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
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
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.
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Further reading
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- Epidemiology and Global Health
- Genetics and Genomics
Background:
Maternal smoking has been linked to adverse health outcomes in newborns but the extent to which it impacts newborn health has not been quantified through an aggregated cord blood DNA methylation (DNAm) score. Here, we examine the feasibility of using cord blood DNAm scores leveraging large external studies as discovery samples to capture the epigenetic signature of maternal smoking and its influence on newborns in White European and South Asian populations.
Methods:
We first examined the association between individual CpGs and cigarette smoking during pregnancy, and smoking exposure in two White European birth cohorts (n=744). Leveraging established CpGs for maternal smoking, we constructed a cord blood epigenetic score of maternal smoking that was validated in one of the European-origin cohorts (n=347). This score was then tested for association with smoking status, secondary smoking exposure during pregnancy, and health outcomes in offspring measured after birth in an independent White European (n=397) and a South Asian birth cohort (n=504).
Results:
Several previously reported genes for maternal smoking were supported, with the strongest and most consistent association signal from the GFI1 gene (6 CpGs with p<5 × 10-5). The epigenetic maternal smoking score was strongly associated with smoking status during pregnancy (OR = 1.09 [1.07, 1.10], p=5.5 × 10-33) and more hours of self-reported smoking exposure per week (1.93 [1.27, 2.58], p=7.8 × 10-9) in White Europeans. However, it was not associated with self-reported exposure (p>0.05) among South Asians, likely due to a lack of smoking in this group. The same score was consistently associated with a smaller birth size (–0.37±0.12 cm, p=0.0023) in the South Asian cohort and a lower birth weight (–0.043±0.013 kg, p=0.0011) in the combined cohorts.
Conclusions:
This cord blood epigenetic score can help identify babies exposed to maternal smoking and assess its long-term impact on growth. Notably, these results indicate a consistent association between the DNAm signature of maternal smoking and a small body size and low birth weight in newborns, in both White European mothers who exhibited some amount of smoking and in South Asian mothers who themselves were not active smokers.
Funding:
This study was funded by the Canadian Institutes of Health Research Metabolomics Team Grant: MWG-146332.
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- Epidemiology and Global Health
- Microbiology and Infectious Disease
Background:
Few national-level studies have evaluated the impact of ‘hybrid’ immunity (vaccination coupled with recovery from infection) from the Omicron variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
Methods:
From May 2020 to December 2022, we conducted serial assessments (each of ~4000–9000 adults) examining SARS-CoV-2 antibodies within a mostly representative Canadian cohort drawn from a national online polling platform. Adults, most of whom were vaccinated, reported viral test-confirmed infections and mailed self-collected dried blood spots (DBSs) to a central lab. Samples underwent highly sensitive and specific antibody assays to spike and nucleocapsid protein antigens, the latter triggered only by infection. We estimated cumulative SARS-CoV-2 incidence prior to the Omicron period and during the BA.1/1.1 and BA.2/5 waves. We assessed changes in antibody levels and in age-specific active immunity levels.
Results:
Spike levels were higher in infected than in uninfected adults, regardless of vaccination doses. Among adults vaccinated at least thrice and infected more than 6 months earlier, spike levels fell notably and continuously for the 9-month post-vaccination. In contrast, among adults infected within 6 months, spike levels declined gradually. Declines were similar by sex, age group, and ethnicity. Recent vaccination attenuated declines in spike levels from older infections. In a convenience sample, spike antibody and cellular responses were correlated. Near the end of 2022, about 35% of adults above age 60 had their last vaccine dose more than 6 months ago, and about 25% remained uninfected. The cumulative incidence of SARS-CoV-2 infection rose from 13% (95% confidence interval 11–14%) before omicron to 78% (76–80%) by December 2022, equating to 25 million infected adults cumulatively. However, the coronavirus disease 2019 (COVID-19) weekly death rate during the BA.2/5 waves was less than half of that during the BA.1/1.1 wave, implying a protective role for hybrid immunity.
Conclusions:
Strategies to maintain population-level hybrid immunity require up-to-date vaccination coverage, including among those recovering from infection. Population-based, self-collected DBSs are a practicable biological surveillance platform.
Funding:
Funding was provided by the COVID-19 Immunity Task Force, Canadian Institutes of Health Research, Pfizer Global Medical Grants, and St. Michael’s Hospital Foundation. PJ and ACG are funded by the Canada Research Chairs Program.