After lifting the COVID-19 lockdown restrictions and opening businesses, screening is essential to prevent the spread of the virus. Group testing could be a promising candidate for screening to save time and resources. However, due to the high false-negative rate (FNR) of the RT-PCR diagnostic test, we should be cautious about using group testing because a group's false-negative result identifies all the individuals in a group as uninfected. Repeating the test is the best solution to reduce the FNR, and repeats should be integrated with the group-testing method to increase the sensitivity of the test. The simplest way is to replicate the test twice for each group (the 2Rgt method). In this paper, we present a new method for group testing (the groupMix method), which integrates two repeats in the test. Then we introduce the 2-stage sequential version of both the groupMix and the 2Rgt methods. We compare these methods analytically regarding the sensitivity and the average number of tests. The tradeoff between the sensitivity and the average number of tests should be considered when choosing the best method for the screening strategy. We applied the groupMix method to screening 263 people and identified 2 infected individuals by performing 98 tests. This method achieved a 63% saving in the number of tests compared to individual testing. Our experimental results show that in COVID-19 screening, the viral load can be low, and the group size should not be more than 6; otherwise, the FNR increases significantly. A web interface of the groupMix method is publicly available for laboratories to implement this method.

This dataset shows daily citywide counts of persons tested by nucleic acid amplification tests (NAAT, also known as a molecular test; e.g. a PCR test) for SARS-CoV-2 , counts of persons with positive tests, and the percent positivity. Also included is a calculation of the average percent positivity over a 7-day period. NAAT tests work through direct detection of the virus’s genetic material, and typically involve collecting a nasal swab. These tests are highly accurate and recommended for diagnosing current COVID-19 infection. After specimen collection, molecular tests are processed in a laboratory, and results are electronically reported to the New York State (NYS) Electronic Clinical Laboratory Results System (ECLRS). Test results for NYC residents are then sent electronically to NYC DOHMH. There is typically a lag of a few days between when a specimen is collected and when a result is reported to NYC DOHMH. Data is sourced from electronic laboratory reporting from NYS ECLRS. All identifying health information is excluded from the dataset.

Since the beginning of the COVID-19 pandemic, the virus has mutated. Variants from the United Kingdom, Brazil, and South Africa were identified. These variants of the coronavirus (COVID-19) also reached France and appeared more virulent in some regions. As of June 6, 2021, around 75 percent of positive PCR tests detected the British variant.

Despite of contact restrictions, population mobility remains the main reason for the spread of SARS-CoV-2. The state of Baden-Württemberg (BW), Germany, approved a model study in Tübingen (TÜMOD) to evaluate how mandatory rapid diagnostic tests (RDT) could reduce transmission. Between 16 March and 24 April 2021, approximately 165,000 residents and visitors to the city were screened for SARS CoV-2 infection using Abbott Panbio™ COVID-19 Antigen rapid test device. We assessed incidences and recorded epidemiological characteristics in a subset of 4,118 participants recruited at three of the nine testing stations. PCR tests were performed in RDT-positives to determine the positive predictive value (PPV), and circulating variants of SARS-CoV-2 were identified by whole-genome sequencing. 2,282 RDT-negative samples were tested by pooled PCR to calculate the false negative rate (FNR). Viral load was compared between variants. 116 (3%) participants were positive by RDT, and of these, 57 (49%) were positive by PCR, 55 (47%) were negative. This resulted in a PPV of 51%. Of the 57 positives, 52 SARS-CoV-2 genomes were successfully sequenced. Of these, 50 belonged to the B.1.1.7 lineage, which had a high viral load (average Ct = 19). Of the 2,282 RDT negatives tested, all were PCR negative (FNR 0%). At the end of TÜMOD, the incidence in Tübingen, which was initially lower, had reached the incidence in the state of BW. While it is difficult to assess the impact of TÜMOD on incidence independent of confounding factors, further studies are needed to identify the effect of close-meshed testing on infection rates.

Summary The total number of COVID-19 tests administered and the 7-day average percent positive rate in each Maryland jurisdiction. Description Testing volume data represent the total number of PCR COVID-19 tests electronically reported for Maryland residents; this count does not include test results submitted by labs and other clinical facilities through non-electronic means. The 7-day percent positive rate is a rolling average of each day’s positivity percentage. The percentage is calculated using the total number of tests electronically reported to MDH (by date of report) and the number of positive tests electronically reported to MDH (by date of report). Electronic lab reports from NEDDSS. Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

This dataset is historical only and ends at 5/7/2021. For more information, please see http://dev.cityofchicago.org/open%20data/data%20portal/2021/05/04/covid-19-testing-by-person.html. The recommended alternative dataset for similar data beyond that date is https://data.cityofchicago.org/Health-Human-Services/COVID-19-Daily-Testing-By-Test/gkdw-2tgv.

This is the source data for some of the metrics available at https://www.chicago.gov/city/en/sites/covid-19/home/latest-data.html.

For all datasets related to COVID-19, see https://data.cityofchicago.org/browse?limitTo=datasets&sortBy=alpha&tags=covid-19.

This dataset contains counts of people tested for COVID-19 and their results. This dataset differs from https://data.cityofchicago.org/d/gkdw-2tgv in that each person is in this dataset only once, even if tested multiple times. In the other dataset, each test is counted, even if multiple tests are performed on the same person, although a person should not appear in that dataset more than once on the same day unless he/she had both a positive and not-positive test.

Only Chicago residents are included based on the home address as provided by the medical provider.

Molecular (PCR) and antigen tests are included, and only one test is counted for each individual. Tests are counted on the day the specimen was collected. A small number of tests collected prior to 3/1/2020 are not included in the table.

Not-positive lab results include negative results, invalid results, and tests not performed due to improper collection. Chicago Department of Public Health (CDPH) does not receive all not-positive results.

Demographic data are more complete for those who test positive; care should be taken when calculating percentage positivity among demographic groups.

All data are provisional and subject to change. Information is updated as additional details are received.

Data Source: Illinois National Electronic Disease Surveillance System

More than 450 public health and clinical laboratories located throughout the United States participate in surveillance for severe acute respiratory virus coronavirus type 2 (SARS-CoV-2), the virus that causes COVID-19, through CDC's National Respiratory and Enteric Virus Surveillance System (NREVSS). The dataset contains a weekly summary of aggregate counts of the total SARS-CoV-2 tests and SARS-CoV-2 detections reported to NREVSS since March 14, 2020. These data are reported weekly on a voluntary basis. Clinical laboratories do not report demographic data through NREVSS. Testing practices may vary regionally, and the number of participating laboratories may change from year to year. Results can be changed for up to 2 years after the initial reporting week. However, discrepancies may be noted and updated at the discretion of the data stewards and key stakeholders.

While NREVSS strives to present the most precise estimates of respiratory viral trends with reporting burden minimized for participating laboratories, there are several inherent limitations to this surveillance system.

NREVSS does not collect patient-specific data or demographic information. Multiple samples may be collected from a single patient, so NREVSS results do not necessarily reflect the number of patients tested, nor do they directly reflect hospitalizations or deaths related to COVID-19.

Participating laboratories vary in size, testing capabilities, and areas served. Some institutions may receive and test samples from sites across a given state or even from multiple states. Without direct knowledge of the population base, NREVSS cannot be used to determine the prevalence or incidence of infection.

For more information on NREVSS and COVID-19 surveillance please visit: https://www.cdc.gov/surveillance/nrevss. These data appear starting May 25, 2023 on the CDC COVID Data Tracker at the following URLs: https://covid.cdc.gov/covid-data-tracker/#trends ; https://covid.cdc.gov/covid-data-tracker/#cases.

NREVSS data are reported at the national and HHS regional levels. The ten (10) U.S. Department of HHS regions are defined here: https://www.hhs.gov/about/agencies/iea/regional-offices/index.html.

The data represent SARS-CoV-2 Nucleic Acid Amplification Test (NAAT) results, which include reverse transcriptase-polymerase chain reaction (RT-PCR) tests from a voluntary, sentinel network of participating laboratories in the United States, including clinical, public health and commercial laboratories (https://www.cdc.gov/surveillance/nrevss/labs/index.html).

These data exclude antigen, antibody, and at-home test results.

All data are provisional and subject to change. Reporting is less complete for the past 1 week, and more complete (>90%) for the period 2 weeks earlier.

There are data from all states across the 10 HHS regions. Because the data are from a sentinel network of laboratories, however, results may vary geographically. The data do not include all test results within a jurisdiction and therefore do not reflect all SARS-CoV-2 NAATs administered in the United States.

Percent positivity is one of the surveillance metrics used to monitor COVID-19 transmission over time and by area. Percent positivity is calculated by dividing the number of positive NAATs by the total number of NAATs administered, then multiplying by 100 [(# of positive NAAT tests / total NAAT tests) x 100].

The data represent laboratory tests performed, not individual (deduplicated) results in people. In the table and upon hovering on the map, the total test counts in the data reflect the latest data reported from NREVSS laboratories and may not match the data presented by various jurisdictions.

On May 11, 2023, CDC discontinued utilizing the COVID electronic laboratory reporting (CELR) platform as the primary laboratory source of COVID-19 results. These data are archived at health.data.gov.

For more information about NREVSS, please see: https://www.cdc.gov/surveillance/nrevss/index.html.

NOTE: This dataset is no longer being updated as of 4/27/2023. It is retired and no longer included in public COVID-19 data dissemination.

See this link for more information https://imap.maryland.gov/pages/covid-data

Summary The daily cumulative total of COVID-19 tests administered in Maryland and the average percent daily positive rate.

Description Testing volume data represent the static daily total of PCR COVID-19 tests electronically reported for Maryland residents; this count does not include test results submitted by labs and other clinical facilities through non-electronic means. The percent positive rate is a seven-day rolling average of positive results as a percentage of all tests. Data are lectronic lab reports from NEDDSS.

Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

This dataset includes aggregated weekly influenza virus laboratory data that the Chicago Department of Public Health (CDPH) uses to monitor influenza activity and assess which influenza types and subtypes are circulating in Chicago. The data represent weekly positive influenza PCR tests voluntarily reported by network of several hospital laboratories in Chicago as well as two commercial laboratories serving Chicago facilities. The data includes positive test results by influenza type (influenza A and influenza B) as well as influenza A subtype (H3N2, H1N1pdm09) when available. These data do not include patient demographic or geographic information and represent both Chicago and non-Chicago residents tested by the reporting facility. Influenza laboratory data are available from the 2010-2011 season to present.

Two percentage fields are available in the dataset. Percentages are calculated for each characteristic group as follows:

The percentage of influenza types is calculated as the total number of positives tests for each influenza type divided by the total number of positive influenza tests reported (e.g., Influenza A/Influenza Positive). The percentage fields describe the percent of positive tests by influenza type each week (count_pct) and for the entire season to date (count_cum_pct).

The percentage of influenza A subtypes is calculated as the total number of positive tests for each influenza A subtype divided by the total number of positive influenza A tests reported (Influenza A Subtype/Influenza A). The percentage fields describe the percent of influenza A positive tests by subtype each week (count_pct) and for the entire season to date (count_cum_pct).

The percentage for characteristic group ‘Total Positive’ will always be 100% and does not represent influenza test positivity. For data on influenza test positivity see: https://data.cityofchicago.org/Health-Human-Services/Influenza-COVID-19-RSV-and-Other-Respiratory-Virus/qgdz-d5m4/about_data.

All data are provisional and subject to change. Information is updated as additional details are received. At any given time, this dataset reflects data currently known to CDPH. Numbers in this dataset may differ from other public sources.

Deprecated as of 12/3/2021. This table will no longer be updated.SummaryThe 7-day average percent positive rate for COVID-19 tests adminstered among Marylanders under 35 years of age and over 35 years of age.DescriptionTesting volume data represent the static daily total of PCR COVID-19 tests electronically reported for Maryland residents; this count does not include test results submitted by labs and other clinical facilities through non-electronic means. The 7-day percent postive rate is a rolling average of each day’s positivity percentage. The percentage is calculated using the total number of tests electronically reported to MDH (by date of report) and the number of positive tests electronically reported to MDH (by date of report).COVID-19 is a disease caused by a respiratory virus first identified in Wuhan, Hubei Province, China in December 2019. COVID-19 is a new virus that hasn't caused illness in humans before. Worldwide, COVID-19 has resulted in thousands of infections, causing illness and in some cases death. Cases have spread to countries throughout the world, with more cases reported daily. The Maryland Department of Health reports daily on COVID-19 cases by county.

ObjectiveDuring the follow-up of patients recovered from coronavirus disease 2019 (COVID-19) in the quarantine and observation period, some of the cured patients showed positive results again. The recurrent positive RT-PCR test results drew widespread concern. We observed a certain number of cured COVID-19 patients with positive RT-PCR test results and try to analyze the factors that caused the phenomenon.MethodsWe conducted an observational study in COVID-19 patients discharged from 6 rehabilitation stations in Wuhan, China. All observed subjects met the criteria for hospital discharge and were in quarantine. Data regarding age, sex, body mass index (BMI), course of disease, comorbidity, smoking status and alcohol consumption, symptoms in and out of quarantine, and intervention were collected from the subjects’ medical records and descriptively analyzed. The main outcome of this study was the RT-PCR test result of the observed subjects at the end of quarantine (negative or positive). Logistic regression analysis was used to identify the influencing factors related to recurrent positive RT-PCR test results.ResultsIn this observational study, 420 observed subjects recovered from COVID-19 were included. The median age was 56 years, 63.6% of the subjects were above 50 years old, and 50.7% (213/420) were female. The most common comorbidities were hypertension [26.4% (111/420)], hyperlipidemia [10.7% (45/420)], and diabetes [10.5% (44/420)]. 54.8% (230/420) manifested one or more symptoms at the beginning of the observation period, the most common symptoms were cough [27.6% (116/420)], shortness of breath 23.8% (100/420)], and fatigue [16.2% (68/420)], with fever rare [2.6% (11/420)]. A total of 325 subjects were exposed to comprehensive intervention; 95 subjects were absence of intervention. The recurrence rate of positive RT-PCR test results with comprehensive intervention was 2.8% (9/325), and that with no intervention was 15.8% (15/95). The results of logistic regression analysis showed that after adjusted for factors such as age, sex, and comorbidity and found out that comprehensive intervention was correlated with the recurrent positive RT-PCR test results. There was appreciably less recurrence in the comprehensive intervention group.ConclusionsThe factors related to positive RT-PCR test results in observed subjects recovered from COVID-19 were age, comorbidity, and comprehensive intervention, among which comprehensive intervention might be a protective factor.Clinical Trial RegistrationChictr.org.cn, identifier ChiCTR2000030747.

Background: To control COVID-19 pandemic is of critical importance to the global public health. To capture the prevalence in an accurate and timely manner and to understand the mode of nosocomial infection are essential for its preventive measure.

Methods: We recruited 685 healthcare workers (HCW's) at Tokyo Shinagawa Hospital prior to the vaccination with COVID-19 vaccine. Sera of the subjects were tested by assays for the titer of IgG against S protein's receptor binding domain (IgG (RBD)) or IgG against nucleocapsid protein (IgG (N)) of SARS-CoV-2. Together with PCR data, the positive rates by these methods were evaluated.

Results: Overall positive rates among HCW's by PCR, IgG (RBD), IgG (N) with a cut-off of 1.4 S/C (IgG (N)1.4), and IgG (N) with a cut-off of 0.2 S/C (IgG (N)0.2) were 3.5%, 9.5%, 6.1%, and 27.7%, respectively. Positive rates of HCW's working in COVID-19 ward were significantly higher than those of HCW's working in non-COVID-19 ward by all the four methods. Concordances of IgG (RBD), IgG (N)1.4, and IgG (N)0.2 against PCR were 97.1%, 71.4%, and 88.6%, respectively. By subtracting the positive rates of PCR from that of IgG (RBD), the rate of overall silent infection and that of HCW's in COVID-19 ward were estimated to be 6.0% and 21.1%, respectively.

Conclusions: For the prevention of nosocomial infection of SARS-CoV-2, identification of silent infection is essential. For the detection of ongoing infection, periodical screening with IgG (RBD) in addition to PCR would be an effective measure. For the surveillance of morbidity in the population, on the other hand, IgG (N)0.2 could be the most reliable indicator among the three serological tests.

Percent of tests positive for a pathogen is one of the surveillance metrics used to monitor respiratory pathogen transmission over time. The percent of tests positive is calculated by dividing the number of positive tests by the total number of tests administered, then multiplying by 100 [(# of positive tests/total tests) x 100]. These data include percent of tests positive values for the detection of severe acute respiratory virus coronavirus type 2 (SARS-CoV-2), the virus that causes COVID-19 and Respiratory syncytial virus (RSV) reported to the National Respiratory and Enteric Virus Surveillance System (NREVSS), a sentinel network of laboratories located through the US, includes clinical, public health and commercial laboratories; additional information available at: https://www.cdc.gov/surveillance/nrevss/index.html. Influenza results include clinical laboratory test results from NREVSS and U.S. World Health Organization collaborating laboratories; more details about influenza virologic surveillance are available here: https://www.cdc.gov/flu/weekly/overview.html.

Data represent calculations based on laboratory tests performed, not individual people tested. RSV and COVID-19 are limited to nucleic acid amplification tests (NAATs), also listed as polymerase chain reaction tests (PCR). Participating laboratories report weekly to CDC the total number of RSV tests performed that week and the number of those tests that were positive. The RSV trend graphs display the national average of the weekly % test positivity for the current, previous, and following weeks in accordance with the recommendations for assessing RSV trends by percent (https://academic.oup.com/jid/article/216/3/345/3860464).

All data are provisional and subject to change.

Priorities for polymerase chain reaction (PCR) testing based on a PCR test-positivity rate of 30%.

The table summarizes the outcome of each of the 20,028 One-Step Real-Time RT-PCR reaction tests for the detection of SARS-CoV-2, carried out between May and July 2020. (XLSX)

Concordance rate between the results of PCR testing and SARS-CoV-2 IgM or IgG serological testing.

NOTE: This dataset has been retired and marked as historical-only. Only Chicago residents are included based on the home ZIP Code as provided by the medical provider. If a ZIP was missing or was not valid, it is displayed as "Unknown". Cases with a positive molecular (PCR) or antigen test are included in this dataset. Cases are counted based on the week the test specimen was collected. For privacy reasons, until a ZIP Code reaches five cumulative cases, both the weekly and cumulative case counts will be blank. Therefore, summing the “Cases - Weekly” column is not a reliable way to determine case totals. Deaths are those that have occurred among cases based on the week of death. For tests, each test is counted once, based on the week the test specimen was collected. Tests performed prior to 3/1/2020 are not included. Test counts include multiple tests for the same person (a change made on 10/29/2020). PCR and antigen tests reported to Chicago Department of Public Health (CDPH) through electronic lab reporting are included. Electronic lab reporting has taken time to onboard and testing availability has shifted over time, so these counts are likely an underestimate of community infection. The “Percent Tested Positive” columns are calculated by dividing the number of positive tests by the number of total tests . Because of the data limitations for the Tests columns, such as persons being tested multiple times as a requirement for employment, these percentages may vary in either direction from the actual disease prevalence in the ZIP Code. All data are provisional and subject to change. Information is updated as additional details are received. To compare ZIP Codes to Chicago Community Areas, please see http://data.cmap.illinois.gov/opendata/uploads/CKAN/NONCENSUS/ADMINISTRATIVE_POLITICAL_BOUNDARIES/CCAzip.pdf. Both ZIP Codes and Community Areas are also geographic datasets on this data portal. Data Source: Illinois National Electronic Disease Surveillance System, Cook County Medical Examiner’s Office, Illinois Vital Records, American Community Survey (2018)

Serological testing is recommended to support the detection of undiagnosed coronavirus disease 2019 (COVID-19) cases. However, the performance of serological assays has not been sufficiently evaluated. Hence, the performance of six severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binding antibody assays [three chemiluminescence (CLIAs) and three lateral flow immunoassays (LFIAs)] and a surrogate virus neutralization test (sVNT) was analyzed in a total of 988 serum samples comprising 389 COVID-19-positives and 599 COVID-19-negatives. The overall diagnostic sensitivities of CLIAs and LFIAs ranged from 54.2 to 56.6% and from 56.3 to 64.3%, respectively. The overall diagnostic specificities of CLIAs and LFIAs ranged from 98.2 to 99.8% and from 97.3 to 99.0%, respectively. In the symptomatic group (n = 321), the positivity rate increased by over 80% in all assays > 14 days after symptom onset. In the asymptomatic group (n = 68), the positivity rate increased by over 80% in all assays > 21 days after initial RT-PCR detection. In LFIAs, negatively interpreted trace bands accounted for the changes in test performance. Most false-positive results were weak or trace reactions and showed negative results in additional sVNT. For six binding antibody assays, the overall agreement percentages ranged from 91.0 to 97.8%. The median inhibition activity of sVNT was significantly higher in the symptomatic group than in the asymptomatic group (50.0% vs. 29.2%; p < 0.0001). The median times to seropositivity in the symptomatic group were 9.7 days for CLIA-IgG, 9.2 and 9.8 days for two CLIAs-Total (IgM + IgG), 7.7 days for LFIA-IgM, 9.2 days for LFIA-IgG, and 8.8 days for sVNT-IgG, respectively. There was a strong positive correlation between the quantitative results of the four binding antibody assays and sVNT with Spearman ρ-values ranging from 0.746 to 0.854. In particular, when using LFIAs, we recommend using more objective interpretable assays or establishing a band interpretation system for each laboratory, accompanied by observer training. We also anticipate that sVNT will play an essential role in SARS-CoV-2 antibody testing and become the practical routine neutralizing antibody assay.

Serological testing is recommended to support the detection of undiagnosed coronavirus disease 2019 (COVID-19) cases. However, the performance of serological assays has not been sufficiently evaluated. Hence, the performance of six severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binding antibody assays [three chemiluminescence (CLIAs) and three lateral flow immunoassays (LFIAs)] and a surrogate virus neutralization test (sVNT) was analyzed in a total of 988 serum samples comprising 389 COVID-19-positives and 599 COVID-19-negatives. The overall diagnostic sensitivities of CLIAs and LFIAs ranged from 54.2 to 56.6% and from 56.3 to 64.3%, respectively. The overall diagnostic specificities of CLIAs and LFIAs ranged from 98.2 to 99.8% and from 97.3 to 99.0%, respectively. In the symptomatic group (n = 321), the positivity rate increased by over 80% in all assays > 14 days after symptom onset. In the asymptomatic group (n = 68), the positivity rate increased by over 80% in all assays > 21 days after initial RT-PCR detection. In LFIAs, negatively interpreted trace bands accounted for the changes in test performance. Most false-positive results were weak or trace reactions and showed negative results in additional sVNT. For six binding antibody assays, the overall agreement percentages ranged from 91.0 to 97.8%. The median inhibition activity of sVNT was significantly higher in the symptomatic group than in the asymptomatic group (50.0% vs. 29.2%; p < 0.0001). The median times to seropositivity in the symptomatic group were 9.7 days for CLIA-IgG, 9.2 and 9.8 days for two CLIAs-Total (IgM + IgG), 7.7 days for LFIA-IgM, 9.2 days for LFIA-IgG, and 8.8 days for sVNT-IgG, respectively. There was a strong positive correlation between the quantitative results of the four binding antibody assays and sVNT with Spearman ρ-values ranging from 0.746 to 0.854. In particular, when using LFIAs, we recommend using more objective interpretable assays or establishing a band interpretation system for each laboratory, accompanied by observer training. We also anticipate that sVNT will play an essential role in SARS-CoV-2 antibody testing and become the practical routine neutralizing antibody assay.