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.
Reviewing Editor
- Talía Malagón, McGill University, Canada
Version history
- Preprint posted: August 31, 2021 (view preprint)
- Received: September 2, 2021
- Accepted: June 3, 2022
- Accepted Manuscript published: June 21, 2022 (version 1)
- Version of Record published: June 24, 2022 (version 2)
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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Further reading
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- Ecology
- Epidemiology and Global Health
Non-pharmaceutical interventions implemented to block SARS-CoV-2 transmission in early 2020 led to global reductions in the incidence of invasive pneumococcal disease (IPD). By contrast, most European countries reported an increase in antibiotic resistance among invasive Streptococcus pneumoniae isolates from 2019 to 2020, while an increasing number of studies reported stable pneumococcal carriage prevalence over the same period. To disentangle the impacts of the COVID-19 pandemic on pneumococcal epidemiology in the community setting, we propose a mathematical model formalizing simultaneous transmission of SARS-CoV-2 and antibiotic-sensitive and -resistant strains of S. pneumoniae. To test hypotheses underlying these trends five mechanisms were built into the model and examined: (1) a population-wide reduction of antibiotic prescriptions in the community, (2) lockdown effect on pneumococcal transmission, (3) a reduced risk of developing an IPD due to the absence of common respiratory viruses, (4) community azithromycin use in COVID-19 infected individuals, (5) and a longer carriage duration of antibiotic-resistant pneumococcal strains. Among 31 possible pandemic scenarios involving mechanisms individually or in combination, model simulations surprisingly identified only two scenarios that reproduced the reported trends in the general population. They included factors (1), (3), and (4). These scenarios replicated a nearly 50% reduction in annual IPD, and an increase in antibiotic resistance from 20% to 22%, all while maintaining a relatively stable pneumococcal carriage. Exploring further, higher SARS-CoV-2 R0 values and synergistic within-host virus-bacteria interaction mechanisms could have additionally contributed to the observed antibiotic resistance increase. Our work demonstrates the utility of the mathematical modeling approach in unraveling the complex effects of the COVID-19 pandemic responses on AMR dynamics.
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- Epidemiology and Global Health
Background:
The aim of our study was to test the hypothesis that the community contact tracing strategy of testing contacts in households immediately instead of at the end of quarantine had an impact on the transmission of SARS-CoV-2 in schools in Reggio Emilia Province.
Methods:
We analysed surveillance data on notification of COVID-19 cases in schools between 1 September 2020 and 4 April 2021. We have applied a mediation analysis that allows for interaction between the intervention (before/after period) and the mediator.
Results:
Median tracing delay decreased from 7 to 3.1 days and the percentage of the known infection source increased from 34–54.8% (incident rate ratio-IRR 1.61 1.40–1.86). Implementation of prompt contact tracing was associated with a 10% decrease in the number of secondary cases (excess relative risk –0.1 95% CI –0.35–0.15). Knowing the source of infection of the index case led to a decrease in secondary transmission (IRR 0.75 95% CI 0.63–0.91) while the decrease in tracing delay was associated with decreased risk of secondary cases (1/IRR 0.97 95% CI 0.94–1.01 per one day of delay). The direct effect of the intervention accounted for the 29% decrease in the number of secondary cases (excess relative risk –0.29 95%–0.61 to 0.03).
Conclusions:
Prompt contact testing in the community reduces the time of contact tracing and increases the ability to identify the source of infection in school outbreaks. Although there are strong reasons for thinking it is a causal link, observed differences can be also due to differences in the force of infection and to other control measures put in place.
Funding:
This project was carried out with the technical and financial support of the Italian Ministry of Health – CCM 2020 and Ricerca Corrente Annual Program 2023.