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.
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.
Metrics
-
- 1,827
- views
-
- 223
- downloads
-
- 20
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Epidemiology and Global Health
- Genetics and Genomics
Alzheimer’s disease (AD) is a complex degenerative disease of the central nervous system, and elucidating its pathogenesis remains challenging. In this study, we used the inverse-variance weighted (IVW) model as the major analysis method to perform hypothesis-free Mendelian randomization (MR) analysis on the data from MRC IEU OpenGWAS (18,097 exposure traits and 16 AD outcome traits), and conducted sensitivity analysis with six models, to assess the robustness of the IVW results, to identify various classes of risk or protective factors for AD, early-onset AD, and late-onset AD. We generated 400,274 data entries in total, among which the major analysis method of the IVW model consists of 73,129 records with 4840 exposure traits, which fall into 10 categories: Disease, Medical laboratory science, Imaging, Anthropometric, Treatment, Molecular trait, Gut microbiota, Past history, Family history, and Lifestyle trait. More importantly, a freely accessed online platform called MRAD (https://gwasmrad.com/mrad/) has been developed using the Shiny package with MR analysis results. Additionally, novel potential AD therapeutic targets (CD33, TBCA, VPS29, GNAI3, PSME1) are identified, among which CD33 was positively associated with the main outcome traits of AD, as well as with both EOAD and LOAD. TBCA and VPS29 were negatively associated with the main outcome traits of AD, as well as with both EOAD and LOAD. GNAI3 and PSME1 were negatively associated with the main outcome traits of AD, as well as with LOAD, but had no significant causal association with EOAD. The findings of our research advance our understanding of the etiology of AD.
-
- Epidemiology and Global Health
Artificially sweetened beverages containing noncaloric monosaccharides were suggested as healthier alternatives to sugar-sweetened beverages. Nevertheless, the potential detrimental effects of these noncaloric monosaccharides on blood vessel function remain inadequately understood. We have established a zebrafish model that exhibits significant excessive angiogenesis induced by high glucose, resembling the hyperangiogenic characteristics observed in proliferative diabetic retinopathy (PDR). Utilizing this model, we observed that glucose and noncaloric monosaccharides could induce excessive formation of blood vessels, especially intersegmental vessels (ISVs). The excessively branched vessels were observed to be formed by ectopic activation of quiescent endothelial cells (ECs) into tip cells. Single-cell transcriptomic sequencing analysis of the ECs in the embryos exposed to high glucose revealed an augmented ratio of capillary ECs, proliferating ECs, and a series of upregulated proangiogenic genes. Further analysis and experiments validated that reduced foxo1a mediated the excessive angiogenesis induced by monosaccharides via upregulating the expression of marcksl1a. This study has provided new evidence showing the negative effects of noncaloric monosaccharides on the vascular system and the underlying mechanisms.