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Coelho LE, Luz PM, Pires DC, Jalil EM, Perazzo H, Torres TS, Cardoso SW, Peixoto EM, Nazer S, Massad E, Carvalho LM, Réquia WJ, Motta FC, Siqueira MM, Vasconcelos AT, da Fonseca GC, Cavalcante LT, Costa CA, Amancio RT, Villela DA, Pereira T, Goedert GT, Santos CV, Rodrigues NC, Bormann de Souza Filho BA, Csillag D, Grinsztejn B, Veloso VG, Struchiner CJ. SARS-CoV-2 transmission in a highly vulnerable population of Brazil: a household cohort study. LANCET REGIONAL HEALTH. AMERICAS 2024; 36:100824. [PMID: 38993539 PMCID: PMC11237915 DOI: 10.1016/j.lana.2024.100824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 06/01/2024] [Accepted: 06/07/2024] [Indexed: 07/13/2024]
Abstract
Background Household transmission studies seek to understand the transmission dynamics of a pathogen by estimating the risk of infection from household contacts and community exposures. We estimated within/extra-household SARS-CoV-2 infection risk and associated factors in a household cohort study in one of the most vulnerable neighbourhoods in Rio de Janeiro city. Methods Individuals ≥1 years-old with suspected or confirmed COVID-19 in the past 30 days (index cases) and household members aged ≥1 year were enrolled and followed at 14 and 28 days (study period November/2020-December/2021). RT-PCR testing, COVID-19 symptoms, and SARS-CoV-2 serologies were ascertained in all visits. Chain binomial household transmission models were fitted using data from 2024 individuals (593 households). Findings Extra-household infection risk was 74.2% (95% credible interval [CrI] 70.3-77.8), while within-household infection risk was 11.4% (95% CrI 5.7-17.2). Participants reporting having received two doses of a COVID-19 vaccine had lower extra-household (68.9%, 95% CrI 57.3-77.6) and within-household (4.1%, 95% CrI 0.4-16.6) infection risk. Within-household infection risk was higher among participants aged 10-19 years, from overcrowded households, and with low family income. Contrastingly, extra-household infection risk was higher among participants aged 20-29 years, unemployed, and public transportation users. Interpretation Our study provides important insights into COVID-19 household/community transmission in a vulnerable population that resided in overcrowded households and who struggled to adhere to lockdown policies and social distancing measures. The high extra-household infection risk highlights the extreme social vulnerability of this population. Prioritising vaccination of the most socially vulnerable could protect these individuals and reduce widespread community transmission. Funding Fundação Oswaldo Cruz, CNPq, FAPERJ, Royal Society, Instituto Serrapilheira, FAPESP.
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Affiliation(s)
- Lara E. Coelho
- Instituto Nacional de Infectologia Evandro Chagas, FIOCRUZ, Rio de Janeiro, Brazil
| | - Paula M. Luz
- Instituto Nacional de Infectologia Evandro Chagas, FIOCRUZ, Rio de Janeiro, Brazil
| | - Débora C. Pires
- Instituto Nacional de Infectologia Evandro Chagas, FIOCRUZ, Rio de Janeiro, Brazil
| | - Emilia M. Jalil
- Instituto Nacional de Infectologia Evandro Chagas, FIOCRUZ, Rio de Janeiro, Brazil
| | - Hugo Perazzo
- Instituto Nacional de Infectologia Evandro Chagas, FIOCRUZ, Rio de Janeiro, Brazil
| | - Thiago S. Torres
- Instituto Nacional de Infectologia Evandro Chagas, FIOCRUZ, Rio de Janeiro, Brazil
| | - Sandra W. Cardoso
- Instituto Nacional de Infectologia Evandro Chagas, FIOCRUZ, Rio de Janeiro, Brazil
| | - Eduardo M. Peixoto
- Instituto Nacional de Infectologia Evandro Chagas, FIOCRUZ, Rio de Janeiro, Brazil
| | - Sandro Nazer
- Instituto Nacional de Infectologia Evandro Chagas, FIOCRUZ, Rio de Janeiro, Brazil
| | - Eduardo Massad
- Escola de Matemática Aplicada, Fundação Getulio Vargas, Rio de Janeiro, Brazil
| | - Luiz Max Carvalho
- Escola de Matemática Aplicada, Fundação Getulio Vargas, Rio de Janeiro, Brazil
- Instituto de Ciências Matemáticas e Computação, Universidade de São Paulo, Brazil
| | - Weeberb J. Réquia
- Escola de Políticas Públicas e Governo, Fundação Getulio Vargas, Brasília, Brazil
| | - Fernando Couto Motta
- Laboratório de Vírus Respiratórios e do Sarampo, Instituto Oswaldo Cruz, FIOCRUZ, Rio de Janeiro, Brazil
| | - Marilda Mendonça Siqueira
- Laboratório de Vírus Respiratórios e do Sarampo, Instituto Oswaldo Cruz, FIOCRUZ, Rio de Janeiro, Brazil
| | - Ana T.R. Vasconcelos
- Laboratório de Bioinformática, Laboratório Nacional de Computação Científica (LNCC), Petrópolis, Brazil
| | - Guilherme C. da Fonseca
- Laboratório de Bioinformática, Laboratório Nacional de Computação Científica (LNCC), Petrópolis, Brazil
| | - Liliane T.F. Cavalcante
- Laboratório de Bioinformática, Laboratório Nacional de Computação Científica (LNCC), Petrópolis, Brazil
| | - Carlos A.M. Costa
- Escola Nacional de Saúde Pública Sérgio Arouca, FIOCRUZ, Rio de Janeiro, Brazil
| | | | - Daniel A.M. Villela
- Programa de Computação Científica (PROCC), FIOCRUZ, Rio de Janeiro, Brazil
- Center for Health and Wellbeing, School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - Tiago Pereira
- Instituto de Ciências Matemáticas e Computação, Universidade de São Paulo, Brazil
| | | | - Cleber V.B.D. Santos
- Instituto de Medicina Social Hesio Cordeiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Nadia C.P. Rodrigues
- Escola Nacional de Saúde Pública Sérgio Arouca, FIOCRUZ, Rio de Janeiro, Brazil
- Instituto de Medicina Social Hesio Cordeiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Breno Augusto Bormann de Souza Filho
- Escola Nacional de Saúde Pública Sérgio Arouca, FIOCRUZ, Rio de Janeiro, Brazil
- Departamento de Saúde Coletiva, Universidade Federal do Rio Grande do Norte, Rio Grande do Norte, Brazil
| | - Daniel Csillag
- Escola de Matemática Aplicada, Fundação Getulio Vargas, Rio de Janeiro, Brazil
| | - Beatriz Grinsztejn
- Instituto Nacional de Infectologia Evandro Chagas, FIOCRUZ, Rio de Janeiro, Brazil
| | - Valdilea G. Veloso
- Instituto Nacional de Infectologia Evandro Chagas, FIOCRUZ, Rio de Janeiro, Brazil
| | - Claudio J. Struchiner
- Escola de Matemática Aplicada, Fundação Getulio Vargas, Rio de Janeiro, Brazil
- Instituto de Medicina Social Hesio Cordeiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
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2
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Major CG, Rodríguez DM, Sánchez-González L, Rodríguez-Estrada V, Morales-Ortíz T, Torres C, Pérez-Rodríguez NM, Medina-Lópes NA, Alexander N, Mabey D, Ryff K, Tosado-Acevedo R, Muñoz-Jordán J, Adams LE, Rivera-Amill V, Rolfes M, Paz-Bailey G. Investigating SARS-CoV-2 Incidence and Morbidity in Ponce, Puerto Rico: Protocol and Baseline Results From a Community Cohort Study. JMIR Res Protoc 2024; 13:e53837. [PMID: 38640475 PMCID: PMC11034577 DOI: 10.2196/53837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 01/19/2024] [Indexed: 04/21/2024] Open
Abstract
BACKGROUND A better understanding of SARS-CoV-2 infection risk among Hispanic and Latino populations and in low-resource settings in the United States is needed to inform control efforts and strategies to improve health equity. Puerto Rico has a high poverty rate and other population characteristics associated with increased vulnerability to COVID-19, and there are limited data to date to determine community incidence. OBJECTIVE This study describes the protocol and baseline seroprevalence of SARS-CoV-2 in a prospective community-based cohort study (COPA COVID-19 [COCOVID] study) to investigate SARS-CoV-2 infection incidence and morbidity in Ponce, Puerto Rico. METHODS In June 2020, we implemented the COCOVID study within the Communities Organized to Prevent Arboviruses project platform among residents of 15 communities in Ponce, Puerto Rico, aged 1 year or older. Weekly, participants answered questionnaires on acute symptoms and preventive behaviors and provided anterior nasal swab samples for SARS-CoV-2 polymerase chain reaction testing; additional anterior nasal swabs were collected for expedited polymerase chain reaction testing from participants that reported 1 or more COVID-19-like symptoms. At enrollment and every 6 months during follow-up, participants answered more comprehensive questionnaires and provided venous blood samples for multiantigen SARS-CoV-2 immunoglobulin G antibody testing (an indicator of seroprevalence). Weekly follow-up activities concluded in April 2022 and 6-month follow-up visits concluded in August 2022. Primary study outcome measures include SARS-CoV-2 infection incidence and seroprevalence, relative risk of SARS-CoV-2 infection by participant characteristics, SARS-CoV-2 household attack rate, and COVID-19 illness characteristics and outcomes. In this study, we describe the characteristics of COCOVID participants overall and by SARS-CoV-2 seroprevalence status at baseline. RESULTS We enrolled a total of 1030 participants from 388 households. Relative to the general populations of Ponce and Puerto Rico, our cohort overrepresented middle-income households, employed and middle-aged adults, and older children (P<.001). Almost all participants (1021/1025, 99.61%) identified as Latino/a, 17.07% (175/1025) had annual household incomes less than US $10,000, and 45.66% (463/1014) reported 1 or more chronic medical conditions. Baseline SARS-CoV-2 seroprevalence was low (16/1030, 1.55%) overall and increased significantly with later study enrollment time (P=.003). CONCLUSIONS The COCOVID study will provide a valuable opportunity to better estimate the burden of SARS-CoV-2 and associated risk factors in a primarily Hispanic or Latino population, assess the limitations of surveillance, and inform mitigation measures in Puerto Rico and other similar populations. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR1-10.2196/53837.
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Affiliation(s)
- Chelsea G Major
- Division of Vector Borne Diseases, Centers for Disease Control and Prevention, San Juan, Puerto Rico
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Dania M Rodríguez
- Division of Vector Borne Diseases, Centers for Disease Control and Prevention, San Juan, Puerto Rico
| | - Liliana Sánchez-González
- Division of Vector Borne Diseases, Centers for Disease Control and Prevention, San Juan, Puerto Rico
| | | | | | - Carolina Torres
- Division of Vector Borne Diseases, Centers for Disease Control and Prevention, San Juan, Puerto Rico
- Kāpili Services, LLC, Alaka`ina Foundation Family of Companies, Orlando, FL, United States
| | - Nicole M Pérez-Rodríguez
- Division of Vector Borne Diseases, Centers for Disease Control and Prevention, San Juan, Puerto Rico
| | - Nicole A Medina-Lópes
- Division of Vector Borne Diseases, Centers for Disease Control and Prevention, San Juan, Puerto Rico
| | - Neal Alexander
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - David Mabey
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Kyle Ryff
- Division of Vector Borne Diseases, Centers for Disease Control and Prevention, San Juan, Puerto Rico
| | - Rafael Tosado-Acevedo
- Division of Vector Borne Diseases, Centers for Disease Control and Prevention, San Juan, Puerto Rico
| | - Jorge Muñoz-Jordán
- Division of Vector Borne Diseases, Centers for Disease Control and Prevention, San Juan, Puerto Rico
| | - Laura E Adams
- Division of Vector Borne Diseases, Centers for Disease Control and Prevention, San Juan, Puerto Rico
| | | | - Melissa Rolfes
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Gabriela Paz-Bailey
- Division of Vector Borne Diseases, Centers for Disease Control and Prevention, San Juan, Puerto Rico
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Geenen C, Raymenants J, Gorissen S, Thibaut J, McVernon J, Lorent N, André E. Individual level analysis of digital proximity tracing for COVID-19 in Belgium highlights major bottlenecks. Nat Commun 2023; 14:6717. [PMID: 37872213 PMCID: PMC10593825 DOI: 10.1038/s41467-023-42518-6] [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: 07/07/2023] [Accepted: 10/12/2023] [Indexed: 10/25/2023] Open
Abstract
To complement labour-intensive conventional contact tracing, digital proximity tracing was implemented widely during the COVID-19 pandemic. However, the privacy-centred design of the dominant Google-Apple exposure notification framework has hindered assessment of its effectiveness. Between October 2021 and January 2022, we systematically collected app use and notification receipt data within a test and trace programme targeting around 50,000 university students in Leuven, Belgium. Due to low success rates in each studied step of the digital notification cascade, only 4.3% of exposed contacts (CI: 2.8-6.1%) received such notifications, resulting in 10 times more cases detected through conventional contact tracing. Moreover, the infection risk of digitally traced contacts (5.0%; CI: 3.0-7.7%) was lower than that of conventionally traced non-app users (9.8%; CI: 8.8-10.7%; p = 0.002). Contrary to common perception as near instantaneous, there was a 1.2-day delay (CI: 0.6-2.2) between case PCR result and digital contact notification. These results highlight major limitations of a digital proximity tracing system based on the dominant framework.
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Affiliation(s)
- Caspar Geenen
- KU Leuven, Dept of Microbiology, Immunology and Transplantation, Laboratory of Clinical Microbiology, Leuven, Belgium.
| | - Joren Raymenants
- KU Leuven, Dept of Microbiology, Immunology and Transplantation, Laboratory of Clinical Microbiology, Leuven, Belgium
- Department of Infectious Diseases, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Sarah Gorissen
- KU Leuven, Dept of Microbiology, Immunology and Transplantation, Laboratory of Clinical Microbiology, Leuven, Belgium
| | - Jonathan Thibaut
- KU Leuven, Dept of Microbiology, Immunology and Transplantation, Laboratory of Clinical Microbiology, Leuven, Belgium
| | - Jodie McVernon
- Department of Infectious Diseases, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
- Victorian Infectious Diseases Laboratory Epidemiology Unit, Royal Melbourne Hospital at The Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Natalie Lorent
- University Hospitals Leuven, Respiratory Diseases, Leuven, Belgium
- KU Leuven, Dept of CHROMETA, Laboratory of Thoracic Surgery and Respiratory Diseases (BREATHE), Leuven, Belgium
| | - Emmanuel André
- KU Leuven, Dept of Microbiology, Immunology and Transplantation, Laboratory of Clinical Microbiology, Leuven, Belgium
- University Hospitals Leuven, Laboratory Medicine, Leuven, Belgium
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Anderson TL, Nande A, Merenstein C, Raynor B, Oommen A, Kelly BJ, Levy MZ, Hill AL. Quantifying individual-level heterogeneity in infectiousness and susceptibility through household studies. Epidemics 2023; 44:100710. [PMID: 37556994 PMCID: PMC10594662 DOI: 10.1016/j.epidem.2023.100710] [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: 12/01/2022] [Revised: 03/17/2023] [Accepted: 07/18/2023] [Indexed: 08/11/2023] Open
Abstract
The spread of SARS-CoV-2, like that of many other pathogens, is governed by heterogeneity. "Superspreading," or "over-dispersion," is an important factor in transmission, yet it is hard to quantify. Estimates from contact tracing data are prone to potential biases due to the increased likelihood of detecting large clusters of cases, and may reflect variation in contact behavior more than biological heterogeneity. In contrast, the average number of secondary infections per contact is routinely estimated from household surveys, and these studies can minimize biases by testing all members of a household. However, the models used to analyze household transmission data typically assume that infectiousness and susceptibility are the same for all individuals or vary only with predetermined traits such as age. Here we develop and apply a combined forward simulation and inference method to quantify the degree of inter-individual variation in both infectiousness and susceptibility from observations of the distribution of infections in household surveys. First, analyzing simulated data, we show our method can reliably ascertain the presence, type, and amount of these heterogeneities given data from a sufficiently large sample of households. We then analyze a collection of household studies of COVID-19 from diverse settings around the world, and find strong evidence for large heterogeneity in both the infectiousness and susceptibility of individuals. Our results also provide a framework to improve the design of studies to evaluate household interventions in the presence of realistic heterogeneity between individuals.
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Affiliation(s)
- Thayer L Anderson
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States of America
| | - Anjalika Nande
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States of America
| | - Carter Merenstein
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Brinkley Raynor
- Department of Biostatistics, Epidemiology, & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Anisha Oommen
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States of America; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States of America
| | - Brendan J Kelly
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America; Department of Biostatistics, Epidemiology, & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America; Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Michael Z Levy
- Department of Biostatistics, Epidemiology, & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Alison L Hill
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States of America; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States of America.
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Frutos AM, Kuan G, Lopez R, Ojeda S, Shotwell A, Sanchez N, Saborio S, Plazaola M, Barilla C, Kenah E, Balmaseda A, Gordon A. Infection-Induced Immunity Is Associated With Protection Against Severe Acute Respiratory Syndrome Coronavirus 2 Infection and Decreased Infectivity. Clin Infect Dis 2023; 76:2126-2133. [PMID: 36774538 PMCID: PMC10273383 DOI: 10.1093/cid/ciad074] [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: 10/10/2022] [Revised: 01/24/2023] [Accepted: 02/08/2023] [Indexed: 02/13/2023] Open
Abstract
BACKGROUND The impact of infection-induced immunity on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission has not been well established. Here we estimate the effects of prior infection induced immunity in adults and children on SARS-CoV-2 transmission in households. METHODS We conducted a household cohort study from March 2020-November 2022 in Managua, Nicaragua; following a housheold SARS-CoV-2 infection, household members are closely monitored for infection. We estimate the association of time period, age, symptoms, and prior infection with secondary attack risk. RESULTS Overall, transmission occurred in 70.2% of households, 40.9% of household contacts were infected, and the secondary attack risk ranged from 8.1% to 13.9% depending on the time period. Symptomatic infected individuals were more infectious (rate ratio [RR] 21.2, 95% confidence interval [CI]: 7.4-60.7) and participants with a prior infection were half as likely to be infected compared to naïve individuals (RR 0.52, 95% CI:.38-.70). In models stratified by age, prior infection was associated with decreased infectivity in adults and adolescents (secondary attack risk [SAR] 12.3, 95% CI: 10.3, 14.8 vs 17.5, 95% CI: 14.8, 20.7). However, although young children were less likely to transmit, neither prior infection nor symptom presentation was associated with infectivity. During the Omicron era, infection-induced immunity remained protective against infection. CONCLUSIONS Infection-induced immunity is associated with decreased infectivity for adults and adolescents. Although young children are less infectious, prior infection and asymptomatic presentation did not reduce their infectivity as was seen in adults. As SARS-CoV-2 transitions to endemicity, children may become more important in transmission dynamics.
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Affiliation(s)
- Aaron M Frutos
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| | - Guillermina Kuan
- Health Center Sócrates Flores Vivas, Ministry of Health, Managua, Nicaragua
- Sustainable Sciences Institute, Managua, Nicaragua
| | - Roger Lopez
- Sustainable Sciences Institute, Managua, Nicaragua
- Laboratorio Nacional de Virología, Centro Nacional de Diagnóstico y Referencia, Ministry of Health, Managua, Nicaragua
| | - Sergio Ojeda
- Sustainable Sciences Institute, Managua, Nicaragua
| | - Abigail Shotwell
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| | - Nery Sanchez
- Sustainable Sciences Institute, Managua, Nicaragua
| | - Saira Saborio
- Laboratorio Nacional de Virología, Centro Nacional de Diagnóstico y Referencia, Ministry of Health, Managua, Nicaragua
- Sustainable Sciences Institute, Managua, Nicaragua
| | | | | | - Eben Kenah
- Biostatistics Division, College of Public Health, The Ohio State University, Columbus, Ohio, USA
| | - Angel Balmaseda
- Sustainable Sciences Institute, Managua, Nicaragua
- Laboratorio Nacional de Virología, Centro Nacional de Diagnóstico y Referencia, Ministry of Health, Managua, Nicaragua
| | - Aubree Gordon
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
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Bannick MS, Gao F, Brown ER, Janes HE. Retrospective, Observational Studies for Estimating Vaccine Effects on the Secondary Attack Rate of SARS-CoV-2. Am J Epidemiol 2023; 192:1016-1028. [PMID: 36883907 PMCID: PMC10505422 DOI: 10.1093/aje/kwad046] [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: 05/31/2022] [Revised: 11/21/2022] [Accepted: 02/23/2023] [Indexed: 03/09/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) vaccines are highly efficacious at preventing symptomatic infection, severe disease, and death. Most of the evidence that COVID-19 vaccines also reduce transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is based on retrospective, observational studies. Specifically, an increasing number of studies are evaluating vaccine effectiveness against the secondary attack rate of SARS-CoV-2 using data available in existing health-care databases or contact-tracing databases. Since these types of databases were designed for clinical diagnosis or management of COVID-19, they are limited in their ability to provide accurate information on infection, infection timing, and transmission events. We highlight challenges with using existing databases to identify transmission units and confirm potential SARS-CoV-2 transmission events. We discuss the impact of common diagnostic testing strategies, including event-prompted and infrequent testing, and illustrate their potential biases in estimating vaccine effectiveness against the secondary attack rate of SARS-CoV-2. We articulate the need for prospective observational studies of vaccine effectiveness against the SARS-CoV-2 secondary attack rate, and we provide design and reporting considerations for studies using retrospective databases.
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Affiliation(s)
- Marlena S Bannick
- Correspondence to Marlena Bannick, Department of Biostatistics, Hans Rosling Center for Population Health, Box 357232, University of Washington, Seattle, WA 98195 (e-mail: )
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Yu M, Lee SE, Lee HY, Kim HJ, Song YJ, Jeong J, Park AK, Kim IH, Kim EJ, Park YJ. Results of contact tracing for SARS-CoV-2 Omicron sub-lineages (BA.4, BA.5, BA.2.75) and the household secondary attack risk. Osong Public Health Res Perspect 2023; 14:173-179. [PMID: 37415434 PMCID: PMC10522828 DOI: 10.24171/j.phrp.2022.0285] [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: 10/23/2022] [Revised: 03/28/2023] [Accepted: 05/01/2023] [Indexed: 07/08/2023] Open
Abstract
BACKGROUND This study aimed to assess the contact tracing outcomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron sub-lineages BA.4, BA.5, and BA.2.75 within Republic of Korea, and to generate foundational data for responding to future novel variants. METHODS We conducted investigations and contact tracing for 79 confirmed BA.4 cases, 396 confirmed BA.5 cases, and 152 confirmed BA.2.75 cases. These cases were identified through random sampling of both domestically confirmed and imported cases, with the goal of evaluating the pattern of occurrence and transmissibility. RESULTS We detected 79 instances of Omicron sub-lineage BA.4 across a span of 46 days, 396 instances of Omicron sub-lineage BA.5 in 46 days, and 152 instances of Omicron sub-lineage BA.2.75 over 62 days. One patient with severe illness was confirmed among the BA.5 cases; however, there were no reports of severe illness in the confirmed BA.4 and BA.2.75 cases. The secondary attack risk among household contacts were 19.6% for BA.4, 27.8% for BA.5, and 24.3% for BA.2.75. No statistically significant difference was found between the Omicron sub-lineages. CONCLUSION BA.2.75 did not demonstrate a higher tendency for transmissibility, disease severity, or secondary attack risk within households when compared to BA.4 and BA.5. We will continue to monitor major SARS-CoV-2 variants, and we plan to enhance the disease control and response systems.
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Affiliation(s)
- Mi Yu
- Division of Epidemiological Investigation, Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea
| | - Sang-Eun Lee
- Division of Epidemiological Investigation, Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea
| | - Hye Young Lee
- Division of Epidemiological Investigation, Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea
| | - Hye-jin Kim
- Division of Epidemiological Investigation, Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea
| | - Yeong-Jun Song
- Division of Epidemiological Investigation, Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea
| | - Jian Jeong
- Division of Epidemiological Investigation, Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea
| | - Ae Kyung Park
- Division of Emerging Infectious Diseases, Bureau of Infectious Diseases Diagnosis Control, Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea
| | - Il-Hwan Kim
- Division of Emerging Infectious Diseases, Bureau of Infectious Diseases Diagnosis Control, Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea
| | - Eun-jin Kim
- Division of Emerging Infectious Diseases, Bureau of Infectious Diseases Diagnosis Control, Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea
| | - Young-Joon Park
- Division of Epidemiological Investigation, Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea
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Anderson TL, Nande A, Merenstein C, Raynor B, Oommen A, Kelly BJ, Levy MZ, Hill AL. Quantifying individual-level heterogeneity in infectiousness and susceptibility through household studies. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.12.02.22281853. [PMID: 36523404 PMCID: PMC9753792 DOI: 10.1101/2022.12.02.22281853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The spread of SARS-CoV-2, like that of many other pathogens, is governed by heterogeneity. "Superspreading," or "over-dispersion," is an important factor in transmission, yet it is hard to quantify. Estimates from contact tracing data are prone to potential biases due to the increased likelihood of detecting large clusters of cases, and may reflect variation in contact behavior more than biological heterogeneity. In contrast, the average number of secondary infections per contact is routinely estimated from household surveys, and these studies can minimize biases by testing all members of a household. However, the models used to analyze household transmission data typically assume that infectiousness and susceptibility are the same for all individuals or vary only with predetermined traits such as age. Here we develop and apply a combined forward simulation and inference method to quantify the degree of inter-individual variation in both infectiousness and susceptibility from observations of the distribution of infections in household surveys. First, analyzing simulated data, we show our method can reliably ascertain the presence, type, and amount of these heterogeneities with data from a sufficiently large sample of households. We then analyze a collection of household studies of COVID-19 from diverse settings around the world, and find strong evidence for large heterogeneity in both the infectiousness and susceptibility of individuals. Our results also provide a framework to improve the design of studies to evaluate household interventions in the presence of realistic heterogeneity between individuals.
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Affiliation(s)
- Thayer L Anderson
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218
| | - Anjalika Nande
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218
| | - Carter Merenstein
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Brinkley Raynor
- Department of Biostatistics, Epidemiology, & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Anisha Oommen
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218
| | - Brendan J Kelly
- Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Michael Z Levy
- Department of Biostatistics, Epidemiology, & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Alison L Hill
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218
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Infection-induced immunity is associated with protection against SARS-CoV-2 infection, but not decreased infectivity during household transmission. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.10.10.22280915. [PMID: 36263069 PMCID: PMC9580390 DOI: 10.1101/2022.10.10.22280915] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Background Understanding the impact of infection-induced immunity on SARS-CoV-2 transmission will provide insight into the transition of SARS-CoV-2 to endemicity. Here we estimate the effects of prior infection induced immunity and children on SARS-CoV-2 transmission in households. Methods We conducted a household cohort study between March 2020-June 2022 in Managua, Nicaragua where when one household member tests positive for SARS-CoV-2, household members are closely monitored for SARS-CoV-2 infection. Using a pairwise survival model, we estimate the association of infection period, age, symptoms, and infection-induced immunity with secondary attack risk. Results Overall transmission occurred in 72.4% of households, 42% of household contacts were infected and the secondary attack risk was 13.0% (95% CI: 11.7, 14.6). Prior immunity did not impact the probability of transmitting SARS-CoV-2. However, participants with pre-existing infection-induced immunity were half as likely to be infected compared to naïve individuals (RR 0.53, 95% CI: 0.39, 0.72), but this reduction was not observed in children. Likewise, symptomatic infected individuals were more likely to transmit (RR 24.4, 95% CI: 7.8, 76.1); however, symptom presentation was not associated with infectivity of young children. Young children were less likely to transmit SARS-CoV-2 than adults. During the omicron era, infection-induced immunity remained protective against infection. Conclusions Infection-induced immunity is associated with protection against infection for adults and adolescents. While young children are less infectious, prior infection and asymptomatic presentation did not reduce their infectivity as was seen in adults. As SARS-CoV-2 transitions to endemicity, children may become more important in transmission dynamics. Article summary Infection-induced immunity protects against SARS-CoV-2 infection for adolescents and adults; however, there was no protection in children. Prior immunity in an infected individual did not impact the probability they will spread SARS-CoV-2 in a household setting.
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10
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Increased household transmission and immune escape of the SARS-CoV-2 Omicron compared to Delta variants. Nat Commun 2022; 13:5706. [PMID: 36175424 PMCID: PMC9520116 DOI: 10.1038/s41467-022-33233-9] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 09/08/2022] [Indexed: 11/23/2022] Open
Abstract
Understanding the epidemic growth of the novel SARS-CoV-2 Omicron variant is critical for public health. We compared the ten-day secondary attack rate (SAR) of the Omicron and Delta variants in households using Norwegian contact tracing data, December 2021 - January 2022. Omicron SAR was higher than Delta, with a relative risk (RR) of 1.41 (95% CI 1.27-1.56). We observed increased susceptibility to Omicron infection in household contacts compared to Delta, independent of contacts’ vaccination status. Among three-dose vaccinated contacts, the mean SAR was lower for both variants. We found increased Omicron transmissibility from primary cases to contacts in all vaccination groups, except 1-dose vaccinated, compared to Delta. Omicron SAR of three-dose vaccinated primary cases was high, 46% vs 11 % for Delta. In conclusion, three-dose vaccinated primary cases with Omicron infection can efficiently spread in households, while three-dose vaccinated contacts have a lower risk of being infected by Delta and Omicron. In this study the authors investigate household transmission of SARS-CoV-2 in Norway. They find that the secondary attack rate was higher for Omicron than Delta, but that among three-dose vaccinated contacts the secondary attack rate was lower for both variants compared to contacts with two doses.
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11
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Jalali N, Brustad HK, Frigessi A, MacDonald EA, Meijerink H, Feruglio SL, Nygård KM, Rø G, Madslien EH, de Blasio BF. Increased household transmission and immune escape of the SARS-CoV-2 Omicron compared to Delta variants. Nat Commun 2022; 13:5706. [PMID: 36175424 DOI: 10.1101/2022.02.07.22270437] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 09/08/2022] [Indexed: 05/22/2023] Open
Abstract
Understanding the epidemic growth of the novel SARS-CoV-2 Omicron variant is critical for public health. We compared the ten-day secondary attack rate (SAR) of the Omicron and Delta variants in households using Norwegian contact tracing data, December 2021 - January 2022. Omicron SAR was higher than Delta, with a relative risk (RR) of 1.41 (95% CI 1.27-1.56). We observed increased susceptibility to Omicron infection in household contacts compared to Delta, independent of contacts' vaccination status. Among three-dose vaccinated contacts, the mean SAR was lower for both variants. We found increased Omicron transmissibility from primary cases to contacts in all vaccination groups, except 1-dose vaccinated, compared to Delta. Omicron SAR of three-dose vaccinated primary cases was high, 46% vs 11 % for Delta. In conclusion, three-dose vaccinated primary cases with Omicron infection can efficiently spread in households, while three-dose vaccinated contacts have a lower risk of being infected by Delta and Omicron.
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Affiliation(s)
- Neda Jalali
- Norwegian Institute of Public Health, Oslo, Norway
| | - Hilde K Brustad
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway
| | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | | | | | | | | | - Gunnar Rø
- Norwegian Institute of Public Health, Oslo, Norway
| | | | - Birgitte Freiesleben de Blasio
- Norwegian Institute of Public Health, Oslo, Norway.
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway.
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