From the beginning, our goal was to provide a simple visual way to compare, organize, and track near-real-time surveillance data as the pandemic progresses. Our dashboard includes a number of advanced features for zooming, filtering, categorizing and visualizing multiple time series on a single canvas. In developing this dashboard, we have also identified 6 key metrics we call the 6Cs standard which we propose as a standard for the design and evaluation of real-time epidemic science dashboards. Our dashboard was one of the first released to the public, and remains one of the most visited and highly used. Our group uses it to support federal, state and local public health authorities, and it is used by people worldwide to track the pandemic evolution, build their own dashboards, and support their organizations as they plan their responses to the pandemic. We illustrate the utility of our dashboard by describing how it can be used to support data story-telling - an important emerging area in data science.
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Affiliation(s)
| | - Dawen Xie
- Biocomplexity Institute & Initiative, University of Virginia
| | | | - Mandy L Wilson
- Biocomplexity Institute & Initiative, University of Virginia
| | - Dustin Machi
- Biocomplexity Institute & Initiative, University of Virginia
| | | | - Brian Klahn
- Biocomplexity Institute & Initiative, University of Virginia
| | | | | | | | - Erin Raymond
- Biocomplexity Institute & Initiative, University of Virginia
| | - Madhav Marathe
- Biocomplexity Institute & Initiative, University of Virginia
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17
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Antonopoulos DA, Assaf R, Aziz RK, Brettin T, Bun C, Conrad N, Davis JJ, Dietrich EM, Disz T, Gerdes S, Kenyon RW, Machi D, Mao C, Murphy-Olson DE, Nordberg EK, Olsen GJ, Olson R, Overbeek R, Parrello B, Pusch GD, Santerre J, Shukla M, Stevens RL, VanOeffelen M, Vonstein V, Warren AS, Wattam AR, Xia F, Yoo H. PATRIC as a unique resource for studying antimicrobial resistance. Brief Bioinform 2020; 20:1094-1102. [PMID: 28968762 PMCID: PMC6781570 DOI: 10.1093/bib/bbx083] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2017] [Revised: 06/13/2017] [Indexed: 02/07/2023] Open
Abstract
The Pathosystems Resource Integration Center (PATRIC, www.patricbrc.org) is designed to provide researchers with the tools and services that they need to perform genomic and other ‘omic’ data analyses. In response to mounting concern over antimicrobial resistance (AMR), the PATRIC team has been developing new tools that help researchers understand AMR and its genetic determinants. To support comparative analyses, we have added AMR phenotype data to over 15 000 genomes in the PATRIC database, often assembling genomes from reads in public archives and collecting their associated AMR panel data from the literature to augment the collection. We have also been using this collection of AMR metadata to build machine learning-based classifiers that can predict the AMR phenotypes and the genomic regions associated with resistance for genomes being submitted to the annotation service. Likewise, we have undertaken a large AMR protein annotation effort by manually curating data from the literature and public repositories. This collection of 7370 AMR reference proteins, which contains many protein annotations (functional roles) that are unique to PATRIC and RAST, has been manually curated so that it projects stably across genomes. The collection currently projects to 1 610 744 proteins in the PATRIC database. Finally, the PATRIC Web site has been expanded to enable AMR-based custom page views so that researchers can easily explore AMR data and design experiments based on whole genomes or individual genes.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Alice R Wattam
- Corresponding author: Alice R. Wattam, Biocomplexity Institute of Virginia Tech, 1015 Life Science Circle, Blacksburg, VA 24061 USA. Tel.: 540-231-1263; Fax: 540-231-2606; E-mail:
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18
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Adiga A, Venkatramanan S, Schlitt J, Peddireddy A, Dickerman A, Bura A, Warren A, Klahn BD, Mao C, Xie D, Machi D, Raymond E, Meng F, Barrow G, Mortveit H, Chen J, Walke J, Goldstein J, Wilson ML, Orr M, Porebski P, Telionis PA, Beckman R, Hoops S, Eubank S, Baek YY, Lewis B, Marathe M, Barrett C. Evaluating the impact of international airline suspensions on the early global spread of COVID-19. medRxiv 2020:2020.02.20.20025882. [PMID: 32511466 PMCID: PMC7255786 DOI: 10.1101/2020.02.20.20025882] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/13/2023]
Abstract
Global airline networks play a key role in the global importation of emerging infectious diseases. Detailed information on air traffic between international airports has been demonstrated to be useful in retrospectively validating and prospectively predicting case emergence in other countries. In this paper, we use a well-established metric known as effective distance on the global air traffic data from IATA to quantify risk of emergence for different countries as a consequence of direct importation from China, and compare it against arrival times for the first 24 countries. Using this model trained on official first reports from WHO, we estimate time of arrival (ToA) for all other countries. We then incorporate data on airline suspensions to recompute the effective distance and assess the effect of such cancellations in delaying the estimated arrival time for all other countries. Finally we use the infectious disease vulnerability indices to explain some of the estimated reporting delays.
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19
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Davis JJ, Wattam AR, Aziz RK, Brettin T, Butler R, Butler RM, Chlenski P, Conrad N, Dickerman A, Dietrich EM, Gabbard JL, Gerdes S, Guard A, Kenyon RW, Machi D, Mao C, Murphy-Olson D, Nguyen M, Nordberg EK, Olsen GJ, Olson RD, Overbeek JC, Overbeek R, Parrello B, Pusch GD, Shukla M, Thomas C, VanOeffelen M, Vonstein V, Warren AS, Xia F, Xie D, Yoo H, Stevens R. The PATRIC Bioinformatics Resource Center: expanding data and analysis capabilities. Nucleic Acids Res 2020; 48:D606-D612. [PMID: 31667520 PMCID: PMC7145515 DOI: 10.1093/nar/gkz943] [Citation(s) in RCA: 394] [Impact Index Per Article: 98.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 10/07/2019] [Accepted: 10/11/2019] [Indexed: 12/24/2022] Open
Abstract
The PathoSystems Resource Integration Center (PATRIC) is the bacterial Bioinformatics Resource Center funded by the National Institute of Allergy and Infectious Diseases (https://www.patricbrc.org). PATRIC supports bioinformatic analyses of all bacteria with a special emphasis on pathogens, offering a rich comparative analysis environment that provides users with access to over 250 000 uniformly annotated and publicly available genomes with curated metadata. PATRIC offers web-based visualization and comparative analysis tools, a private workspace in which users can analyze their own data in the context of the public collections, services that streamline complex bioinformatic workflows and command-line tools for bulk data analysis. Over the past several years, as genomic and other omics-related experiments have become more cost-effective and widespread, we have observed considerable growth in the usage of and demand for easy-to-use, publicly available bioinformatic tools and services. Here we report the recent updates to the PATRIC resource, including new web-based comparative analysis tools, eight new services and the release of a command-line interface to access, query and analyze data.
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Affiliation(s)
- James J Davis
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL 60637, USA
- Division of Data Science and Learning, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Alice R Wattam
- Division of Data Science and Learning, Argonne National Laboratory, Argonne, IL 60439, USA
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA 22904, USA
| | - Ramy K Aziz
- Department of Microbiology and Immunology, Faculty of Pharmacy, Cairo University, 11562 Cairo, Egypt
- Center for Genome and Microbiome Research, Cairo University, 11562 Cairo, Egypt
| | - Thomas Brettin
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL 60637, USA
- Computing Environment and Life Sciences, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Ralph Butler
- Division of Data Science and Learning, Argonne National Laboratory, Argonne, IL 60439, USA
- Middle Tennessee State University, Murfreesboro, TN 37132, USA
| | - Rory M Butler
- Division of Data Science and Learning, Argonne National Laboratory, Argonne, IL 60439, USA
| | | | - Neal Conrad
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL 60637, USA
- Division of Data Science and Learning, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Allan Dickerman
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA 22904, USA
| | - Emily M Dietrich
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL 60637, USA
- Computing Environment and Life Sciences, Argonne National Laboratory, Argonne, IL 60439, USA
| | | | - Svetlana Gerdes
- Fellowship for Interpretation of Genomes, Burr Ridge, IL 60527, USA
| | - Andrew Guard
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL 60637, USA
| | - Ronald W Kenyon
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA 22904, USA
| | - Dustin Machi
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA 22904, USA
| | - Chunhong Mao
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA 22904, USA
| | - Dan Murphy-Olson
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL 60637, USA
- Computing Environment and Life Sciences, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Marcus Nguyen
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL 60637, USA
- Division of Data Science and Learning, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Eric K Nordberg
- Transportation Institute, Virginia Tech University, Blacksburg, VA 24061, USA
| | - Gary J Olsen
- Department of Microbiology, University of Illinois, Urbana, IL 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana, IL 61801, USA
| | - Robert D Olson
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL 60637, USA
- Division of Data Science and Learning, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Jamie C Overbeek
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL 60637, USA
- Division of Data Science and Learning, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Ross Overbeek
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL 60637, USA
| | - Bruce Parrello
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL 60637, USA
- Division of Data Science and Learning, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Gordon D Pusch
- Fellowship for Interpretation of Genomes, Burr Ridge, IL 60527, USA
| | - Maulik Shukla
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL 60637, USA
- Division of Data Science and Learning, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Chris Thomas
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL 60637, USA
| | | | | | - Andrew S Warren
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA 22904, USA
| | - Fangfang Xia
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL 60637, USA
- Division of Data Science and Learning, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Dawen Xie
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA 22904, USA
| | - Hyunseung Yoo
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL 60637, USA
- Division of Data Science and Learning, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Rick Stevens
- Computing Environment and Life Sciences, Argonne National Laboratory, Argonne, IL 60439, USA
- University of Chicago, Department of Computer Science, Chicago, IL 60637, USA
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20
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Wattam AR, Davis JJ, Assaf R, Boisvert S, Brettin T, Bun C, Conrad N, Dietrich EM, Disz T, Gabbard JL, Gerdes S, Henry CS, Kenyon RW, Machi D, Mao C, Nordberg EK, Olsen GJ, Murphy-Olson DE, Olson R, Overbeek R, Parrello B, Pusch GD, Shukla M, Vonstein V, Warren A, Xia F, Yoo H, Stevens RL. Improvements to PATRIC, the all-bacterial Bioinformatics Database and Analysis Resource Center. Nucleic Acids Res 2016; 45:D535-D542. [PMID: 27899627 PMCID: PMC5210524 DOI: 10.1093/nar/gkw1017] [Citation(s) in RCA: 1036] [Impact Index Per Article: 129.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2016] [Revised: 10/14/2016] [Accepted: 11/09/2016] [Indexed: 12/14/2022] Open
Abstract
The Pathosystems Resource Integration Center (PATRIC) is the bacterial Bioinformatics Resource Center (https://www.patricbrc.org). Recent changes to PATRIC include a redesign of the web interface and some new services that provide users with a platform that takes them from raw reads to an integrated analysis experience. The redesigned interface allows researchers direct access to tools and data, and the emphasis has changed to user-created genome-groups, with detailed summaries and views of the data that researchers have selected. Perhaps the biggest change has been the enhanced capability for researchers to analyze their private data and compare it to the available public data. Researchers can assemble their raw sequence reads and annotate the contigs using RASTtk. PATRIC also provides services for RNA-Seq, variation, model reconstruction and differential expression analysis, all delivered through an updated private workspace. Private data can be compared by ‘virtual integration’ to any of PATRIC's public data. The number of genomes available for comparison in PATRIC has expanded to over 80 000, with a special emphasis on genomes with antimicrobial resistance data. PATRIC uses this data to improve both subsystem annotation and k-mer classification, and tags new genomes as having signatures that indicate susceptibility or resistance to specific antibiotics.
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Affiliation(s)
- Alice R Wattam
- Biocomplexity Institute, Virginia Tech University, Blacksburg, VA 24060, USA
| | - James J Davis
- Computation Institute, University of Chicago, Chicago, IL 60637, USA.,Computing, Environment and Life Sciences, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Rida Assaf
- Department of Computer Science, University of Chicago, Chicago, IL 60637, USA
| | | | - Thomas Brettin
- Computation Institute, University of Chicago, Chicago, IL 60637, USA.,Computing, Environment and Life Sciences, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Christopher Bun
- Department of Computer Science, University of Chicago, Chicago, IL 60637, USA
| | - Neal Conrad
- Computation Institute, University of Chicago, Chicago, IL 60637, USA.,Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, USA
| | - Emily M Dietrich
- Computation Institute, University of Chicago, Chicago, IL 60637, USA.,Computing, Environment and Life Sciences, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Terry Disz
- Fellowship for Interpretation of Genomes, Burr Ridge, IL 60527, USA
| | - Joseph L Gabbard
- Grado Department of Industrial & Systems Engineering, Virginia Tech, Blacksburg, VA 24060, USA
| | - Svetlana Gerdes
- Fellowship for Interpretation of Genomes, Burr Ridge, IL 60527, USA
| | - Christopher S Henry
- Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, USA
| | - Ronald W Kenyon
- Biocomplexity Institute, Virginia Tech University, Blacksburg, VA 24060, USA
| | - Dustin Machi
- Biocomplexity Institute, Virginia Tech University, Blacksburg, VA 24060, USA
| | - Chunhong Mao
- Biocomplexity Institute, Virginia Tech University, Blacksburg, VA 24060, USA
| | - Eric K Nordberg
- Biocomplexity Institute, Virginia Tech University, Blacksburg, VA 24060, USA
| | - Gary J Olsen
- Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Daniel E Murphy-Olson
- Computing, Environment and Life Sciences, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Robert Olson
- Computation Institute, University of Chicago, Chicago, IL 60637, USA.,Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, USA
| | - Ross Overbeek
- Computing, Environment and Life Sciences, Argonne National Laboratory, Argonne, IL 60439, USA.,Fellowship for Interpretation of Genomes, Burr Ridge, IL 60527, USA
| | - Bruce Parrello
- Computing, Environment and Life Sciences, Argonne National Laboratory, Argonne, IL 60439, USA.,Fellowship for Interpretation of Genomes, Burr Ridge, IL 60527, USA
| | - Gordon D Pusch
- Fellowship for Interpretation of Genomes, Burr Ridge, IL 60527, USA
| | - Maulik Shukla
- Computation Institute, University of Chicago, Chicago, IL 60637, USA.,Computing, Environment and Life Sciences, Argonne National Laboratory, Argonne, IL 60439, USA
| | | | - Andrew Warren
- Biocomplexity Institute, Virginia Tech University, Blacksburg, VA 24060, USA
| | - Fangfang Xia
- Computation Institute, University of Chicago, Chicago, IL 60637, USA.,Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, USA
| | - Hyunseung Yoo
- Computation Institute, University of Chicago, Chicago, IL 60637, USA.,Computing, Environment and Life Sciences, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Rick L Stevens
- Computation Institute, University of Chicago, Chicago, IL 60637, USA.,Computing, Environment and Life Sciences, Argonne National Laboratory, Argonne, IL 60439, USA.,Department of Computer Science, University of Chicago, Chicago, IL 60637, USA
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21
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Warren AS, Aurrecoechea C, Brunk B, Desai P, Emrich S, Giraldo-Calderón GI, Harb O, Hix D, Lawson D, Machi D, Mao C, McClelland M, Nordberg E, Shukla M, Vosshall LB, Wattam AR, Will R, Yoo HS, Sobral B. RNA-Rocket: an RNA-Seq analysis resource for infectious disease research. Bioinformatics 2015; 31:1496-8. [PMID: 25573919 PMCID: PMC4410666 DOI: 10.1093/bioinformatics/btv002] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2014] [Revised: 12/10/2014] [Accepted: 12/31/2014] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION RNA-Seq is a method for profiling transcription using high-throughput sequencing and is an important component of many research projects that wish to study transcript isoforms, condition specific expression and transcriptional structure. The methods, tools and technologies used to perform RNA-Seq analysis continue to change, creating a bioinformatics challenge for researchers who wish to exploit these data. Resources that bring together genomic data, analysis tools, educational material and computational infrastructure can minimize the overhead required of life science researchers. RESULTS RNA-Rocket is a free service that provides access to RNA-Seq and ChIP-Seq analysis tools for studying infectious diseases. The site makes available thousands of pre-indexed genomes, their annotations and the ability to stream results to the bioinformatics resources VectorBase, EuPathDB and PATRIC. The site also provides a combination of experimental data and metadata, examples of pre-computed analysis, step-by-step guides and a user interface designed to enable both novice and experienced users of RNA-Seq data. AVAILABILITY AND IMPLEMENTATION RNA-Rocket is available at rnaseq.pathogenportal.org. Source code for this project can be found at github.com/cidvbi/PathogenPortal. CONTACT anwarren@vt.edu SUPPLEMENTARY INFORMATION Supplementary materials are available at Bioinformatics online.
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Affiliation(s)
- Andrew S Warren
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Cristina Aurrecoechea
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Brian Brunk
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Prerak Desai
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Scott Emrich
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Gloria I Giraldo-Calderón
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Omar Harb
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Deborah Hix
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Daniel Lawson
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Dustin Machi
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Chunhong Mao
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Michael McClelland
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Eric Nordberg
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Maulik Shukla
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Leslie B Vosshall
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Alice R Wattam
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Rebecca Will
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Hyun Seung Yoo
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Bruno Sobral
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
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22
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Wattam AR, Abraham D, Dalay O, Disz TL, Driscoll T, Gabbard JL, Gillespie JJ, Gough R, Hix D, Kenyon R, Machi D, Mao C, Nordberg EK, Olson R, Overbeek R, Pusch GD, Shukla M, Schulman J, Stevens RL, Sullivan DE, Vonstein V, Warren A, Will R, Wilson MJC, Yoo HS, Zhang C, Zhang Y, Sobral BW. PATRIC, the bacterial bioinformatics database and analysis resource. Nucleic Acids Res 2013; 42:D581-91. [PMID: 24225323 PMCID: PMC3965095 DOI: 10.1093/nar/gkt1099] [Citation(s) in RCA: 873] [Impact Index Per Article: 79.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
The Pathosystems Resource Integration Center (PATRIC) is the all-bacterial Bioinformatics Resource Center (BRC) (http://www.patricbrc.org). A joint effort by two of the original National Institute of Allergy and Infectious Diseases-funded BRCs, PATRIC provides researchers with an online resource that stores and integrates a variety of data types [e.g. genomics, transcriptomics, protein-protein interactions (PPIs), three-dimensional protein structures and sequence typing data] and associated metadata. Datatypes are summarized for individual genomes and across taxonomic levels. All genomes in PATRIC, currently more than 10,000, are consistently annotated using RAST, the Rapid Annotations using Subsystems Technology. Summaries of different data types are also provided for individual genes, where comparisons of different annotations are available, and also include available transcriptomic data. PATRIC provides a variety of ways for researchers to find data of interest and a private workspace where they can store both genomic and gene associations, and their own private data. Both private and public data can be analyzed together using a suite of tools to perform comparative genomic or transcriptomic analysis. PATRIC also includes integrated information related to disease and PPIs. All the data and integrated analysis and visualization tools are freely available. This manuscript describes updates to the PATRIC since its initial report in the 2007 NAR Database Issue.
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Affiliation(s)
- Alice R Wattam
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Computation Institute, University of Chicago, Chicago, IL 60637, USA, Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL 60637, USA, Grado Department of Industrial & Systems Engineering, Virginia Tech, Blacksburg, VA 24060, USA, Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Fellowship for Interpretation of Genomes, Burr Ridge, IL 60527, USA, Computing, Environment, and Life Sciences, Argonne National Laboratory, Argonne, IL 60637, USA and Nestlé Institute of Health Sciences SA, Campus EPFL, Quartier de L'innovation, Lausanne, Switzerland
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