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Prasad PV, Steele MK, Reed C, Meyers LA, Du Z, Pasco R, Alfaro-Murillo JA, Lewis B, Venkatramanan S, Schlitt J, Chen J, Orr M, Wilson ML, Eubank S, Wang L, Chinazzi M, Pastore y Piontti A, Davis JT, Halloran ME, Longini I, Vespignani A, Pei S, Galanti M, Kandula S, Shaman J, Haw DJ, Arinaminpathy N, Biggerstaff M. Multimodeling approach to evaluating the efficacy of layering pharmaceutical and nonpharmaceutical interventions for influenza pandemics. Proc Natl Acad Sci U S A 2023; 120:e2300590120. [PMID: 37399393 PMCID: PMC10334766 DOI: 10.1073/pnas.2300590120] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 05/21/2023] [Indexed: 07/05/2023] Open
Abstract
When an influenza pandemic emerges, temporary school closures and antiviral treatment may slow virus spread, reduce the overall disease burden, and provide time for vaccine development, distribution, and administration while keeping a larger portion of the general population infection free. The impact of such measures will depend on the transmissibility and severity of the virus and the timing and extent of their implementation. To provide robust assessments of layered pandemic intervention strategies, the Centers for Disease Control and Prevention (CDC) funded a network of academic groups to build a framework for the development and comparison of multiple pandemic influenza models. Research teams from Columbia University, Imperial College London/Princeton University, Northeastern University, the University of Texas at Austin/Yale University, and the University of Virginia independently modeled three prescribed sets of pandemic influenza scenarios developed collaboratively by the CDC and network members. Results provided by the groups were aggregated into a mean-based ensemble. The ensemble and most component models agreed on the ranking of the most and least effective intervention strategies by impact but not on the magnitude of those impacts. In the scenarios evaluated, vaccination alone, due to the time needed for development, approval, and deployment, would not be expected to substantially reduce the numbers of illnesses, hospitalizations, and deaths that would occur. Only strategies that included early implementation of school closure were found to substantially mitigate early spread and allow time for vaccines to be developed and administered, especially under a highly transmissible pandemic scenario.
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Affiliation(s)
- Pragati V. Prasad
- Applied Research and Modeling Team, Influenza Division, United States Centers for Disease Control and Prevention, Atlanta, GA30333
| | - Molly K. Steele
- Applied Research and Modeling Team, Influenza Division, United States Centers for Disease Control and Prevention, Atlanta, GA30333
| | - Carrie Reed
- Applied Research and Modeling Team, Influenza Division, United States Centers for Disease Control and Prevention, Atlanta, GA30333
| | - Lauren Ancel Meyers
- Section of Integrative Biology and Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX78712
| | - Zhanwei Du
- Section of Integrative Biology and Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX78712
| | - Remy Pasco
- Section of Integrative Biology and Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX78712
| | - Jorge A. Alfaro-Murillo
- Department of Biostatistics & Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT06510
| | - Bryan Lewis
- Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, VA22911
| | | | - James Schlitt
- Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, VA22911
| | - Jiangzhuo Chen
- Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, VA22911
| | - Mark Orr
- Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, VA22911
| | - Mandy L. Wilson
- Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, VA22911
| | - Stephen Eubank
- Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, VA22911
- Public Health Sciences, University of Virginia, Charlottesville, VA22903
| | - Lijing Wang
- Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, VA22911
| | - Matteo Chinazzi
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA02115
| | - Ana Pastore y Piontti
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA02115
| | - Jessica T. Davis
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA02115
| | - M. Elizabeth Halloran
- Fred Hutchinson Cancer Research Center, Seattle, WA98109
- Department of Biostatistics, University of Washington, Seattle, WA98195
| | - Ira Longini
- Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL32603
| | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA02115
| | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY10032
| | - Marta Galanti
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY10032
| | - Sasikiran Kandula
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY10032
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY10032
| | - David J. Haw
- Medical Research Council Centre for Global Infectious Disease Analysis, Imperial College London, LondonSW7 2AZ, United Kingdom
| | - Nimalan Arinaminpathy
- Medical Research Council Centre for Global Infectious Disease Analysis, Imperial College London, LondonSW7 2AZ, United Kingdom
| | - Matthew Biggerstaff
- Applied Research and Modeling Team, Influenza Division, United States Centers for Disease Control and Prevention, Atlanta, GA30333
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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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Borchering RK, Viboud C, Howerton E, Smith CP, Truelove S, Runge MC, Reich NG, Contamin L, Levander J, Salerno J, van Panhuis W, Kinsey M, Tallaksen K, Obrecht RF, Asher L, Costello C, Kelbaugh M, Wilson S, Shin L, Gallagher ME, Mullany LC, Rainwater-Lovett K, Lemaitre JC, Dent J, Grantz KH, Kaminsky J, Lauer SA, Lee EC, Meredith HR, Perez-Saez J, Keegan LT, 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, Schlitt J, Corbett P, Telionis PA, Wang L, Peddireddy AS, Hurt B, Chen J, Vullikanti A, Marathe M, Healy JM, Slayton RB, Biggerstaff M, Johansson MA, Shea K, Lessler J. Modeling of Future COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Rates and Nonpharmaceutical Intervention Scenarios - United States, April-September 2021. MMWR Morb Mortal Wkly Rep 2021. [PMID: 33988185 DOI: 10.15585/mmwr.mm7019e3externalicon] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/27/2023]
Abstract
After a period of rapidly declining U.S. COVID-19 incidence during January-March 2021, increases occurred in several jurisdictions (1,2) despite the rapid rollout of a large-scale vaccination program. This increase coincided with the spread of more transmissible variants of SARS-CoV-2, the virus that causes COVID-19, including B.1.1.7 (1,3) and relaxation of COVID-19 prevention strategies such as those for businesses, large-scale gatherings, and educational activities. To provide long-term projections of potential trends in COVID-19 cases, hospitalizations, and deaths, COVID-19 Scenario Modeling Hub teams used a multiple-model approach comprising six models to assess the potential course of COVID-19 in the United States across four scenarios with different vaccination coverage rates and effectiveness estimates and strength and implementation of nonpharmaceutical interventions (NPIs) (public health policies, such as physical distancing and masking) over a 6-month period (April-September 2021) using data available through March 27, 2021 (4). Among the four scenarios, an accelerated decline in NPI adherence (which encapsulates NPI mandates and population behavior) was shown to undermine vaccination-related gains over the subsequent 2-3 months and, in combination with increased transmissibility of new variants, could lead to surges in cases, hospitalizations, and deaths. A sharp decline in cases was projected by July 2021, with a faster decline in the high-vaccination scenarios. High vaccination rates and compliance with public health prevention measures are essential to control the COVID-19 pandemic and to prevent surges in hospitalizations and deaths in the coming months.
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Borchering RK, Viboud C, Howerton E, Smith CP, Truelove S, Runge MC, Reich NG, Contamin L, Levander J, Salerno J, van Panhuis W, Kinsey M, Tallaksen K, Obrecht RF, Asher L, Costello C, Kelbaugh M, Wilson S, Shin L, Gallagher ME, Mullany LC, Rainwater-Lovett K, Lemaitre JC, Dent J, Grantz KH, Kaminsky J, Lauer SA, Lee EC, Meredith HR, Perez-Saez J, Keegan LT, 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, Schlitt J, Corbett P, Telionis PA, Wang L, Peddireddy AS, Hurt B, Chen J, Vullikanti A, Marathe M, Healy JM, Slayton RB, Biggerstaff M, Johansson MA, Shea K, Lessler J. Modeling of Future COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Rates and Nonpharmaceutical Intervention Scenarios - United States, April-September 2021. MMWR Morb Mortal Wkly Rep 2021; 70:719-724. [PMID: 33988185 PMCID: PMC8118153 DOI: 10.15585/mmwr.mm7019e3] [Citation(s) in RCA: 86] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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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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Affiliation(s)
- M D Weist
- Dept. of Psychiatry, University of Maryland, Baltimore, MD 21201, USA.
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