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Lopes R, Pham K, Klaassen F, Chitwood MH, Hahn AM, Redmond S, Swartwood NA, Salomon JA, Menzies NA, Cohen T, Grubaugh ND. Combining genomic data and infection estimates to characterize the complex dynamics of SARS-CoV-2 Omicron variants in the US. Cell Rep 2024; 43:114451. [PMID: 38970788 DOI: 10.1016/j.celrep.2024.114451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 05/03/2024] [Accepted: 06/20/2024] [Indexed: 07/08/2024] Open
Abstract
Omicron surged as a variant of concern in late 2021. Several distinct Omicron variants appeared and overtook each other. We combined variant frequencies and infection estimates from a nowcasting model for each US state to estimate variant-specific infections, attack rates, and effective reproduction numbers (Rt). BA.1 rapidly emerged, and we estimate that it infected 47.7% of the US population before it was replaced by BA.2. We estimate that BA.5 infected 35.7% of the US population, persisting in circulation for nearly 6 months. Other variants-BA.2, BA.4, and XBB-together infected 30.7% of the US population. We found a positive correlation between the state-level BA.1 attack rate and social vulnerability and a negative correlation between the BA.1 and BA.2 attack rates. Our findings illustrate the complex interplay between viral evolution, population susceptibility, and social factors during the Omicron emergence in the US.
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Affiliation(s)
- Rafael Lopes
- Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA.
| | - Kien Pham
- Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA
| | - Fayette Klaassen
- Department of Global Health and Population, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Melanie H Chitwood
- Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA
| | - Anne M Hahn
- Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA
| | - Seth Redmond
- Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA
| | - Nicole A Swartwood
- Department of Global Health and Population, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Joshua A Salomon
- Department of Health Policy, Stanford University School of Medicine, Stanford, CA, USA
| | - Nicolas A Menzies
- Department of Global Health and Population, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Ted Cohen
- Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA.
| | - Nathan D Grubaugh
- Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA; Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA.
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Lipsitch M, Bassett MT, Brownstein JS, Elliott P, Eyre D, Grabowski MK, Hay JA, Johansson MA, Kissler SM, Larremore DB, Layden JE, Lessler J, Lynfield R, MacCannell D, Madoff LC, Metcalf CJE, Meyers LA, Ofori SK, Quinn C, Bento AI, Reich NG, Riley S, Rosenfeld R, Samore MH, Sampath R, Slayton RB, Swerdlow DL, Truelove S, Varma JK, Grad YH. Infectious disease surveillance needs for the United States: lessons from Covid-19. Front Public Health 2024; 12:1408193. [PMID: 39076420 PMCID: PMC11285106 DOI: 10.3389/fpubh.2024.1408193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Accepted: 06/18/2024] [Indexed: 07/31/2024] Open
Abstract
The COVID-19 pandemic has highlighted the need to upgrade systems for infectious disease surveillance and forecasting and modeling of the spread of infection, both of which inform evidence-based public health guidance and policies. Here, we discuss requirements for an effective surveillance system to support decision making during a pandemic, drawing on the lessons of COVID-19 in the U.S., while looking to jurisdictions in the U.S. and beyond to learn lessons about the value of specific data types. In this report, we define the range of decisions for which surveillance data are required, the data elements needed to inform these decisions and to calibrate inputs and outputs of transmission-dynamic models, and the types of data needed to inform decisions by state, territorial, local, and tribal health authorities. We define actions needed to ensure that such data will be available and consider the contribution of such efforts to improving health equity.
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Affiliation(s)
- Marc Lipsitch
- Center for Forecasting and Outbreak Analytics, US Centers for Disease Control and Prevention, Atlanta, GA, United States
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, United States
| | - Mary T. Bassett
- François-Xavier Bagnoud Center for Health and Human Rights, Department of Social and Behavioral Sciences, Harvard T. H. Chan School of Public Health, Boston, MA, United States
| | - John S. Brownstein
- Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Paul Elliott
- Department of Epidemiology and Public Health Medicine, Imperial College London, London, United Kingdom
| | - David Eyre
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - M. Kate Grabowski
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States
| | - James A. Hay
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Michael A. Johansson
- Division of Vector-Borne Diseases, US Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Stephen M. Kissler
- Department of Computer Science, University of Colorado Boulder, Boulder, CO, United States
| | - Daniel B. Larremore
- Department of Computer Science, University of Colorado Boulder, Boulder, CO, United States
- BioFrontiers Institute, University of Colorado Boulder, Boulder, CO, United States
| | - Jennifer E. Layden
- Office of Public Health Data, Surveillance, and Technology, US Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Justin Lessler
- Department of Epidemiology, UNC Gillings School of Public Health, Chapel Hill, NC, United States
| | - Ruth Lynfield
- Minnesota Department of Health, Minneapolis, MN, United States
| | - Duncan MacCannell
- US Centers for Disease Control and Prevention, Office of Advanced Molecular Detection, Atlanta, GA, United States
| | | | - C. Jessica E. Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, United States
| | - Lauren A. Meyers
- Department of Integrative Biology, University of Texas at Austin, Austin, TX, United States
| | - Sylvia K. Ofori
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, United States
| | - Celia Quinn
- Division of Disease Control, New York City Department of Health and Mental Hygiene, New York City, NY, United States
| | - Ana I. Bento
- Department of Public and Ecosystem Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, United States
| | - Nicholas G. Reich
- Departments of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, United States
| | - Steven Riley
- United Kingdom Health Security Agency, London, United Kingdom
| | - Roni Rosenfeld
- Departments of Computer Science and Computational Biology, Carnegie Melon University, Pittsburgh, PA, United States
| | - Matthew H. Samore
- Division of Epidemiology, Department of Medicine, University of Utah, Salt Lake City, UT, United States
| | | | - Rachel B. Slayton
- Division of Healthcare Quality Promotion, US Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - David L. Swerdlow
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, United States
| | - Shaun Truelove
- Department of Epidemiology, UNC Gillings School of Public Health, Chapel Hill, NC, United States
| | - Jay K. Varma
- SIGA Technologies, New York City, NY, United States
| | - Yonatan H. Grad
- Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA, United States
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3
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Moore M, Zhu Y, Hirsch I, White T, Reiner RC, Barber RM, Pigott D, Collins JK, Santoni S, Sobieszczyk ME, Janes H. Estimating vaccine efficacy during open-label follow-up of COVID-19 vaccine trials based on population-level surveillance data. Epidemics 2024; 47:100768. [PMID: 38643547 PMCID: PMC11257040 DOI: 10.1016/j.epidem.2024.100768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 03/20/2024] [Accepted: 04/11/2024] [Indexed: 04/23/2024] Open
Abstract
While rapid development and roll out of COVID-19 vaccines is necessary in a pandemic, the process limits the ability of clinical trials to assess longer-term vaccine efficacy. We leveraged COVID-19 surveillance data in the U.S. to evaluate vaccine efficacy in U.S. Government-funded COVID-19 vaccine efficacy trials with a three-step estimation process. First, we used a compartmental epidemiological model informed by county-level surveillance data, a "population model", to estimate SARS-CoV-2 incidence among the unvaccinated. Second, a "cohort model" was used to adjust the population SARS-CoV-2 incidence to the vaccine trial cohort, taking into account individual participant characteristics and the difference between SARS-CoV-2 infection and COVID-19 disease. Third, we fit a regression model estimating the offset between the cohort-model-based COVID-19 incidence in the unvaccinated with the placebo-group COVID-19 incidence in the trial during blinded follow-up. Counterfactual placebo COVID-19 incidence was estimated during open-label follow-up by adjusting the cohort-model-based incidence rate by the estimated offset. Vaccine efficacy during open-label follow-up was estimated by contrasting the vaccine group COVID-19 incidence with the counterfactual placebo COVID-19 incidence. We documented good performance of the methodology in a simulation study. We also applied the methodology to estimate vaccine efficacy for the two-dose AZD1222 COVID-19 vaccine using data from the phase 3 U.S. trial (ClinicalTrials.gov # NCT04516746). We estimated AZD1222 vaccine efficacy of 59.1% (95% uncertainty interval (UI): 40.4%-74.3%) in April, 2021 (mean 106 days post-second dose), which reduced to 35.7% (95% UI: 15.0%-51.7%) in July, 2021 (mean 198 days post-second-dose). We developed and evaluated a methodology for estimating longer-term vaccine efficacy. This methodology could be applied to estimating counterfactual placebo incidence for future placebo-controlled vaccine efficacy trials of emerging pathogens with early termination of blinded follow-up, to active-controlled or uncontrolled COVID-19 vaccine efficacy trials, and to other clinical endpoints influenced by vaccination.
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Affiliation(s)
- Mia Moore
- Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, USA.
| | | | - Ian Hirsch
- Biometrics, Vaccines, & Immune Therapies, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Tom White
- Biometrics, Vaccines, & Immune Therapies, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Robert C Reiner
- Institute for Health Metrics and Evaluation within the Schools of Medicine at the University of Washington, Seattle, WA, USA
| | - Ryan M Barber
- Institute for Health Metrics and Evaluation within the Schools of Medicine at the University of Washington, Seattle, WA, USA
| | - David Pigott
- Institute for Health Metrics and Evaluation within the Schools of Medicine at the University of Washington, Seattle, WA, USA
| | - James K Collins
- Institute for Health Metrics and Evaluation within the Schools of Medicine at the University of Washington, Seattle, WA, USA
| | - Serena Santoni
- Institute for Health Metrics and Evaluation within the Schools of Medicine at the University of Washington, Seattle, WA, USA
| | - Magdalena E Sobieszczyk
- Division of Infectious Diseases, Department of Medicine, Vagelos College of Physicians and Surgeons, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Holly Janes
- Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, USA
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4
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Klaassen F, Holm RH, Smith T, Cohen T, Bhatnagar A, Menzies NA. Predictive power of wastewater for nowcasting infectious disease transmission: A retrospective case study of five sewershed areas in Louisville, Kentucky. ENVIRONMENTAL RESEARCH 2024; 240:117395. [PMID: 37838198 PMCID: PMC10863376 DOI: 10.1016/j.envres.2023.117395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 09/29/2023] [Accepted: 10/11/2023] [Indexed: 10/16/2023]
Abstract
BACKGROUND Epidemiological nowcasting traditionally relies on count surveillance data. The availability and quality of such count data may vary over time, limiting representation of true infections. Wastewater data correlates with traditional surveillance data and may provide additional value for nowcasting disease trends. METHODS We obtained SARS-CoV-2 case, death, wastewater, and serosurvey data for Jefferson County, Kentucky (USA), between August 2020 and March 2021, and parameterized an existing nowcasting model using combinations of these data. We assessed the predictive performance and variability at the sewershed level and compared the effects of adding or replacing wastewater data to case and death reports. FINDINGS Adding wastewater data minimally improved the predictive performance of nowcasts compared to a model fitted to case and death data (Weighted Interval Score (WIS) 0.208 versus 0.223), and reduced the predictive performance compared to a model fitted to deaths data (WIS 0.517 versus 0.500). Adding wastewater data to deaths data improved the nowcasts agreement to estimates from models using cases and deaths data. These findings were consistent across individual sewersheds as well as for models fit to the aggregated total data of 5 sewersheds. Retrospective reconstructions of epidemiological dynamics created using different combinations of data were in general agreement (coverage >75%). INTERPRETATION These findings show wastewater data may be valuable for infectious disease nowcasting when clinical surveillance data are absent, such as early in a pandemic or in low-resource settings where systematic collection of epidemiologic data is difficult.
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Affiliation(s)
- Fayette Klaassen
- Department of Global Health and Population, Harvard TH Chan School of Public Health, Boston, MA, USA.
| | - Rochelle H Holm
- Christina Lee Brown Envirome Institute, School of Medicine, University of Louisville, Louisville, KY, USA.
| | - Ted Smith
- Christina Lee Brown Envirome Institute, School of Medicine, University of Louisville, Louisville, KY, USA.
| | - Ted Cohen
- Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA.
| | - Aruni Bhatnagar
- Christina Lee Brown Envirome Institute, School of Medicine, University of Louisville, Louisville, KY, USA.
| | - Nicolas A Menzies
- Department of Global Health and Population, Harvard TH Chan School of Public Health, Boston, MA, USA; Center for Health Decision Science, Harvard TH Chan School of Public Health, Boston, MA, USA.
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5
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Feltham E, Forastiere L, Alexander M, Christakis NA. Mass gatherings for political expression had no discernible association with the local course of the COVID-19 pandemic in the USA in 2020 and 2021. Nat Hum Behav 2023; 7:1708-1728. [PMID: 37524931 DOI: 10.1038/s41562-023-01654-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 06/14/2023] [Indexed: 08/02/2023]
Abstract
Epidemic disease can spread during mass gatherings. We assessed the impact of a type of mass gathering about which comprehensive data were available on the local-area trajectory of the COVID-19 epidemic. Here we examined five types of political event in 2020 and 2021: the US primary elections, the US Senate special election in Georgia, the gubernatorial elections in New Jersey and Virginia, Donald Trump's political rallies and the Black Lives Matter protests. Our study period encompassed over 700 such mass gatherings during multiple phases of the pandemic. We used data from the 48 contiguous states, representing 3,108 counties, and we implemented a novel extension of a recently developed non-parametric, generalized difference-in-difference estimator with a (high-quality) matching procedure for panel data to estimate the average effect of the gatherings on local mortality and other outcomes. There were no statistically significant increases in cases, deaths or a measure of epidemic transmissibility (Rt) in a 40-day period following large-scale political activities. We estimated small and statistically non-significant effects, corresponding to an average difference of -0.0567 deaths (95% CI = -0.319, 0.162) and 8.275 cases (95% CI = -1.383, 20.7) on each day for counties that held mass gatherings for political expression compared to matched control counties. In sum, there is no statistical evidence of a material increase in local COVID-19 deaths, cases or transmissibility after mass gatherings for political expression during the first 2 years of the pandemic in the USA. This may relate to the specific manner in which such activities are typically conducted.
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Affiliation(s)
- Eric Feltham
- Yale Institute for Network Science, Yale University, New Haven, CT, USA.
- Department of Sociology, Yale University, New Haven, CT, USA.
| | - Laura Forastiere
- Yale Institute for Network Science, Yale University, New Haven, CT, USA
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Marcus Alexander
- Yale Institute for Network Science, Yale University, New Haven, CT, USA
- Frank H. Netter MD School of Medicine, Quinnipiac University, North Haven, CT, USA
| | - Nicholas A Christakis
- Yale Institute for Network Science, Yale University, New Haven, CT, USA
- Department of Sociology, Yale University, New Haven, CT, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA
- Department of Medicine, Yale School of Medicine, New Haven, CT, USA
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6
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Earnest R, Hahn AM, Feriancek NM, Brandt M, Filler RB, Zhao Z, Breban MI, Vogels CBF, Chen NFG, Koch RT, Porzucek AJ, Sodeinde A, Garbiel A, Keanna C, Litwak H, Stuber HR, Cantoni JL, Pitzer VE, Olarte Castillo XA, Goodman LB, Wilen CB, Linske MA, Williams SC, Grubaugh ND. Survey of white-footed mice in Connecticut, USA reveals low SARS-CoV-2 seroprevalence and infection with divergent betacoronaviruses. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.22.559030. [PMID: 37808797 PMCID: PMC10557615 DOI: 10.1101/2023.09.22.559030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Diverse mammalian species display susceptibility to and infection with SARS-CoV-2. Potential SARS-CoV-2 spillback into rodents is understudied despite their host role for numerous zoonoses and human proximity. We assessed exposure and infection among white-footed mice (Peromyscus leucopus) in Connecticut, USA. We observed 1% (6/540) wild-type neutralizing antibody seroprevalence among 2020-2022 residential mice with no cross-neutralization of variants. We detected no SARS-CoV-2 infections via RT-qPCR, but identified non-SARS-CoV-2 betacoronavirus infections via pan-coronavirus PCR among 1% (5/468) of residential mice. Sequencing revealed two divergent betacoronaviruses, preliminarily named Peromyscus coronavirus-1 and -2. Both belong to the Betacoronavirus 1 species and are ~90% identical to the closest known relative, Porcine hemagglutinating encephalomyelitis virus. Low SARS-CoV-2 seroprevalence suggests white-footed mice may not be sufficiently susceptible or exposed to SARS-CoV-2 to present a long-term human health risk. However, the discovery of divergent, non-SARS-CoV-2 betacoronaviruses expands the diversity of known rodent coronaviruses and further investigation is required to understand their transmission extent.
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Affiliation(s)
- Rebecca Earnest
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Anne M Hahn
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Nicole M Feriancek
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Matthew Brandt
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Renata B Filler
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT 06520, USA
- Department of Immunobiology, Yale School of Medicine, New Haven, CT 06520, USA
| | - Zhe Zhao
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT 06520, USA
- Department of Immunobiology, Yale School of Medicine, New Haven, CT 06520, USA
| | - Mallery I Breban
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Chantal B F Vogels
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Nicholas F G Chen
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Robert T Koch
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Abbey J Porzucek
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Afeez Sodeinde
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Alexa Garbiel
- Department of Environmental Science and Forestry, The Connecticut Agricultural Experiment Station, New Haven, CT 06511, USA
| | - Claire Keanna
- Department of Environmental Science and Forestry, The Connecticut Agricultural Experiment Station, New Haven, CT 06511, USA
| | - Hannah Litwak
- Department of Environmental Science and Forestry, The Connecticut Agricultural Experiment Station, New Haven, CT 06511, USA
| | - Heidi R Stuber
- Department of Entomology, The Connecticut Agricultural Experiment Station, New Haven, CT 06511, USA
| | - Jamie L Cantoni
- Department of Entomology, The Connecticut Agricultural Experiment Station, New Haven, CT 06511, USA
| | - Virginia E Pitzer
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Ximena A Olarte Castillo
- Department of Microbiology and Immunology, Cornell University College of Veterinary Medicine, Ithaca, NY 14853
| | - Laura B Goodman
- Department of Public & Ecosystem Health, Cornell University College of Veterinary Medicine, Ithaca, NY 14853
| | - Craig B Wilen
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT 06520, USA
- Department of Immunobiology, Yale School of Medicine, New Haven, CT 06520, USA
| | - Megan A Linske
- Department of Entomology, The Connecticut Agricultural Experiment Station, New Haven, CT 06511, USA
| | - Scott C Williams
- Department of Environmental Science and Forestry, The Connecticut Agricultural Experiment Station, New Haven, CT 06511, USA
| | - Nathan D Grubaugh
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06510, USA
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7
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Klaassen F, Chitwood MH, Cohen T, Pitzer VE, Russi M, Swartwood NA, Salomon JA, Menzies NA. Changes in Population Immunity Against Infection and Severe Disease From Severe Acute Respiratory Syndrome Coronavirus 2 Omicron Variants in the United States Between December 2021 and November 2022. Clin Infect Dis 2023; 77:355-361. [PMID: 37074868 PMCID: PMC10425195 DOI: 10.1093/cid/ciad210] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 03/24/2023] [Accepted: 04/04/2023] [Indexed: 04/20/2023] Open
Abstract
BACKGROUND Although a substantial fraction of the US population was infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during December 2021-February 2022, the subsequent evolution of population immunity reflects the competing influences of waning protection over time and acquisition or restoration of immunity through additional infections and vaccinations. METHODS Using a Bayesian evidence synthesis model of reported coronavirus disease 2019 (COVID-19) data (diagnoses, hospitalizations), vaccinations, and waning patterns for vaccine- and infection-acquired immunity, we estimate population immunity against infection and severe disease from SARS-CoV-2 Omicron variants in the United States, by location (national, state, county) and week. RESULTS By 9 November 2022, 97% (95%-99%) of the US population were estimated to have prior immunological exposure to SARS-CoV-2. Between 1 December 2021 and 9 November 2022, protection against a new Omicron infection rose from 22% (21%-23%) to 63% (51%-75%) nationally, and protection against an Omicron infection leading to severe disease increased from 61% (59%-64%) to 89% (83%-92%). Increasing first booster uptake to 55% in all states (current US coverage: 34%) and second booster uptake to 22% (current US coverage: 11%) would increase protection against infection by 4.5 percentage points (2.4-7.2) and protection against severe disease by 1.1 percentage points (1.0-1.5). CONCLUSIONS Effective protection against SARS-CoV-2 infection and severe disease in November 2022 was substantially higher than in December 2021. Despite this high level of protection, a more transmissible or immune evading (sub)variant, changes in behavior, or ongoing waning of immunity could lead to a new SARS-CoV-2 wave.
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Affiliation(s)
- Fayette Klaassen
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Melanie H Chitwood
- Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven, Connecticut, USA
| | - Ted Cohen
- Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven, Connecticut, USA
| | - Virginia E Pitzer
- Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven, Connecticut, USA
| | - Marcus Russi
- Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven, Connecticut, USA
| | - Nicole A Swartwood
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Joshua A Salomon
- Department of Health Policy, Stanford University School of Medicine, Stanford, California, USA
| | - Nicolas A Menzies
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
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8
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García-Carreras B, Hitchings MDT, Johansson MA, Biggerstaff M, Slayton RB, Healy JM, Lessler J, Quandelacy T, Salje H, Huang AT, Cummings DAT. Accounting for assay performance when estimating the temporal dynamics in SARS-CoV-2 seroprevalence in the U.S. Nat Commun 2023; 14:2235. [PMID: 37076502 PMCID: PMC10115837 DOI: 10.1038/s41467-023-37944-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 04/06/2023] [Indexed: 04/21/2023] Open
Abstract
Reconstructing the incidence of SARS-CoV-2 infection is central to understanding the state of the pandemic. Seroprevalence studies are often used to assess cumulative infections as they can identify asymptomatic infection. Since July 2020, commercial laboratories have conducted nationwide serosurveys for the U.S. CDC. They employed three assays, with different sensitivities and specificities, potentially introducing biases in seroprevalence estimates. Using models, we show that accounting for assays explains some of the observed state-to-state variation in seroprevalence, and when integrating case and death surveillance data, we show that when using the Abbott assay, estimates of proportions infected can differ substantially from seroprevalence estimates. We also found that states with higher proportions infected (before or after vaccination) had lower vaccination coverages, a pattern corroborated using a separate dataset. Finally, to understand vaccination rates relative to the increase in cases, we estimated the proportions of the population that received a vaccine prior to infection.
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Affiliation(s)
- Bernardo García-Carreras
- Department of Biology, University of Florida, Gainesville, FL, USA.
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA.
| | - Matt D T Hitchings
- Department of Biostatistics, University of Florida, Gainesville, FL, USA
| | - Michael A Johansson
- COVID-19 Response, US Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Matthew Biggerstaff
- COVID-19 Response, US Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Rachel B Slayton
- COVID-19 Response, US Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Jessica M Healy
- COVID-19 Response, US Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Justin Lessler
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Carolina Population Center, Chapel Hill, NC, USA
| | | | - Henrik Salje
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Angkana T Huang
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Derek A T Cummings
- Department of Biology, University of Florida, Gainesville, FL, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
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9
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Tracking Covid-19 cases and deaths in the United States: metrics of pandemic progression derived from a queueing framework. Health Care Manag Sci 2023; 26:79-92. [PMID: 36282367 PMCID: PMC9592548 DOI: 10.1007/s10729-022-09619-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 09/26/2022] [Indexed: 11/04/2022]
Abstract
We analyze the progression of COVID-19 in the United States over a nearly one-year period beginning March 1, 2020 with a novel metric motivated by queueing models, tracking partial-average day-of-event and cumulative probability distributions for events, where events are points in time when new cases and new deaths are reported. The partial average represents the average day of all events preceding a point of time, and is an indicator as to whether the pandemic is accelerating or decelerating in the context of the entire history of the pandemic. The measure supplements traditional metrics, and also enables direct comparisons of case and death histories on a common scale. We also compare methods for estimating actual infections and deaths to assess the timing and dynamics of the pandemic by location. Three example states are graphically compared as functions of date, as well as Hong Kong as an example that experienced a pronounced recent wave of the pandemic. In addition, statistics are compared for all 50 states. Over the period studied, average case day and average death day varied by two to five months among the 50 states, depending on data source, with the earliest averages in New York and surrounding states, as well as Louisiana.
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10
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Klaassen F, Chitwood MH, Cohen T, Pitzer VE, Russi M, Swartwood NA, Salomon JA, Menzies NA. Changes in population immunity against infection and severe disease from SARS-CoV-2 Omicron variants in the United States between December 2021 and November 2022. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.11.19.22282525. [PMID: 36451882 PMCID: PMC9709792 DOI: 10.1101/2022.11.19.22282525] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/28/2023]
Abstract
Importance While a substantial fraction of the US population was infected with SARS-CoV-2 during December 2021 - February 2022, the subsequent evolution of population immunity against SARS-CoV-2 Omicron variants reflects the competing influences of waning protection over time and acquisition or restoration of immunity through additional infections and vaccinations. Objective To estimate changes in population immunity against infection and severe disease due to circulating SARS-CoV-2 Omicron variants in the United States from December 2021 to November 2022, and to quantify the protection against a potential 2022-2023 winter SARS-CoV-2 wave. Design setting participants Bayesian evidence synthesis of reported COVID-19 data (diagnoses, hospitalizations), vaccinations, and waning patterns for vaccine- and infection-acquired immunity, using a mathematical model of COVID-19 natural history. Main Outcomes and Measures Population immunity against infection and severe disease from SARS-CoV-2 Omicron variants in the United States, by location (national, state, county) and week. Results By November 9, 2022, 94% (95% CrI, 79%-99%) of the US population were estimated to have been infected by SARS-CoV-2 at least once. Combined with vaccination, 97% (95%-99%) were estimated to have some prior immunological exposure to SARS-CoV-2. Between December 1, 2021 and November 9, 2022, protection against a new Omicron infection rose from 22% (21%-23%) to 63% (51%-75%) nationally, and protection against an Omicron infection leading to severe disease increased from 61% (59%-64%) to 89% (83%-92%). Increasing first booster uptake to 55% in all states (current US coverage: 34%) and second booster uptake to 22% (current US coverage: 11%) would increase protection against infection by 4.5 percentage points (2.4-7.2) and protection against severe disease by 1.1 percentage points (1.0-1.5). Conclusions and Relevance Effective protection against SARS-CoV-2 infection and severe disease in November 2022 was substantially higher than in December 2021. Despite this high level of protection, a more transmissible or immune evading (sub)variant, changes in behavior, or ongoing waning of immunity could lead to a new SARS-CoV-2 wave. Key points Question: How did population immunity against SARS-CoV-2 infection and subsequent severe disease change between December 2021, and November 2022?Findings: On November 9, 2022, the protection against a SARS-CoV-2 infection with the Omicron variant was estimated to be 63% (51%-75%) in the US, and the protection against severe disease was 89% (83%-92%).Meaning: As most of the newly acquired immunity has been accumulated in the December 2021-February 2022 Omicron wave, risk of reinfection and subsequent severe disease remains present at the beginning of the 2022-2023 winter, despite high levels of protection.
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Affiliation(s)
- Fayette Klaassen
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston MA
| | - Melanie H Chitwood
- Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven CT
| | - Ted Cohen
- Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven CT
| | - Virginia E Pitzer
- Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven CT
| | - Marcus Russi
- Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven CT
| | - Nicole A Swartwood
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston MA
| | - Joshua A Salomon
- Department of Health Policy, Stanford University School of Medicine, Stanford CA
| | - Nicolas A Menzies
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston MA
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11
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Crawford FW, Jones SA, Cartter M, Dean SG, Warren JL, Li ZR, Barbieri J, Campbell J, Kenney P, Valleau T, Morozova O. Impact of close interpersonal contact on COVID-19 incidence: Evidence from 1 year of mobile device data. SCIENCE ADVANCES 2022; 8:eabi5499. [PMID: 34995121 PMCID: PMC8741180 DOI: 10.1126/sciadv.abi5499] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 11/17/2021] [Indexed: 05/06/2023]
Abstract
Close contact between people is the primary route for transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19). We quantified interpersonal contact at the population level using mobile device geolocation data. We computed the frequency of contact (within 6 feet) between people in Connecticut during February 2020 to January 2021 and aggregated counts of contact events by area of residence. When incorporated into a SEIR-type model of COVID-19 transmission, the contact rate accurately predicted COVID-19 cases in Connecticut towns. Contact in Connecticut explains the initial wave of infections during March to April, the drop in cases during June to August, local outbreaks during August to September, broad statewide resurgence during September to December, and decline in January 2021. The transmission model fits COVID-19 transmission dynamics better using the contact rate than other mobility metrics. Contact rate data can help guide social distancing and testing resource allocation.
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Affiliation(s)
- Forrest W. Crawford
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA
- Yale School of Management, New Haven, CT, USA
| | - Sydney A. Jones
- Epidemic Intelligence Service, Centers for Disease Control and Prevention, Atlanta, GA, USA
- Infectious Diseases Section, Connecticut Department of Public Health, Hartford, CT, USA
| | - Matthew Cartter
- Infectious Diseases Section, Connecticut Department of Public Health, Hartford, CT, USA
| | - Samantha G. Dean
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Joshua L. Warren
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Zehang Richard Li
- Department of Statistics, University of California, Santa Cruz, Santa Cruz, CA, USA
| | | | | | | | | | - Olga Morozova
- Program in Public Health and Department of Family, Population and Preventive Medicine, Stony Brook University, Stony Brook, NY, USA
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