Inferring Incidence of Unreported SARS-CoV-2 Infections Using Seroprevalence of Open Reading Frame 8 Antigen, Hong Kong

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Abstract

We tested seroprevalence of open reading frame 8 antigens to infer the number of unrecognized SARS-CoV-2 Omicron infections in Hong Kong during 2022. We estimate 33.6% of the population was infected, 72.1% asymptomatically. Surveillance and control activities during large-scale outbreaks should account for potentially substantial undercounts.

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Prevalence of Asymptomatic SARS-CoV-2 Infection

Daniel P Oran, Eric Topol (2020)

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread rapidly throughout the world since the first cases of coronavirus disease 2019 (COVID-19) were observed in December 2019 in Wuhan, China. It has been suspected that infected persons who remain asymptomatic play a significant role in the ongoing pandemic, but their relative number and effect have been uncertain. The authors sought to review and synthesize the available evidence on asymptomatic SARS-CoV-2 infection. Asymptomatic persons seem to account for approximately 40% to 45% of SARS-CoV-2 infections, and they can transmit the virus to others for an extended period, perhaps longer than 14 days. Asymptomatic infection may be associated with subclinical lung abnormalities, as detected by computed tomography. Because of the high risk for silent spread by asymptomatic persons, it is imperative that testing programs include those without symptoms. To supplement conventional diagnostic testing, which is constrained by capacity, cost, and its one-off nature, innovative tactics for public health surveillance, such as crowdsourcing digital wearable data and monitoring sewage sludge, might be helpful.

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A New Framework and Software to Estimate Time-Varying Reproduction Numbers During Epidemics

Anne Cori, Neil M Ferguson, Christophe Fraser … (2014)

Abstract The quantification of transmissibility during epidemics is essential to designing and adjusting public health responses. Transmissibility can be measured by the reproduction number R, the average number of secondary cases caused by an infected individual. Several methods have been proposed to estimate R over the course of an epidemic; however, they are usually difficult to implement for people without a strong background in statistical modeling. Here, we present a ready-to-use tool for estimating R from incidence time series, which is implemented in popular software including Microsoft Excel (Microsoft Corporation, Redmond, Washington). This tool produces novel, statistically robust analytical estimates of R and incorporates uncertainty in the distribution of the serial interval (the time between the onset of symptoms in a primary case and the onset of symptoms in secondary cases). We applied the method to 5 historical outbreaks; the resulting estimates of R are consistent with those presented in the literature. This tool should help epidemiologists quantify temporal changes in the transmission intensity of future epidemics by using surveillance data.

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ORF8 and ORF3b antibodies are accurate serological markers of early and late SARS-CoV-2 infection

Asmaa Hachim, Niloufar Kavian, Carolyn A. Cohen … (2020)

The SARS-CoV-2 virus emerged in December 2019 and has caused a worldwide pandemic due to the lack of any pre-existing immunity. Accurate serology testing is urgently needed to help diagnose infection, determine past exposure of populations and assess the response to a future vaccine. The landscape of antibody responses to SARS-CoV-2 is unknown. In this study, we utilized the luciferase immunoprecipitation system to assess the antibody responses to 15 different SARS-CoV-2 antigens in patients with COVID-19. We identified new targets of the immune response to SARS-CoV-2 and show that nucleocapsid, open reading frame (ORF)8 and ORF3b elicit the strongest specific antibody responses. ORF8 and ORF3b antibodies, taken together as a cluster of points, identified 96.5% of COVID-19 samples at early and late time points of disease with 99.5% specificity. Our findings could be used to develop second-generation diagnostic tests to improve serological assays for COVID-19 and are important in understanding pathogenicity.

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Author and article information

Journal

Journal ID (nlm-ta): Emerg Infect Dis

Journal ID (iso-abbrev): Emerg Infect Dis

Journal ID (publisher-id): EID

Title: Emerging Infectious Diseases

Publisher: Centers for Disease Control and Prevention

ISSN (Print): 1080-6040

ISSN (Electronic): 1080-6059

Publication date (Print): February 2024

Volume: 30

Issue: 2

Pages: 325-328

Affiliations

[1]Author affiliations: School of Public Health, Tianjin Medical University, Tianjin, China (S. Zhao); JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China (S. Zhao, C.K.P. Mok, Y.S. Tang, C. Chen, Y. Sun, K.C. Chong); Centre for Health Systems and Policy Research, Chinese University of Hong Kong, Hong Kong, China (S. Zhao, K.C. Chong); CUHK Shenzhen Research Institute, Shenzhen, China (S. Zhao, K.C. Chong); Li Ka Shing Institute of Health Sciences, Chinese University of Hong Kong, Hong Kong, China (C.K.P. Mok, Y.S. Tang, C. Chen, Y. Sun); S.H. Ho Research Centre for Infectious Diseases, Chinese University of Hong Kong, Hong Kong, China (C.K.P. Mok, D.S.C. Hui)

Author notes

Address for correspondence: Ka Chun Chong, JC School of Public Health and Primary Care, Prince of Wales Hospital, 30-32 Ngan Shing Street, Shatin, New Territories, Hong Kong, China; email: marc@ 123456cuhk.edu.hk ; David SC Hui, Department of Medicine and Therapeutics, Prince of Wales Hospital, 30-32 Ngan Shing Street, Shatin, New Territories, Hong Kong, China; email: dschui@ 123456cuhk.edu.hk

Article

Publisher ID: 23-1332

DOI: 10.3201/eid3002.231332

PMC ID: 10826773

PubMed ID: 38167176

SO-VID: e42ff352-054a-4d06-8663-8a9fa7e95822

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Emerging Infectious Diseases is a publication of the U.S. Government. This publication is in the public domain and is therefore without copyright. All text from this work may be reprinted freely. Use of these materials should be properly cited.

Inferring Incidence of Unreported SARS-CoV-2 Infections Using Seroprevalence of Open Reading Frame 8 Antigen, Hong Kong

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Abstract

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Novel Coronavirus Disease COVID-19

Most cited references 9

Prevalence of Asymptomatic SARS-CoV-2 Infection

A New Framework and Software to Estimate Time-Varying Reproduction Numbers During Epidemics

ORF8 and ORF3b antibodies are accurate serological markers of early and late SARS-CoV-2 infection

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