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Merling MR, Williams A, Mahfooz NS, Ruane-Foster M, Smith J, Jahnes J, Ayers LW, Bazan JA, Norris A, Norris Turner A, Oglesbee M, Faith SA, Quam MB, Robinson RT. The emergence of SARS-CoV-2 lineages and associated saliva antibody responses among asymptomatic individuals in a large university community. PLoS Pathog 2023; 19:e1011596. [PMID: 37603565 PMCID: PMC10470930 DOI: 10.1371/journal.ppat.1011596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 08/31/2023] [Accepted: 08/02/2023] [Indexed: 08/23/2023] Open
Abstract
SARS-CoV-2 (CoV2) infected, asymptomatic individuals are an important contributor to COVID transmission. CoV2-specific immunoglobulin (Ig)-as generated by the immune system following infection or vaccination-has helped limit CoV2 transmission from asymptomatic individuals to susceptible populations (e.g. elderly). Here, we describe the relationships between COVID incidence and CoV2 lineage, viral load, saliva Ig levels (CoV2-specific IgM, IgA and IgG), and ACE2 binding inhibition capacity in asymptomatic individuals between January 2021 and May 2022. These data were generated as part of a large university COVID monitoring program in Ohio, United States of America, and demonstrate that COVID incidence among asymptomatic individuals occurred in waves which mirrored those in surrounding regions, with saliva CoV2 viral loads becoming progressively higher in our community until vaccine mandates were established. Among the unvaccinated, infection with each CoV2 lineage (pre-Omicron) resulted in saliva Spike-specific IgM, IgA, and IgG responses, the latter increasing significantly post-infection and being more pronounced than N-specific IgG responses. Vaccination resulted in significantly higher Spike-specific IgG levels compared to unvaccinated infected individuals, and uninfected vaccinees' saliva was more capable of inhibiting Spike function. Vaccinees with breakthrough Delta infections had Spike-specific IgG levels comparable to those of uninfected vaccinees; however, their ability to inhibit Spike binding was diminished. These data are consistent with COVID vaccines having achieved hoped-for effects in our community, including the generation of mucosal antibodies that inhibit Spike and lower community viral loads, and suggest breakthrough Delta infections were not due to an absence of vaccine-elicited Ig, but instead limited Spike binding activity in the face of high community viral loads.
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Affiliation(s)
- Marlena R. Merling
- Department of Microbial Infection & Immunity, The Ohio State University, Columbus, Ohio, United States of America
| | - Amanda Williams
- Department of Microbial Infection & Immunity, The Ohio State University, Columbus, Ohio, United States of America
- Infectious Disease Institute, The Ohio State University, Columbus, Ohio, United States of America
| | - Najmus S. Mahfooz
- Department of Microbial Infection & Immunity, The Ohio State University, Columbus, Ohio, United States of America
| | - Marisa Ruane-Foster
- Department of Microbial Infection & Immunity, The Ohio State University, Columbus, Ohio, United States of America
| | - Jacob Smith
- Infectious Disease Institute, The Ohio State University, Columbus, Ohio, United States of America
| | - Jeff Jahnes
- Infectious Disease Institute, The Ohio State University, Columbus, Ohio, United States of America
| | - Leona W. Ayers
- Department of Pathology, The Ohio State University, Columbus, Ohio, United States of America
| | - Jose A. Bazan
- Division of Infectious Disease, Department of Internal Medicine, The Ohio State University, Columbus, Ohio, United States of America
| | - Alison Norris
- Division of Infectious Disease, Department of Internal Medicine, The Ohio State University, Columbus, Ohio, United States of America
- Department of Epidemiology, The Ohio State University, Columbus, Ohio, United States of America
| | - Abigail Norris Turner
- Division of Infectious Disease, Department of Internal Medicine, The Ohio State University, Columbus, Ohio, United States of America
| | - Michael Oglesbee
- Infectious Disease Institute, The Ohio State University, Columbus, Ohio, United States of America
| | - Seth A. Faith
- Infectious Disease Institute, The Ohio State University, Columbus, Ohio, United States of America
| | - Mikkel B. Quam
- Department of Epidemiology, The Ohio State University, Columbus, Ohio, United States of America
| | - Richard T. Robinson
- Department of Microbial Infection & Immunity, The Ohio State University, Columbus, Ohio, United States of America
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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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3
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Gao H, Chen YJ, Xu XQ, Xu ZY, Xu SJ, Xing JB, Liu J, Zha YF, Sun YK, Zhang GH. Comprehensive phylogeographic and phylodynamic analyses of global Senecavirus A. Front Microbiol 2022; 13:980862. [PMID: 36246286 PMCID: PMC9557172 DOI: 10.3389/fmicb.2022.980862] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 08/15/2022] [Indexed: 11/13/2022] Open
Abstract
Senecavirus A (SVA) is a member of the genus Senecavirus in the family Picornaviridae that infects pigs and shows symptoms similar to foot and mouth diseases and other vesicular diseases. It is difficult to prevent, thus, causing tremendous economic loss to the pig industry. However, the global transmission routes of SVA and its natural origins remain unclear. In this study, we processed representative SVA sequences from the GenBank database along with 10 newly isolated SVA strains from the field samples collected from our lab to explore the origins, population characteristics, and transmission patterns of SVA. The SVA strains were firstly systematically divided into eight clades including Clade I–VII and Clade Ancestor based on the maximum likelihood phylogenetic inference. Phylogeographic and phylodynamics analysis within the Bayesian statistical framework revealed that SVA originated in the United States in the 1980s and afterward spread to different countries and regions. Our analysis of viral transmission routes also revealed its historical spread from the United States and the risk of the global virus prevalence. Overall, our study provided a comprehensive assessment of the phylogenetic characteristics, origins, history, and geographical evolution of SVA on a global scale, unlocking insights into developing efficient disease management strategies.
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Affiliation(s)
- Han Gao
- Guangdong Provincial Key Laboratory of Zoonosis Prevention and Control, College of Veterinary Medicine, South China Agricultural University, Guangzhou, China
- Maoming Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China
- Key Laboratory of Animal Vaccine Development, Ministry of Agriculture and Rural Affairs, Guangzhou, China
- National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, China
| | - Yong-jie Chen
- Guangdong Provincial Key Laboratory of Zoonosis Prevention and Control, College of Veterinary Medicine, South China Agricultural University, Guangzhou, China
- Maoming Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China
- Key Laboratory of Animal Vaccine Development, Ministry of Agriculture and Rural Affairs, Guangzhou, China
- National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, China
| | - Xiu-qiong Xu
- Guangdong Animal Health and Quarantine Office, Guangdong Animal Disease Prevention and Control Center, Guangzhou, China
| | - Zhi-ying Xu
- Guangdong Provincial Key Laboratory of Zoonosis Prevention and Control, College of Veterinary Medicine, South China Agricultural University, Guangzhou, China
- Maoming Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China
- Key Laboratory of Animal Vaccine Development, Ministry of Agriculture and Rural Affairs, Guangzhou, China
- National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, China
| | - Si-jia Xu
- Guangdong Provincial Key Laboratory of Zoonosis Prevention and Control, College of Veterinary Medicine, South China Agricultural University, Guangzhou, China
- Maoming Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China
- Key Laboratory of Animal Vaccine Development, Ministry of Agriculture and Rural Affairs, Guangzhou, China
- National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, China
| | - Jia-bao Xing
- Guangdong Provincial Key Laboratory of Zoonosis Prevention and Control, College of Veterinary Medicine, South China Agricultural University, Guangzhou, China
- Maoming Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China
- Key Laboratory of Animal Vaccine Development, Ministry of Agriculture and Rural Affairs, Guangzhou, China
- National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, China
| | - Jing Liu
- Guangdong Provincial Key Laboratory of Zoonosis Prevention and Control, College of Veterinary Medicine, South China Agricultural University, Guangzhou, China
- Maoming Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China
- Key Laboratory of Animal Vaccine Development, Ministry of Agriculture and Rural Affairs, Guangzhou, China
- National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, China
| | - Yun-feng Zha
- Guangdong Animal Health and Quarantine Office, Guangdong Animal Disease Prevention and Control Center, Guangzhou, China
| | - Yan-kuo Sun
- Guangdong Provincial Key Laboratory of Zoonosis Prevention and Control, College of Veterinary Medicine, South China Agricultural University, Guangzhou, China
- Maoming Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China
- Key Laboratory of Animal Vaccine Development, Ministry of Agriculture and Rural Affairs, Guangzhou, China
- National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, China
- *Correspondence: Yan-kuo Sun,
| | - Gui-hong Zhang
- Guangdong Provincial Key Laboratory of Zoonosis Prevention and Control, College of Veterinary Medicine, South China Agricultural University, Guangzhou, China
- Maoming Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China
- Key Laboratory of Animal Vaccine Development, Ministry of Agriculture and Rural Affairs, Guangzhou, China
- National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, China
- Gui-hong Zhang,
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Seroprevalence of SARS-CoV-2 on health professionals via Bayesian estimation: a Brazilian case study before and after vaccines. Acta Trop 2022; 233:106551. [PMID: 35691330 PMCID: PMC9181309 DOI: 10.1016/j.actatropica.2022.106551] [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: 02/28/2022] [Revised: 06/01/2022] [Accepted: 06/03/2022] [Indexed: 11/20/2022]
Abstract
The increasing number of COVID-19 infections brought by the current pandemic has encouraged the scientific community to analyze the seroprevalence in populations to support health policies. In this context, accurate estimations of SARS-CoV-2 antibodies based on antibody tests metrics (e.g., specificity and sensitivity) and the study of population characteristics are essential. Here, we propose a Bayesian analysis using IgA and IgG antibody levels through multiple scenarios regarding data availability from different information sources to estimate the seroprevalence of health professionals in a Northeastern Brazilian city: no data available, data only related to the test performance, data from other regions. The study population comprises 432 subjects with more than 620 collections analyzed via IgA/IgG ELISA tests. We conducted the study in pre- and post-vaccination campaigns started in Brazil. We discuss the importance of aggregating available data from various sources to create informative prior knowledge. Considering prior information from the USA and Europe, the pre-vaccine seroprevalence means are 8.04% and 10.09% for IgG and 7.40% and 9.11% for IgA. For the post-vaccination campaign and considering local informative prior, the median is 84.83% for IgG, which confirms a sharp increase in the seroprevalence after vaccination. Additionally, stratification considering differences in sex, age (younger than 30 years, between 30 and 49 years, and older than 49 years), and presence of comorbidities are provided for all scenarios.
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Turner AN, Kline D, Norris A, Phillips WG, Root E, Wakefield J, Li ZR, Lemeshow S, Spahnie M, Luff A, Chu Y, Francis MK, Gallo M, Chakraborty P, Lindstrom M, Lozanski G, Miller W, Clark S. Prevalence of current and past COVID-19 in Ohio adults. Ann Epidemiol 2021; 67:50-60. [PMID: 34921991 DOI: 10.1016/j.annepidem.2021.11.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 11/22/2021] [Accepted: 11/27/2021] [Indexed: 11/01/2022]
Abstract
PURPOSE To estimate the prevalence of current and past COVID-19 in Ohio adults. METHODS We used stratified, probability-proportionate-to-size cluster sampling. During July 2020, we enrolled 727 randomly-sampled adult English- and Spanish-speaking participants through a household survey. Participants provided nasopharyngeal swabs and blood samples to detect current and past COVID-19. We used Bayesian latent class models with multilevel regression and poststratification to calculate the adjusted prevalence of current and past COVID-19. We accounted for the potential effects of non-ignorable non-response bias. RESULTS The estimated statewide prevalence of current COVID-19 was 0.9% (95% credible interval: 0.1-2.0%), corresponding to ∼85,000 prevalent infections (95% credible interval: 6,300-177,000) in Ohio adults during the study period. The estimated statewide prevalence of past COVID-19 was 1.3% (95% credible interval: 0.2-2.7%), corresponding to ∼118,000 Ohio adults (95% credible interval: 22,000-240,000). Estimates did not change meaningfully due to non-response bias. CONCLUSIONS Total COVID-19 cases in Ohio in July 2020 were approximately 3.5 times as high as diagnosed cases. The lack of broad COVID-19 screening in the United States early in the pandemic resulted in a paucity of population-representative prevalence data, limiting the ability to measure the effects of statewide control efforts.
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Affiliation(s)
| | - David Kline
- Department of Biostatistics and Data Science, Division of Public Health Sciences, School of Medicine, Wake Forest University
| | - Alison Norris
- Division of Infectious Diseases, College of Medicine, Ohio State University; Division of Epidemiology, College of Medicine, Ohio State University
| | | | - Elisabeth Root
- Division of Epidemiology, College of Medicine, Ohio State University; Institute for Disease Modeling, The Bill and Melinda Gates Foundation
| | | | | | - Stanley Lemeshow
- Division of Biostatistics, College of Public Health, Ohio State University
| | - Morgan Spahnie
- Division of Epidemiology, College of Medicine, Ohio State University
| | - Amanda Luff
- Division of Epidemiology, College of Medicine, Ohio State University
| | - Yue Chu
- Department of Sociology, College of Arts and Sciences, Ohio State University
| | | | - Maria Gallo
- Division of Epidemiology, College of Medicine, Ohio State University
| | - Payal Chakraborty
- Division of Epidemiology, College of Medicine, Ohio State University
| | - Megan Lindstrom
- Institute for Disease Modeling, The Bill and Melinda Gates Foundation
| | - Gerard Lozanski
- Department of Pathology, College of Medicine, Ohio State University
| | - William Miller
- Division of Epidemiology, College of Medicine, Ohio State University
| | - Samuel Clark
- Department of Sociology, College of Arts and Sciences, Ohio State University; MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
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6
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Irons NJ, Raftery AE. Estimating SARS-CoV-2 infections from deaths, confirmed cases, tests, and random surveys. Proc Natl Acad Sci U S A 2021; 118:e2103272118. [PMID: 34312227 PMCID: PMC8346866 DOI: 10.1073/pnas.2103272118] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
There are multiple sources of data giving information about the number of SARS-CoV-2 infections in the population, but all have major drawbacks, including biases and delayed reporting. For example, the number of confirmed cases largely underestimates the number of infections, and deaths lag infections substantially, while test positivity rates tend to greatly overestimate prevalence. Representative random prevalence surveys, the only putatively unbiased source, are sparse in time and space, and the results can come with big delays. Reliable estimates of population prevalence are necessary for understanding the spread of the virus and the effectiveness of mitigation strategies. We develop a simple Bayesian framework to estimate viral prevalence by combining several of the main available data sources. It is based on a discrete-time Susceptible-Infected-Removed (SIR) model with time-varying reproductive parameter. Our model includes likelihood components that incorporate data on deaths due to the virus, confirmed cases, and the number of tests administered on each day. We anchor our inference with data from random-sample testing surveys in Indiana and Ohio. We use the results from these two states to calibrate the model on positive test counts and proceed to estimate the infection fatality rate and the number of new infections on each day in each state in the United States. We estimate the extent to which reported COVID cases have underestimated true infection counts, which was large, especially in the first months of the pandemic. We explore the implications of our results for progress toward herd immunity.
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Affiliation(s)
- Nicholas J Irons
- Department of Statistics, University of Washington, Seattle, WA 98195
| | - Adrian E Raftery
- Department of Statistics, University of Washington, Seattle, WA 98195;
- Department of Sociology, University of Washington, Seattle, WA 98195
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7
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Davis G, York AJ, Bacon WC, Lin SC, McNeal MM, Yarawsky AE, Maciag JJ, Miller JLC, Locker KCS, Bailey M, Stone R, Hall M, Gonzalez J, Sproles A, Woodle ES, Safier K, Justus KA, Spearman P, Ware RE, Cancelas JA, Jordan MB, Herr AB, Hildeman DA, Molkentin JD. Seroprevalence of SARS-CoV-2 infection in Cincinnati Ohio USA from August to December 2020. PLoS One 2021; 16:e0254667. [PMID: 34260645 PMCID: PMC8279307 DOI: 10.1371/journal.pone.0254667] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 06/30/2021] [Indexed: 11/24/2022] Open
Abstract
The world is currently in a pandemic of COVID-19 (Coronavirus disease-2019) caused by a novel positive-sense, single-stranded RNA β-coronavirus referred to as SARS-CoV-2. Here we investigated rates of SARS-CoV-2 infection in the greater Cincinnati, Ohio, USA metropolitan area from August 13 to December 8, 2020, just prior to initiation of the national vaccination program. Examination of 9,550 adult blood donor volunteers for serum IgG antibody positivity against the SARS-CoV-2 Spike protein showed an overall prevalence of 8.40%, measured as 7.56% in the first 58 days and 9.24% in the last 58 days, and 12.86% in December 2020, which we extrapolated to ~20% as of March, 2021. Males and females showed similar rates of past infection, and rates among Hispanic or Latinos, African Americans and Whites were also investigated. Donors under 30 years of age had the highest rates of past infection, while those over 60 had the lowest. Geographic analysis showed higher rates of infectivity on the West side of Cincinnati compared with the East side (split by I-75) and the lowest rates in the adjoining region of Kentucky (across the Ohio river). These results in regional seroprevalence will help inform efforts to best achieve herd immunity in conjunction with the national vaccination campaign.
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Affiliation(s)
- Greg Davis
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, United State of America
| | - Allen J. York
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, United State of America
| | - Willis Clark Bacon
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, United State of America
| | - Suh-Chin Lin
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, United State of America
| | - Monica Malone McNeal
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, United State of America
| | - Alexander E. Yarawsky
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, United State of America
| | - Joseph J. Maciag
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, United State of America
| | - Jeanette L. C. Miller
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, United State of America
| | - Kathryn C. S. Locker
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, United State of America
| | - Michelle Bailey
- Hoxworth Blood Center, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
| | - Rebecca Stone
- Hoxworth Blood Center, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
| | - Michael Hall
- Hoxworth Blood Center, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
| | - Judith Gonzalez
- Hoxworth Blood Center, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
| | - Alyssa Sproles
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, United State of America
| | - E. Steve Woodle
- Department of Surgery, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
| | - Kristen Safier
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, United State of America
| | - Kristine A. Justus
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, United State of America
| | - Paul Spearman
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, United State of America
| | - Russell E. Ware
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, United State of America
| | - Jose A. Cancelas
- Hoxworth Blood Center, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
| | - Michael B. Jordan
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, United State of America
| | - Andrew B. Herr
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, United State of America
| | - David A. Hildeman
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, United State of America
| | - Jeffery D. Molkentin
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, United State of America
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8
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Clark SJ, Turner AN. Monitoring epidemics: Lessons from measuring population prevalence of the coronavirus. Proc Natl Acad Sci U S A 2021; 118:e2026412118. [PMID: 33627409 PMCID: PMC7936272 DOI: 10.1073/pnas.2026412118] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Affiliation(s)
- Samuel J Clark
- Department of Sociology, The Ohio State University, Columbus, OH 43210;
| | - Abigail Norris Turner
- Division of Infectious Diseases, College of Medicine, The Ohio State University, Columbus, OH 43210
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