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
- Johns Hopkins University, United States
- Harvard University, United States
- Johns Hopkins University Applied Physics Laboratory, United States
- Pennsylvania State University, United States
- University of North Carolina at Chapel Hill, United States
- University of Pittsburgh, United States
- École Polytechnique Fédérale de Lausanne, Switzerland
- University of Victoria, Canada
- Northeastern University, United States
- University of Southern California, United States
- University of Virginia, United States
- University of North Carolina at Charlotte, United States
- Columbia University Medical Center, United States
- Centers for Disease Control and Prevention, United States
- United States Geological Survey, United States
- National Institutes of Health, United States
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-10 Scenario Modeling Hub, an ensemble of nine mechanistic models produced six-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, though may have had even greater impacts, considering the underestimated resurgence magnitude from the model.
Data availability
All model output data are available on the project github at https://github.com/midas-network/covid19-scenario-modeling-hub. Code and data specific to this manuscript has been consolidated into a repository at https://github.com/midas-network/covid19-scenario-modeling-hub/tree/master/paper-source-code/round-7. All data used are publicly available.
Article and author information
Author details
Rebecca K Borchering
Kaitlin Rainwater-Lovett
Przemyslaw Porebski
Funding
National Science Foundation (2127976)
- Shaun Truelove
- Claire P Smith
- Juan Dent Hulse
- Joshua Kaminsky
- Elizabeth C Lee
- Alison Hill
California Department of Public Health
- Shaun Truelove
- Claire P Smith
- Justin Lessler
- Juan Dent Hulse
- Joshua Kaminsky
- Elizabeth C Lee
- Javier Perez-Saez
Johns Hopkins University
- Shaun Truelove
- Claire P Smith
- Justin Lessler
- Juan Dent Hulse
- Joshua Kaminsky
- Elizabeth C Lee
- Javier Perez-Saez
- Alison Hill
National Institutes of Health (R01GM140564)
- Justin Lessler
Swiss National Science Foundation (200021--172578))
- Joseph C Lemairtre
National Institutes of Health (R01GM109718)
- Przemyslaw Porebski
- Srinivasan Venkatramanan
- Aniruddha Adiga
- Bryan Lewis
- Brian Klahn
- Joseph Outten
- Mark Orr
- Galen Harrison
- Benjamin Hurt
- Jiangzhuo Chen
- Anil Vullikanti
- Madhav Marathe
- Stefan Hoops
- Parantapa Bhattacharya
- Dustin Machi
Virginia Department of Health (VDH-21-501-0135)
- Przemyslaw Porebski
- Srinivasan Venkatramanan
- Aniruddha Adiga
- Bryan Lewis
- Brian Klahn
- Joseph Outten
- Mark Orr
- Galen Harrison
- Benjamin Hurt
- Jiangzhuo Chen
- Anil Vullikanti
- Madhav Marathe
- Stefan Hoops
- Parantapa Bhattacharya
- Dustin Machi
National Science Foundation (OAC-1916805,CCF-1918656,CCF-2142997,OAC-2027541,TG-BIO210084)
- Przemyslaw Porebski
- Srinivasan Venkatramanan
- Aniruddha Adiga
- Bryan Lewis
- Brian Klahn
- Joseph Outten
- Mark Orr
- Galen Harrison
- Benjamin Hurt
- Jiangzhuo Chen
- Anil Vullikanti
- Madhav Marathe
- Stefan Hoops
- Parantapa Bhattacharya
- Dustin Machi
Centers for Disease Control and Prevention (75D30119C05935)
- Przemyslaw Porebski
- Srinivasan Venkatramanan
- Aniruddha Adiga
- Bryan Lewis
- Brian Klahn
- Joseph Outten
- Mark Orr
- Galen Harrison
- Benjamin Hurt
- Jiangzhuo Chen
- Anil Vullikanti
- Madhav Marathe
- Stefan Hoops
- Parantapa Bhattacharya
- Dustin Machi
Defense Threat Reduction Agency (S-D00189-15-TO-01-UVA)
- Przemyslaw Porebski
- Srinivasan Venkatramanan
- Aniruddha Adiga
- Bryan Lewis
- Brian Klahn
- Joseph Outten
- Mark Orr
- Galen Harrison
- Benjamin Hurt
- Jiangzhuo Chen
- Anil Vullikanti
- Madhav Marathe
- Stefan Hoops
- Parantapa Bhattacharya
- Dustin Machi
Centers for Disease Control and Prevention (200-2016-91781)
- Shaun Truelove
- Claire P Smith
- Justin Lessler
- Joseph C Lemairtre
- Joshua Kaminsky
- Alison Hill
National Science Foundation (2028301,2126278)
- Rebecca K Borchering
- Katriona Shea
University of Virginia
- Przemyslaw Porebski
- Srinivasan Venkatramanan
- Aniruddha Adiga
- Bryan Lewis
- Brian Klahn
- Joseph Outten
- Mark Orr
- Galen Harrison
- Benjamin Hurt
- Jiangzhuo Chen
- Anil Vullikanti
- Madhav Marathe
- Stefan Hoops
- Parantapa Bhattacharya
- Dustin Machi
COVID-19 HPC Consortium
- Przemyslaw Porebski
- Srinivasan Venkatramanan
- Aniruddha Adiga
- Bryan Lewis
- Brian Klahn
- Joseph Outten
- Mark Orr
- Galen Harrison
- Benjamin Hurt
- Jiangzhuo Chen
- Anil Vullikanti
- Madhav Marathe
- Stefan Hoops
- Parantapa Bhattacharya
- Dustin Machi
Amazon Web Services
- Shaun Truelove
- Claire P Smith
- Justin Lessler
- Joseph C Lemairtre
- Juan Dent Hulse
- Joshua Kaminsky
- Elizabeth C Lee
- Javier Perez-Saez
- Alison Hill
Models of Infectious Disease Agent Study (MIDASUP-05)
- Shi Chen
- Rajib Paul
- Daniel Janies
- Jean-Claude Thill
North Carolina Biotechnology Center
- Shi Chen
- Rajib Paul
- Daniel Janies
- Jean-Claude Thill
National Institutes of Health (R01AI163023)
- Marta Galanti
- Teresa K Yamana
- Sen Pei
- Jeffrey L Shaman
Council of State and Territorial Epidemiologists (NU38OT000297)
- Marta Galanti
- Teresa K Yamana
- Sen Pei
- Jeffrey L Shaman
Morris-Singer Foundation
- Marta Galanti
- Teresa K Yamana
- Sen Pei
- Jeffrey L Shaman
Huck Institutes of the Life Sciences
- Katriona Shea
- Emily Howerton
National Institute of General Medical Sciences (5U24GM132013-02)
- Lucie Contamin
- John Levander
- Jessica Salerno
- Harry Hochheiser
United States Department of Health and Human Services (75A50121C00003)
- Luke C Mullany
- Matt Kinsey
- Kate Tallaksen
- Shelby Wilson
- Lauren Shin
- Kaitlin Rainwater-Lovett
United States Department of Health and Human Services (6U01IP001137)
- Jessica T Davis
- Ana Pastore y Piontti
- Alessandro Vespignani
United States Department of Health and Human Services (5U01IP0001137)
- Matteo Chinazzi
- Kunpeng Mu
- Xinyue Xiong
- Alessandro Vespignani
National Science Foundation (2027007)
- Ajitesh Srivastava
United States Department of Health and Human Services
- Shaun Truelove
- Claire P Smith
- Justin Lessler
- Juan Dent Hulse
- Joshua Kaminsky
- Elizabeth C Lee
- Javier Perez-Saez
- Alison Hill
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
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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 11:e73584.
https://doi.org/10.7554/eLife.73584
Further reading
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- Epidemiology and Global Health
Background:
Biological aging exhibits heterogeneity across multi-organ systems. However, it remains unclear how is lifestyle associated with overall and organ-specific aging and which factors contribute most in Southwest China.
Methods:
This study involved 8396 participants who completed two surveys from the China Multi-Ethnic Cohort (CMEC) study. The healthy lifestyle index (HLI) was developed using five lifestyle factors: smoking, alcohol, diet, exercise, and sleep. The comprehensive and organ-specific biological ages (BAs) were calculated using the Klemera–Doubal method based on longitudinal clinical laboratory measurements, and validation were conducted to select BA reflecting related diseases. Fixed effects model was used to examine the associations between HLI or its components and the acceleration of validated BAs. We further evaluated the relative contribution of lifestyle components to comprehension and organ systems BAs using quantile G-computation.
Results:
About two-thirds of participants changed HLI scores between surveys. After validation, three organ-specific BAs (the cardiopulmonary, metabolic, and liver BAs) were identified as reflective of specific diseases and included in further analyses with the comprehensive BA. The health alterations in HLI showed a protective association with the acceleration of all BAs, with a mean shift of –0.19 (95% CI −0.34, –0.03) in the comprehensive BA acceleration. Diet and smoking were the major contributors to overall negative associations of five lifestyle factors, with the comprehensive BA and metabolic BA accounting for 24% and 55% respectively.
Conclusions:
Healthy lifestyle changes were inversely related to comprehensive and organ-specific biological aging in Southwest China, with diet and smoking contributing most to comprehensive and metabolic BA separately. Our findings highlight the potential of lifestyle interventions to decelerate aging and identify intervention targets to limit organ-specific aging in less-developed regions.
Funding:
This work was primarily supported by the National Natural Science Foundation of China (Grant No. 82273740) and Sichuan Science and Technology Program (Natural Science Foundation of Sichuan Province, Grant No. 2024NSFSC0552). The CMEC study was funded by the National Key Research and Development Program of China (Grant No. 2017YFC0907305, 2017YFC0907300). The sponsors had no role in the design, analysis, interpretation, or writing of this article.
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- Epidemiology and Global Health
- Microbiology and Infectious Disease
Background:
In many settings, a large fraction of the population has both been vaccinated against and infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Hence, quantifying the protection provided by post-infection vaccination has become critical for policy. We aimed to estimate the protective effect against SARS-CoV-2 reinfection of an additional vaccine dose after an initial Omicron variant infection.
Methods:
We report a retrospective, population-based cohort study performed in Shanghai, China, using electronic databases with information on SARS-CoV-2 infections and vaccination history. We compared reinfection incidence by post-infection vaccination status in individuals initially infected during the April–May 2022 Omicron variant surge in Shanghai and who had been vaccinated before that period. Cox models were fit to estimate adjusted hazard ratios (aHRs).
Results:
275,896 individuals were diagnosed with real-time polymerase chain reaction-confirmed SARS-CoV-2 infection in April–May 2022; 199,312/275,896 were included in analyses on the effect of a post-infection vaccine dose. Post-infection vaccination provided protection against reinfection (aHR 0.82; 95% confidence interval 0.79–0.85). For patients who had received one, two, or three vaccine doses before their first infection, hazard ratios for the post-infection vaccination effect were 0.84 (0.76–0.93), 0.87 (0.83–0.90), and 0.96 (0.74–1.23), respectively. Post-infection vaccination within 30 and 90 days before the second Omicron wave provided different degrees of protection (in aHR): 0.51 (0.44–0.58) and 0.67 (0.61–0.74), respectively. Moreover, for all vaccine types, but to different extents, a post-infection dose given to individuals who were fully vaccinated before first infection was protective.
Conclusions:
In previously vaccinated and infected individuals, an additional vaccine dose provided protection against Omicron variant reinfection. These observations will inform future policy decisions on COVID-19 vaccination in China and other countries.
Funding:
This study was funded the Key Discipline Program of Pudong New Area Health System (PWZxk2022-25), the Development and Application of Intelligent Epidemic Surveillance and AI Analysis System (21002411400), the Shanghai Public Health System Construction (GWVI-11.2-XD08), the Shanghai Health Commission Key Disciplines (GWVI-11.1-02), the Shanghai Health Commission Clinical Research Program (20214Y0020), the Shanghai Natural Science Foundation (22ZR1414600), and the Shanghai Young Health Talents Program (2022YQ076).
