Dataset Information

Epidemiological study using IgM and IgG antibody titers against SARS-CoV-2 in The University of Tokyo, Japan (UT-CATS).

ABSTRACT:

Introduction

The worldwide pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has continued to date. Given that some of the patients with coronavirus disease 2019 (COVID-19) are asymptomatic, antibody tests are useful to determine whether there is a previous infection with SARS-CoV-2. In this study, we measured IgM and IgG antibody titers against SARS-CoV-2 in the serum of asymptomatic healthy subjects in The University of Tokyo, Japan.

Methods

From June 2020, we recruited participants, who were students, staff, and faculty members of The University of Tokyo in the project named The University of Tokyo COVID-19 Antibody Titer Survey (UT-CATS). Following blood sample collection, participants were required to answer an online questionnaire about their social and health information. We measured IgG and IgM titers against SARS-CoV-2 using iFlash-SARS-CoV-2 IgM and IgG detection kit which applies a chemiluminescent immunoassay (CLIA) for the qualitative detection.

Results

There were 6609 volunteers in this study. After setting the cutoff value at 10 AU/mL, 32 (0.48%) were positive for IgG and 16 (0.24%) for IgM. Of six participants with a history of COVID-19, five were positive for IgG, whereas all were negative for IgM. The median titer of IgG was 0.40 AU/mL and 0.39 AU/mL for IgM. Both IgG and IgM titers were affected by gender, age, smoking status, and comorbidities.

Conclusions

Positive rates of IgG and IgM titers were relatively low in our university. Serum levels of these antibodies were affected by several factors, which might affect the clinical course of COVID-19.

SUBMITTER: Mitani A

PROVIDER: S-EPMC8196331 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundCOVID-19 currently poses a global public health threat. Although Tokyo, Japan, is no exception to this, it was initially affected by only a small-level epidemic. Nevertheless, medical collapse nearly happened since no predictive methods were available to assess infection counts. A standard susceptible-infectious-removed (SIR) epidemiological model has been widely used, but its applicability is limited often to the early phase of an epidemic in the case of a large collective population. A full numerical simulation of the entire period from beginning until end would be helpful for understanding COVID-19 trends in (separate) counts of inpatient and infectious cases and can also aid the preparation of hospital beds and development of quarantine strategies.ObjectiveThis study aimed to develop an epidemiological model that considers the isolation period to simulate a comprehensive trend of the initial epidemic in Tokyo that yields separate counts of inpatient and infectious cases. It was also intended to induce important corollaries of governing equations (ie, effective reproductive number) and equations for the final count.MethodsTime-series data related to SARS-CoV-2 from February 28 to May 23, 2020, from Tokyo and antibody testing conducted by the Japanese government were adopted for this study. A novel epidemiological model based on a discrete delay differential equation (apparent time-lag model [ATLM]) was introduced. The model can predict trends in inpatient and infectious cases in the field. Various data such as daily new confirmed cases, cumulative infections, inpatients, and PCR (polymerase chain reaction) test positivity ratios were used to verify the model. This approach also derived an alternative formulation equivalent to the standard SIR model.ResultsIn a typical parameter setting, the present ATLM provided 20% less infectious cases in the field compared to the standard SIR model prediction owing to isolation. The basic reproductive number was inferred as 2.30 under the condition that the time lag T from infection to detection and isolation is 14 days. Based on this, an adequate vaccine ratio to avoid an outbreak was evaluated for 57% of the population. We assessed the date (May 23) that the government declared a rescission of the state of emergency. Taking into consideration the number of infectious cases in the field, a date of 1 week later (May 30) would have been most effective. Furthermore, simulation results with a shorter time lag of T=7 and a larger transmission rate of α=1.43α0 suggest that infections at large should reduce by half and inpatient numbers should be similar to those of the first wave of COVID-19.ConclusionsA novel mathematical model was proposed and examined using SARS-CoV-2 data for Tokyo. The simulation agreed with data from the beginning of the pandemic. Shortening the period from infection to hospitalization is effective against outbreaks without rigorous public health interventions and control.