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Fox T, Geppert J, Dinnes J, Scandrett K, Bigio J, Sulis G, Hettiarachchi D, Mathangasinghe Y, Weeratunga P, Wickramasinghe D, Bergman H, Buckley BS, Probyn K, Sguassero Y, Davenport C, Cunningham J, Dittrich S, Emperador D, Hooft L, Leeflang MM, McInnes MD, Spijker R, Struyf T, Van den Bruel A, Verbakel JY, Takwoingi Y, Taylor-Phillips S, Deeks JJ. Antibody tests for identification of current and past infection with SARS-CoV-2. Cochrane Database Syst Rev 2022; 11:CD013652. [PMID: 36394900 PMCID: PMC9671206 DOI: 10.1002/14651858.cd013652.pub2] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
BACKGROUND The diagnostic challenges associated with the COVID-19 pandemic resulted in rapid development of diagnostic test methods for detecting SARS-CoV-2 infection. Serology tests to detect the presence of antibodies to SARS-CoV-2 enable detection of past infection and may detect cases of SARS-CoV-2 infection that were missed by earlier diagnostic tests. Understanding the diagnostic accuracy of serology tests for SARS-CoV-2 infection may enable development of effective diagnostic and management pathways, inform public health management decisions and understanding of SARS-CoV-2 epidemiology. OBJECTIVES To assess the accuracy of antibody tests, firstly, to determine if a person presenting in the community, or in primary or secondary care has current SARS-CoV-2 infection according to time after onset of infection and, secondly, to determine if a person has previously been infected with SARS-CoV-2. Sources of heterogeneity investigated included: timing of test, test method, SARS-CoV-2 antigen used, test brand, and reference standard for non-SARS-CoV-2 cases. SEARCH METHODS The COVID-19 Open Access Project living evidence database from the University of Bern (which includes daily updates from PubMed and Embase and preprints from medRxiv and bioRxiv) was searched on 30 September 2020. We included additional publications from the Evidence for Policy and Practice Information and Co-ordinating Centre (EPPI-Centre) 'COVID-19: Living map of the evidence' and the Norwegian Institute of Public Health 'NIPH systematic and living map on COVID-19 evidence'. We did not apply language restrictions. SELECTION CRITERIA We included test accuracy studies of any design that evaluated commercially produced serology tests, targeting IgG, IgM, IgA alone, or in combination. Studies must have provided data for sensitivity, that could be allocated to a predefined time period after onset of symptoms, or after a positive RT-PCR test. Small studies with fewer than 25 SARS-CoV-2 infection cases were excluded. We included any reference standard to define the presence or absence of SARS-CoV-2 (including reverse transcription polymerase chain reaction tests (RT-PCR), clinical diagnostic criteria, and pre-pandemic samples). DATA COLLECTION AND ANALYSIS We use standard screening procedures with three reviewers. Quality assessment (using the QUADAS-2 tool) and numeric study results were extracted independently by two people. Other study characteristics were extracted by one reviewer and checked by a second. We present sensitivity and specificity with 95% confidence intervals (CIs) for each test and, for meta-analysis, we fitted univariate random-effects logistic regression models for sensitivity by eligible time period and for specificity by reference standard group. Heterogeneity was investigated by including indicator variables in the random-effects logistic regression models. We tabulated results by test manufacturer and summarised results for tests that were evaluated in 200 or more samples and that met a modification of UK Medicines and Healthcare products Regulatory Agency (MHRA) target performance criteria. MAIN RESULTS We included 178 separate studies (described in 177 study reports, with 45 as pre-prints) providing 527 test evaluations. The studies included 64,688 samples including 25,724 from people with confirmed SARS-CoV-2; most compared the accuracy of two or more assays (102/178, 57%). Participants with confirmed SARS-CoV-2 infection were most commonly hospital inpatients (78/178, 44%), and pre-pandemic samples were used by 45% (81/178) to estimate specificity. Over two-thirds of studies recruited participants based on known SARS-CoV-2 infection status (123/178, 69%). All studies were conducted prior to the introduction of SARS-CoV-2 vaccines and present data for naturally acquired antibody responses. Seventy-nine percent (141/178) of studies reported sensitivity by week after symptom onset and 66% (117/178) for convalescent phase infection. Studies evaluated enzyme-linked immunosorbent assays (ELISA) (165/527; 31%), chemiluminescent assays (CLIA) (167/527; 32%) or lateral flow assays (LFA) (188/527; 36%). Risk of bias was high because of participant selection (172, 97%); application and interpretation of the index test (35, 20%); weaknesses in the reference standard (38, 21%); and issues related to participant flow and timing (148, 82%). We judged that there were high concerns about the applicability of the evidence related to participants in 170 (96%) studies, and about the applicability of the reference standard in 162 (91%) studies. Average sensitivities for current SARS-CoV-2 infection increased by week after onset for all target antibodies. Average sensitivity for the combination of either IgG or IgM was 41.1% in week one (95% CI 38.1 to 44.2; 103 evaluations; 3881 samples, 1593 cases), 74.9% in week two (95% CI 72.4 to 77.3; 96 evaluations, 3948 samples, 2904 cases) and 88.0% by week three after onset of symptoms (95% CI 86.3 to 89.5; 103 evaluations, 2929 samples, 2571 cases). Average sensitivity during the convalescent phase of infection (up to a maximum of 100 days since onset of symptoms, where reported) was 89.8% for IgG (95% CI 88.5 to 90.9; 253 evaluations, 16,846 samples, 14,183 cases), 92.9% for IgG or IgM combined (95% CI 91.0 to 94.4; 108 evaluations, 3571 samples, 3206 cases) and 94.3% for total antibodies (95% CI 92.8 to 95.5; 58 evaluations, 7063 samples, 6652 cases). Average sensitivities for IgM alone followed a similar pattern but were of a lower test accuracy in every time slot. Average specificities were consistently high and precise, particularly for pre-pandemic samples which provide the least biased estimates of specificity (ranging from 98.6% for IgM to 99.8% for total antibodies). Subgroup analyses suggested small differences in sensitivity and specificity by test technology however heterogeneity in study results, timing of sample collection, and smaller sample numbers in some groups made comparisons difficult. For IgG, CLIAs were the most sensitive (convalescent-phase infection) and specific (pre-pandemic samples) compared to both ELISAs and LFAs (P < 0.001 for differences across test methods). The antigen(s) used (whether from the Spike-protein or nucleocapsid) appeared to have some effect on average sensitivity in the first weeks after onset but there was no clear evidence of an effect during convalescent-phase infection. Investigations of test performance by brand showed considerable variation in sensitivity between tests, and in results between studies evaluating the same test. For tests that were evaluated in 200 or more samples, the lower bound of the 95% CI for sensitivity was 90% or more for only a small number of tests (IgG, n = 5; IgG or IgM, n = 1; total antibodies, n = 4). More test brands met the MHRA minimum criteria for specificity of 98% or above (IgG, n = 16; IgG or IgM, n = 5; total antibodies, n = 7). Seven assays met the specified criteria for both sensitivity and specificity. In a low-prevalence (2%) setting, where antibody testing is used to diagnose COVID-19 in people with symptoms but who have had a negative PCR test, we would anticipate that 1 (1 to 2) case would be missed and 8 (5 to 15) would be falsely positive in 1000 people undergoing IgG or IgM testing in week three after onset of SARS-CoV-2 infection. In a seroprevalence survey, where prevalence of prior infection is 50%, we would anticipate that 51 (46 to 58) cases would be missed and 6 (5 to 7) would be falsely positive in 1000 people having IgG tests during the convalescent phase (21 to 100 days post-symptom onset or post-positive PCR) of SARS-CoV-2 infection. AUTHORS' CONCLUSIONS Some antibody tests could be a useful diagnostic tool for those in whom molecular- or antigen-based tests have failed to detect the SARS-CoV-2 virus, including in those with ongoing symptoms of acute infection (from week three onwards) or those presenting with post-acute sequelae of COVID-19. However, antibody tests have an increasing likelihood of detecting an immune response to infection as time since onset of infection progresses and have demonstrated adequate performance for detection of prior infection for sero-epidemiological purposes. The applicability of results for detection of vaccination-induced antibodies is uncertain.
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Affiliation(s)
- Tilly Fox
- Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK
| | - Julia Geppert
- Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK
| | - Jacqueline Dinnes
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | - Katie Scandrett
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Jacob Bigio
- Research Institute of the McGill University Health Centre, Montreal, Canada
- McGill International TB Centre, Montreal, Canada
| | - Giorgia Sulis
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada
| | - Dineshani Hettiarachchi
- Department of Anatomy Genetics and Biomedical Informatics, Faculty of Medicine, University of Colombo, Colombo, Sri Lanka
| | - Yasith Mathangasinghe
- Department of Anatomy Genetics and Biomedical Informatics, Faculty of Medicine, University of Colombo, Colombo, Sri Lanka
- Australian Regenerative Medicine Institute, Monash University, Clayton, Australia
| | - Praveen Weeratunga
- Department of Clinical Medicine, Faculty of Medicine, University of Colombo, Colombo, Sri Lanka
| | | | | | - Brian S Buckley
- Cochrane Response, Cochrane, London, UK
- Department of Surgery, University of the Philippines, Manila, Philippines
| | | | | | - Clare Davenport
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | - Jane Cunningham
- Global Malaria Programme, World Health Organization, Geneva, Switzerland
| | | | | | - Lotty Hooft
- Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht , Netherlands
| | - Mariska Mg Leeflang
- Epidemiology and Data Science, Amsterdam UMC location University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Public Health, Amsterdam, Netherlands
| | | | - René Spijker
- Medical Library, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health, Amsterdam, Netherlands
- Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Thomas Struyf
- Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
| | - Ann Van den Bruel
- Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
| | - Jan Y Verbakel
- Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
| | - Yemisi Takwoingi
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | - Sian Taylor-Phillips
- Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Jonathan J Deeks
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
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Catalysis on Nanostructured Indium Tin Oxide Surface for Fast and Inexpensive Probing of Antibodies during Pandemics. Catalysts 2021. [DOI: 10.3390/catal11020191] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global threat to human health and the economy. Society needs inexpensive, fast, and accurate quantitative diagnostic tools. Here, we report a new approach using a solid-state biosensor to measure antibodies, which does not require functionalization, unlike conventional biosensors. A nanostructured semiconductor surface with catalytic properties was used as a transducer for rapid immobilization and measurement of the antibody. The transducer response was based on solid-state electronics properties. The changes on the surface of the semiconductor induced changes in the direct current (DC) surface resistivity. This was a result of a catalytic chemical reaction on that surface. This new low-cost approach reduced the response time of the measurement significantly, and it required only a very small amount of sample on the microliter scale.
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Peirlinck M, Linka K, Sahli Costabal F, Bhattacharya J, Bendavid E, Ioannidis JPA, Kuhl E. Visualizing the invisible: The effect of asymptomatic transmission on the outbreak dynamics of COVID-19. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2020; 372:113410. [PMID: 33518823 PMCID: PMC7831913 DOI: 10.1016/j.cma.2020.113410] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 08/28/2020] [Accepted: 08/31/2020] [Indexed: 05/04/2023]
Abstract
Understanding the outbreak dynamics of the COVID-19 pandemic has important implications for successful containment and mitigation strategies. Recent studies suggest that the population prevalence of SARS-CoV-2 antibodies, a proxy for the number of asymptomatic cases, could be an order of magnitude larger than expected from the number of reported symptomatic cases. Knowing the precise prevalence and contagiousness of asymptomatic transmission is critical to estimate the overall dimension and pandemic potential of COVID-19. However, at this stage, the effect of the asymptomatic population, its size, and its outbreak dynamics remain largely unknown. Here we use reported symptomatic case data in conjunction with antibody seroprevalence studies, a mathematical epidemiology model, and a Bayesian framework to infer the epidemiological characteristics of COVID-19. Our model computes, in real time, the time-varying contact rate of the outbreak, and projects the temporal evolution and credible intervals of the effective reproduction number and the symptomatic, asymptomatic, and recovered populations. Our study quantifies the sensitivity of the outbreak dynamics of COVID-19 to three parameters: the effective reproduction number, the ratio between the symptomatic and asymptomatic populations, and the infectious periods of both groups. For nine distinct locations, our model estimates the fraction of the population that has been infected and recovered by Jun 15, 2020 to 24.15% (95% CI: 20.48%-28.14%) for Heinsberg (NRW, Germany), 2.40% (95% CI: 2.09%-2.76%) for Ada County (ID, USA), 46.19% (95% CI: 45.81%-46.60%) for New York City (NY, USA), 11.26% (95% CI: 7.21%-16.03%) for Santa Clara County (CA, USA), 3.09% (95% CI: 2.27%-4.03%) for Denmark, 12.35% (95% CI: 10.03%-15.18%) for Geneva Canton (Switzerland), 5.24% (95% CI: 4.84%-5.70%) for the Netherlands, 1.53% (95% CI: 0.76%-2.62%) for Rio Grande do Sul (Brazil), and 5.32% (95% CI: 4.77%-5.93%) for Belgium. Our method traces the initial outbreak date in Santa Clara County back to January 20, 2020 (95% CI: December 29, 2019-February 13, 2020). Our results could significantly change our understanding and management of the COVID-19 pandemic: A large asymptomatic population will make isolation, containment, and tracing of individual cases challenging. Instead, managing community transmission through increasing population awareness, promoting physical distancing, and encouraging behavioral changes could become more relevant.
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Affiliation(s)
- Mathias Peirlinck
- Department of Mechanical Engineering, Stanford University School of Engineering, Stanford, CA, United States
| | - Kevin Linka
- Department of Mechanical Engineering, Stanford University School of Engineering, Stanford, CA, United States
| | - Francisco Sahli Costabal
- Department of Mechanical and Metallurgical Engineering and Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - Jay Bhattacharya
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States
| | - Eran Bendavid
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States
| | - John P A Ioannidis
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, United States
| | - Ellen Kuhl
- Department of Mechanical Engineering, Stanford University School of Engineering, Stanford, CA, United States
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Chen X, Chen Z, Azman AS, Deng X, Chen X, Lu W, Zhao Z, Yang J, Viboud C, Ajelli M, Leung DT, Yu H. Serological evidence of human infection with SARS-CoV-2: a systematic review and meta-analysis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020. [PMID: 32935122 DOI: 10.1101/2020.09.11.20192773] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Background A rapidly increasing number of serological surveys for anti-SARS-CoV-2 antibodies have been reported worldwide. A synthesis of this large corpus of data is needed. Purpose To evaluate the quality of serological studies and provide a global picture of seroprevalence across demographic and occupational groups, and to provide guidance for conducting better serosurveys. Data sources We searched PubMed, Embase, Web of Science, and 4 pre-print servers for English-language papers published from December 1, 2019 to September 25, 2020. Study selection Serological studies evaluating SARS-CoV-2 seroprevalence in humans. Data extraction Two investigators independently extracted data from studies. Data Synthesis Most of 230 serological studies, representing tests in >1,400,000 individuals, identified were of low quality based on a standardized study quality scale. In the 51 studies of higher quality, high-risk healthcare workers had higher seroprevalence of 17.1% (95% CI: 9.9-24.4%), compared to low-risk healthcare workers and general population of 5.4% (0.7-10.1%) and 5.3% (4.2-6.4%). Seroprevalence varied hugely across WHO regions, with lowest seroprevalence of general population in Western Pacific region (1.7%, 0.0-5.0%). Generally, the young (<20 years) and the old (≥65 years) were less likely to be seropositive compared to middle-aged (20-64 years) populations.Seroprevalence correlated with clinical COVID-19 reports, with pooled average of 7.7 (range: 2.0 to 23.1) serologically-detected-infections per confirmed COVID-19 case. Limitations Some heterogeneity cannot be well explained quantitatively. Conclusions The overall quality of seroprevalence studies examined was low. The relatively low seroprevalence among general populations suggest that in most settings, antibody-mediated herd immunity is far from being reached. Given the relatively narrow range of estimates of the ratio of serologically-detected infections to confirmed cases across different locales, reported case counts may help provide insights into the true proportion of the population infected. Primary Funding source National Science Fund for Distinguished Young Scholars (PROSPERO: CRD42020198253).
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Grandjean P, Timmermann CAG, Kruse M, Nielsen F, Vinholt PJ, Boding L, Heilmann C, Mølbak K. Severity of COVID-19 at elevated exposure to perfluorinated alkylates. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020:2020.10.22.20217562. [PMID: 33140071 PMCID: PMC7605584 DOI: 10.1101/2020.10.22.20217562] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Background The course of coronavirus disease 2019 (COVID-19) seems to be aggravated by air pollution, and some industrial chemicals, such as the perfluorinated alkylate substances (PFASs), are immunotoxic and may contribute as well. Methods From Danish biobanks, we obtained plasma samples from 323 subjects aged 30-70 years with known SARS-CoV-2 infection. The PFAS concentrations measured at the background exposures included five PFASs known to be immunotoxic. Register data was obtained to classify disease status, other health information, and demographic variables. We used ordinal and ordered logistic regression analyses to determine associations between PFAS concentrations and disease outcome. Results Plasma-PFAS concentrations were higher in males, in subjects with Western European background, and tended to increase with age, but were not associated with the presence of chronic disease. Of the study population, 108 (33%) had not been hospitalized, and of those hospitalized, 53 (16%) had been in intensive care or were deceased. Among the five PFASs considered, perfluorobutanoic acid (PFBA) showed an odds ratio (OR) of 2.19 (95% confidence interval, CI, 1.39-3.46) for increasing severities of the disease, although the OR decreased to 1.77 (95% CI, 1.09, 2.87) after adjustment for age, sex, sampling site and interval between blood sampling and diagnosis. Conclusions Measures of individual exposures to immunotoxic PFASs included PFBA that accumulates in the lungs. Elevated plasma-PFBA concentrations were associated with an increased risk of more severe course of CIVID-19. Given the low background exposure levels in this study, the role of PFAS exposure in COVID-19 needs to be ascertained in populations with elevated exposures.
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Affiliation(s)
- P Grandjean
- The Department of Environmental Health, Harvard T.H.Chan School of Public Health, Boston, MA
- the Department of Environmental Medicine, University of Southern Denmark, Odense, Denmark
| | - C A G Timmermann
- the Department of Environmental Medicine, University of Southern Denmark, Odense, Denmark
| | - M Kruse
- the Department of Health Economics, University of Southern Denmark, Odense, Denmark
| | - F Nielsen
- the Department of Environmental Medicine, University of Southern Denmark, Odense, Denmark
| | - P Just Vinholt
- the Department of Clinical Biochemistry and Pharmacology, Odense University Hospital, Odense, Denmark
| | - L Boding
- the Department of Epidemiology, Statens Serum Institut, Copenhagen, Denmark
| | - C Heilmann
- Pediatric Clinic, Rigshospitalet - National University Hospital, Copenhagen, Denmark
| | - K Mølbak
- the Department of Epidemiology, Statens Serum Institut, Copenhagen, Denmark
- the Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen
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Villalobos C. SARS-CoV-2 Infections in the World: An Estimation of the Infected Population and a Measure of How Higher Detection Rates Save Lives. Front Public Health 2020; 8:489. [PMID: 33102412 PMCID: PMC7545403 DOI: 10.3389/fpubh.2020.00489] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Accepted: 07/31/2020] [Indexed: 12/21/2022] Open
Abstract
This paper provides an estimation of the accumulated detection rates and the accumulated number of infected individuals by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Worldwide, on July 20, it has been estimated above 160 million individuals infected by SARS-CoV-2. Moreover, it is found that only about 1 out of 11 infected individuals are detected. In an information context in which population-based seroepidemiological studies are not frequently available, this study shows a parsimonious alternative to provide estimates of the number of SARS-CoV-2 infected individuals. By comparing our estimates with those provided by the population-based seroepidemiological ENE-COVID study in Spain, we confirm the utility of our approach. Then, using a cross-country regression, we investigated if differences in detection rates are associated with differences in the cumulative number of deaths. The hypothesis investigated in this study is that higher levels of detection of SARS-CoV-2 infections can reduce the risk exposure of the susceptible population with a relatively higher risk of death. Our results show that, on average, detecting 5 instead of 35 percent of the infections is associated with multiplying the number of deaths by a factor of about 6. Using this result, we estimated that 120 days after the pandemic outbreak, if the US would have tested with the same intensity as South Korea, about 85,000 out of their 126,000 reported deaths could have been avoided.
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Affiliation(s)
- Carlos Villalobos
- Escuela de Ingeniería Comercial, Centro de Investigación en Economía Aplicada, Facultad de Economía y Negocios, Universidad de Talca, Talca, Chile
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Peirlinck M, Linka K, Costabal FS, Bhattacharya J, Bendavid E, Ioannidis JPA, Kuhl E. Visualizing the invisible: The effect of asymptomatic transmission on the outbreak dynamics of COVID-19. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020:2020.05.23.20111419. [PMID: 32869035 PMCID: PMC7457606 DOI: 10.1101/2020.05.23.20111419] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Understanding the outbreak dynamics of the COVID-19 pandemic has important implications for successful containment and mitigation strategies. Recent studies suggest that the population prevalence of SARS-CoV-2 antibodies, a proxy for the number of asymptomatic cases, could be an order of magnitude larger than expected from the number of reported symptomatic cases. Knowing the precise prevalence and contagiousness of asymptomatic transmission is critical to estimate the overall dimension and pandemic potential of COVID-19. However, at this stage, the effect of the asymptomatic population, its size, and its outbreak dynamics remain largely unknown. Here we use reported symptomatic case data in conjunction with antibody seroprevalence studies, a mathematical epidemiology model, and a Bayesian framework to infer the epidemiological characteristics of COVID-19. Our model computes, in real time, the time-varying contact rate of the outbreak, and projects the temporal evolution and credible intervals of the effective reproduction number and the symptomatic, asymptomatic, and recovered populations. Our study quantifies the sensitivity of the outbreak dynamics of COVID-19 to three parameters: the effective reproduction number, the ratio between the symptomatic and asymptomatic populations, and the infectious periods of both groups For nine distinct locations, our model estimates the fraction of the population that has been infected and recovered by Jun 15, 2020 to 24.15% (95% CI: 20.48%-28.14%) for Heinsberg (NRW, Germany), 2.40% (95% CI: 2.09%-2.76%) for Ada County (ID, USA), 46.19% (95% CI: 45.81%-46.60%) for New York City (NY, USA), 11.26% (95% CI: 7.21%-16.03%) for Santa Clara County (CA, USA), 3.09% (95% CI: 2.27%-4.03%) for Denmark, 12.35% (95% CI: 10.03%-15.18%) for Geneva Canton (Switzerland), 5.24% (95% CI: 4.84%-5.70%) for the Netherlands, 1.53% (95% CI: 0.76%-2.62%) for Rio Grande do Sul (Brazil), and 5.32% (95% CI: 4.77%-5.93%) for Belgium. Our method traces the initial outbreak date in Santa Clara County back to January 20, 2020 (95% CI: December 29, 2019 - February 13, 2020). Our results could significantly change our understanding and management of the COVID-19 pandemic: A large asymptomatic population will make isolation, containment, and tracing of individual cases challenging. Instead, managing community transmission through increasing population awareness, promoting physical distancing, and encouraging behavioral changes could become more relevant.
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Affiliation(s)
- Mathias Peirlinck
- Department of Mechanical Engineering, Stanford University School of Engineering, Stanford, California, United States
| | - Kevin Linka
- Department of Mechanical Engineering, Stanford University School of Engineering, Stanford, California, United States
| | - Francisco Sahli Costabal
- Department of Mechanical and Metallurgical Engineering and Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - Jay Bhattacharya
- Department of Medicine, Stanford University School of Medicine, Stanford, California, United States
| | - Eran Bendavid
- Department of Medicine, Stanford University School of Medicine, Stanford, California, United States
| | - John P A Ioannidis
- Department of Medicine, Stanford University School of Medicine, Stanford, California, United States
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California, United States
| | - Ellen Kuhl
- Department of Mechanical Engineering, Stanford University School of Engineering, Stanford, California, United States
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Backhaus A. Common Pitfalls in the Interpretation of COVID-19 Data and Statistics. INTER ECONOMICS 2020; 55:162-166. [PMID: 32536714 PMCID: PMC7276107 DOI: 10.1007/s10272-020-0893-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Policymakers, experts and the general public heavily rely on the data that are being reported in the context of the coronavirus pandemic. Daily data releases on confirmed COVID-19 cases and deaths provide information on the course of the pandemic.
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Affiliation(s)
- Andreas Backhaus
- Demographic Change and Aging, Federal Institute for Population Research, Friedrich-Ebert-Allee 4, 65185 Wiesbaden, Germany
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