Clinical sensitivity and interpretation of PCR and serological COVID-19 diagnostics for patients presenting to the hospital

Addressing label noise for electronic health records: insights from computer vision for tabular data

The analysis of extensive electronic health records (EHR) datasets often calls for automated solutions, with machine learning (ML) techniques, including deep learning (DL), taking a lead role. One common task involves categorizing EHR data into predefined groups. However, the vulnerability of EHRs to noise and errors stemming from data collection processes, as well as potential human labeling errors, poses a significant risk. This risk is particularly prominent during the training of DL models, where the possibility of overfitting to noisy labels can have serious repercussions in healthcare. Despite the well-documented existence of label noise in EHR data, few studies have tackled this challenge within the EHR domain. Our work addresses this gap by adapting computer vision (CV) algorithms to mitigate the impact of label noise in DL models trained on EHR data. Notably, it remains uncertain whether CV methods, when applied to the EHR domain, will prove effective, given the substantial divergence between the two domains. We present empirical evidence demonstrating that these methods, whether used individually or in combination, can substantially enhance model performance when applied to EHR data, especially in the presence of noisy/incorrect labels. We validate our methods and underscore their practical utility in real-world EHR data, specifically in the context of COVID-19 diagnosis. Our study highlights the effectiveness of CV methods in the EHR domain, making a valuable contribution to the advancement of healthcare analytics and research.

Determination of prognostic markers for COVID-19 disease severity using routine blood tests and machine learning

The need for the identification of risk factors associated to COVID-19 disease severity remains urgent. Patients' care and resource allocation can be potentially different and are defined based on the current classification of disease severity. This classification is based on the analysis of clinical parameters and routine blood tests, which are not standardized across the globe. Some laboratory test alterations have been associated to COVID-19 severity, although these data are conflicting partly due to the different methodologies used across different studies. This study aimed to construct and validate a disease severity prediction model using machine learning (ML). Seventy-two patients admitted to a Brazilian hospital and diagnosed with COVID-19 through RT-PCR and/or ELISA, and with varying degrees of disease severity, were included in the study. Their electronic medical records and the results from daily blood tests were used to develop a ML model to predict disease severity. Using the above data set, a combination of five laboratorial biomarkers was identified as accurate predictors of COVID-19 severe disease with a ROC-AUC of 0.80 ​±​ 0.13. Those biomarkers included prothrombin activity, ferritin, serum iron, ATTP and monocytes. The application of the devised ML model may help rationalize clinical decision and care.

Testing for SARS-CoV-2: lessons learned and current use cases

SUMMARYThe emergence and worldwide dissemination of SARS-CoV-2 required both urgent development of new diagnostic tests and expansion of diagnostic testing capacity on an unprecedented scale. The rapid evolution of technologies that allowed testing to move out of traditional laboratories and into point-of-care testing centers and the home transformed the diagnostic landscape. Four years later, with the end of the formal public health emergency but continued global circulation of the virus, it is important to take a fresh look at available SARS-CoV-2 testing technologies and consider how they should be used going forward. This review considers current use case scenarios for SARS-CoV-2 antigen, nucleic acid amplification, and immunologic tests, incorporating the latest evidence for analytical/clinical performance characteristics and advantages/limitations for each test type to inform current debates about how tests should or should not be used.

Mitigating machine learning bias between high income and low-middle income countries for enhanced model fairness and generalizability

Collaborative efforts in artificial intelligence (AI) are increasingly common between high-income countries (HICs) and low- to middle-income countries (LMICs). Given the resource limitations often encountered by LMICs, collaboration becomes crucial for pooling resources, expertise, and knowledge. Despite the apparent advantages, ensuring the fairness and equity of these collaborative models is essential, especially considering the distinct differences between LMIC and HIC hospitals. In this study, we show that collaborative AI approaches can lead to divergent performance outcomes across HIC and LMIC settings, particularly in the presence of data imbalances. Through a real-world COVID-19 screening case study, we demonstrate that implementing algorithmic-level bias mitigation methods significantly improves outcome fairness between HIC and LMIC sites while maintaining high diagnostic sensitivity. We compare our results against previous benchmarks, utilizing datasets from four independent United Kingdom Hospitals and one Vietnamese hospital, representing HIC and LMIC settings, respectively.

An indirect ELISA for detecting anti-SARS-CoV-2 antibodies in human sera using a baculovirus-expressed recombinant nucleocapsid antigen

This study focuses on the development and initial assessment of an indirect IgG enzyme-linked immunosorbent assay (ELISA) specifically designed to detect of anti-SARS-CoV-2 antibodies. The unique aspect of this ELISA method lies in its utilization of a recombinant nucleocapsid (N) antigen, produced through baculovirus expression in insect cells. Our analysis involved 292 RT-qPCR confirmed positive serum samples and 54 pre-pandemic healthy controls. The process encompassed cloning, expression, and purification of the SARS-CoV-2 N gene in insect cells, with the resulted purified protein employed in our ELISA tests. Statistical analysis yielded an Area Under the Curve of 0.979, and the optimized cut-off exhibited 92 % sensitivity and 94 % specificity. These results highlight the ELISA's potential for robust and reliable serological detection of SARS-CoV-2 antibodies. Further assessments, including a larger panel size, reproducibility tests, and application in diverse populations, could enhance its utility as a valuable biotechnological solution for diseases surveillance.

Trace detection of canine distemper virus based on Michelson-interferometer sensing probe

A single-mode-fiber (SMF)-multimode-fiber (MMF)-tri-core-fiber (TCF) Michelson probe structure is proposed for trace detection of canine distemper virus (CDV). One end of the TCF is cut flat and fused with the multimode fiber, and the other end is coated with a silver film to enhance the reflection, and an optic-fiber sensing probe with SMF-MMF-TCF structure is obtained. The (PDDA/PSS)3 multilayer film is modified on the surface of the fiber by layer-by-layer self-assembly method as a polyelectrolyte binder to immobilize CDV antibodies to form a (PDDA/PSS)3 /CDV antibody composite membrane for specific detection of CDV antigens. The response-recovery test of the sensor is performed to verify its repeatability. The detection limit, the sensitivity, and the linear fitting degree for CDV antigen are 0.1236 pg/mL, 1.1776 dB/(pg/mL), and 0.9899, respectively. At the same time, the stability, selectivity, and clinical samples of the sensors were also verified.

Effectiveness of BNT162b2 and CoronaVac vaccines in preventing SARS-CoV-2 Omicron infections, hospitalizations, and severe complications in the pediatric population in Hong Kong: a case-control study

Severe COVID-19 appears to be disproportionately more common in children and adolescents since the emergence of Omicron. More evidence regarding vaccine effectiveness (VE) is urgently needed to assist policymakers in making decisions and minimize vaccine hesitancy among the public. This was a case-control study in the pediatric population using data extracted from the electronic health records database in Hong Kong. Individuals aged 3-17 with COVID-19 confirmed by polymerase chain reaction were included in the study. Each case was matched with up to 10 controls based on age, gender, and index date (within 3 calendar days). The VE of BNT162b2 and CoronaVac in preventing COVID-19, hospitalizations, and severe outcomes were estimated using conditional logistic regression adjusted by patients' comorbidities and medication history during the outbreak from January to August 2022. A total of 36,434 COVID-19 cases, 2231 COVID-19-related hospitalizations, and 1918 severe COVID-19 cases were matched to 109,004, 21,788, and 18,823 controls, respectively. Compared to the unvaccinated group, three doses of BNT162b2 or CoronaVac was associated with reduced risk of infection [VE: BNT162b2: 56.0% (95% CI: 49.6-61.6), CoronaVac: 39.4% (95% CI: 25.6-50.6)], hospitalization [VE: BNT162b2: 58.9% (95% CI: 36.1-73.6), CoronaVac: 51.7% (11.6-73.6)], and severe outcomes [VE: BNT162b2: 60.2% (95% CI: 33.7-76.1), CoronaVac: 42.2% (95% CI: -6.2-68.6)]. Our findings showed that three doses of BNT162b2 or CoronaVac was effective in preventing COVID-19, hospitalizations, and severe outcomes among the pediatric population during Omicron-dominant pandemic, which was further enhanced after a booster dose.

Deep reinforcement learning for multi-class imbalanced training: applications in healthcare

With the rapid growth of memory and computing power, datasets are becoming increasingly complex and imbalanced. This is especially severe in the context of clinical data, where there may be one rare event for many cases in the majority class. We introduce an imbalanced classification framework, based on reinforcement learning, for training extremely imbalanced data sets, and extend it for use in multi-class settings. We combine dueling and double deep Q-learning architectures, and formulate a custom reward function and episode-training procedure, specifically with the capability of handling multi-class imbalanced training. Using real-world clinical case studies, we demonstrate that our proposed framework outperforms current state-of-the-art imbalanced learning methods, achieving more fair and balanced classification, while also significantly improving the prediction of minority classes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10994-023-06481-z.

Monitoring of antibody levels in healthcare workers after inactivated coronavirus disease 19 vaccination

OBJECTIVE

Because of the coronavirus disease 19 pandemic, studies on vaccination are being conducted in our country as well as across the world. In this study, the antibody levels in healthcare workers vaccinated with two doses of inactivated vaccine and the factors affecting these levels were investigated.

METHODS

Randomly selected volunteers from healthcare workers, who had been vaccinated with two doses of inactivated vaccine in January to February 2021, were included in the study. Blood samples were drawn twice, 1 month and 6 months after the second dose vaccine (CoronaVac:Sinovac Life Science Co, Ltd, Beijing, China). The antibody levels were determined by the chemiluminescence microparticle immunoassay method using kits for quantitative detection of immunoglobulin class G antibodies to severe acute respiratory syndrome coronavirus 2.

RESULTS

The mean antibody levels of 129 volunteers were 1232.5 (min: 103 to max: 7151) AU/mL in the first month and 403.5 (min: 23 to max: 4963) AU/mL in the sixth month. According to the survey results, 91 (71%) volunteers had not been diagnosed with coronavirus disease 19 before vaccination. The antibody levels 1 month and 6 months after the second dose of vaccination were significantly higher in those who had been diagnosed with coronavirus disease 19 before vaccination than in those who had not. It was found that age, gender, fast food, or healthy nutrition had no effect on antibody levels.

CONCLUSION

Vaccines are very important both to protect against coronavirus disease 19 and to experience only a mild form of the disease. Immunoglobulin class G levels formed after vaccination may be affected by many factors and may decrease over time.

Influence of bovine and human serum albumin on the binding kinetics of biomolecular interactions

Bovine serum albumin (BSA) containing buffers are the standard blocking buffer in biosensing, yet human serum is the intended application for most clinical sensors. However, the effect of human serum albumin (HSA) on binding assays remains underexplored. A simple and well-studied assay (human IgG/goat anti-human IgG) was investigated with a surface plasmon resonance (SPR) sensor to address this fundamental question in sensing. Calibrations were performed with buffers containing various concentrations of bovine or human serum albumin, as well as full and diluted bovine or IgG-depleted human serum. It was found that HSA or human serum, but not BSA or bovine serum, significantly affected the SPR shift and binding constants of the assay. Interestingly, large differences were also observed depending on whether the animal or human antibody was immobilized on the SPR chip for detection, highlighting that matrix protein/analyte/receptor interactions play a significant role in the response. We find that the interaction of soluble HSA with human IgG interferes with the recognition region, affecting the binding constant, and thus results obtained in BSA are not necessarily applicable to clinical samples or in vivo conditions. We also clearly demonstrate why a minimum dilution of 1 : 10 is often required in SPR assays to remove most background effects. Taken together, these results show that: (1) BSA does not affect the binding constant between antibodies and thus serves its purpose well when only surface blocking is intended, (2) HSA is an adequate surrogate for human serum in assay optimization, and (3) blocking buffers should be prepared with HSA in the optimization steps of assays to be translated to human blood or serum.

Dual antiplatelet use in the management of COVID-19 associated acute ischemic stroke reocclusion

INTRO

SARS-CoV-2 (COVID-19) infection is associated with acute ischemic stroke (AIS), which may be due to a prothrombotic state. Early reports have suggested high rates of reocclusion following mechanical thrombectomy (MT) with poor radiographic and clinical outcomes. We report our early experience using intra-procedural antithrombotics to address SARS-CoV-2 reocclusion.

METHODS

We identified 6 patients that experienced early reocclusion after MT for COVID-19-associated AIS through retrospective chart review abstracting their basic demographics, COVID-19 status, and stroke management. All these patients were treated after reocclusion with aspirin and cangrelor intra-procedurally, the latter of which was converted to ticagrelor post-procedurally. Some patients additionally received argatroban infusion intraprocedurally.

RESULTS

Mean age was 54. There were 3 post-procedural and 3 intra-procedural re-occlusions. After repeat thrombectomy and treatment with aspirin and cangrelor, those with post-procedure reocclusion did not show further reocclusion, while those with intra-procedural reocclusion showed radiographic improvement with intraprocedural cangrelor administration. Outcomes for these patients were poor, with a median mRS of 4. Two patients developed petechial hemorrhage of their stroke which was managed conservatively, and one developed a retroperitoneal hemorrhage from femoral access requiring transfusion. There were no patients who developed new parenchymal hematomas.

CONCLUSION

COVID-19 AIS may be associated with a hypercoagulable state which risks malignant reocclusion complicating MT. We found antithrombotic treatment periprocedural cangrelor with or without argatroban transitioned to oral aspirin with ticagrelor to be a viable method for management of these patients.

A network meta-analysis of risk factors of infection among close contacts of COVID-19

Objective

We aimed to use network meta-analysis to compare the impact of infection risk factors of close contacts with COVID-19, identify the most influential factors and rank their subgroups. It can provide a theoretical basis for the rapid and accurate tracking and management of close contacts.

Methods

We searched nine databases from December 1, 2019 to August 2, 2023, which only took Chinese and English studies into consideration. Odd ratios (ORs) were calculated from traditional meta-estimated secondary attack rates (SARs) for different risk factors, and risk ranking of these risk factors was calculated by the surface under the cumulative ranking curve (SUCRA).

Results

25 studies with 152647 participants identified. Among all risk factors, the SUCRA of type of contact was 69.6 % and ranked first. Among six types of contact, compared with transportation contact, medical contact, social contact and other, daily contact increased risk of infection by 12.11 (OR: 12.11, 95 % confidence interval (CI): 6.51-22.55), 7.76 (OR: 7.76, 95 % CI: 4.09-14.73), 4.65 (OR: 4.65, 95 % CI: 2.66-8.51) and 8.23 OR: 8.23, 95 % CI: 4.23-16.01) times, respectively. Overall, SUCRA ranks from highest to lowest as daily contact (94.7 %), contact with pollution subjects (78.4 %), social contact (60.8 %), medical contact (31.8 %), other (27.9 %), transportation contact (6.4 %).

Conclusion

The type of contact had the greatest impact on COVID-19 close contacts infection among the risk factors we included. Daily contact carried the greatest risk of infection among six types of contact, followed by contact with pollution subjects, social contact, other, medical contact and transportation contact. The results can provide scientific basis for rapid assess the risk of infection among close contacts based on fewer risk factors and pay attention to high-risk close contacts during management, thereby reducing tracking and management costs.

Proportion of Respiratory Syncytial Virus, SARS-CoV-2, Influenza A/B, and Adenovirus Cases via Rapid Tests in the Community during Winter 2023-A Cross Sectional Study

Respiratory infections constitute a major reason for infants and children seeking medical advice and visiting health facilities, thus remaining a significant public threat with high morbidity and mortality. The predominant viruses causing viral respiratory infections are influenza A and B viruses (Flu-A, Flu-B), respiratory syncytial virus (RSV), adenovirus and coronaviruses. We aimed to record the proportion of RSV, SARS-CoV-2, influenza A/B and adenovirus cases with rapid antigen tests and validate the results with RT-PCR assays of upper respiratory specimens with a wide range of viral loads and (co)-infection patterns in children. Clinical samples were collected from early symptomatic children (presenting with fever and/or cough and/or headache within 5-7 days). The surveillance program was conducted in five private pediatric dispensaries and one pediatric care unit, from 10 January 2023 to 30 March 2023 in central Greece. The total sample of specimens collected was 784 young children and infants, of which 383 (48.8%) were female and 401 were male (51.2%). The mean age of participants was 7.3 + 5.5 years. The sensitivity of the FLU A & B test was 91.15% (95% CI: 84.33-95.67%), and the specificity was 98.96% (95% CI: 97.86-99.58%). The sensitivity and specificity of the adenovirus and RSV test was {92.45% (95% CI: 81.79-97.91%), 99.32% (95% CI: 98.41-99.78%)} and {92.59% (95% CI: 75.71-99.09%), 99.47% (95% CI: 98.65-99.86%)} respectively. Lastly, the sensitivity of the SARS-CoV-2 test was 100.00% (95% CI: 79.41-100.00%) and the specificity was 99.74% (95% CI: 99.06-99.97%). We recorded a proportion of 14.3% and 3.44% for influenza A and B, respectively, followed by a proportion of 6.9% for adenovirus, a proportion of 3.7% for RSV, and finally, a proportion of 2.3% for SARS-CoV-2. The combination of a new multiple rapid test with multiple antigens will probably be a useful tool with a financial impact for health systems targeting the early detection and appropriate treatment of respiratory infections in emergency departments in primary health care facilities.

Algorithmic fairness and bias mitigation for clinical machine learning with deep reinforcement learning

As models based on machine learning continue to be developed for healthcare applications, greater effort is needed to ensure that these technologies do not reflect or exacerbate any unwanted or discriminatory biases that may be present in the data. Here we introduce a reinforcement learning framework capable of mitigating biases that may have been acquired during data collection. In particular, we evaluated our model for the task of rapidly predicting COVID-19 for patients presenting to hospital emergency departments and aimed to mitigate any site (hospital)-specific and ethnicity-based biases present in the data. Using a specialized reward function and training procedure, we show that our method achieves clinically effective screening performances, while significantly improving outcome fairness compared with current benchmarks and state-of-the-art machine learning methods. We performed external validation across three independent hospitals, and additionally tested our method on a patient intensive care unit discharge status task, demonstrating model generalizability.

One-Step Quantification of anti-Covid-19 Antibodies via Dual Affinity Ratiometric Quenching Assays

The global pandemic caused by acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected millions of people and paralyzed healthcare systems worldwide. Developing rapid and accurate tests to detect and quantify anti-SARS-CoV-2 antibodies in complex fluids is critical to (i) track and address the spread of SARS-CoV-2 variants with different virulence and (ii) support the industrial manufacturing and clinical administration of anti-SARS-CoV-2 therapeutic antibodies. Conventional immunoassays, such as lateral flow, ELISA, and surface plasmon resonance (SPR), are either qualitative or, when quantitative, are laborious and expensive and suffer from high variability. Responding to these challenges, this study evaluates the performance of the Dual-Affinity Ratiometric Quenching (DARQ) assay for the quantification of anti-SARS-CoV-2 antibodies in bioprocess harvests and intermediate fractions (i.e., a Chinese hamster ovary (CHO) cell culture supernatant and a purified eluate) and human fluids (i.e., saliva and plasma). Monoclonal antibodies targeting the SARS-CoV-2 nucleocapsid as well as the spike protein of the delta and omicron variants are adopted as model analytes. Additionally, conjugate pads loaded with dried protein were studied as an at-line quantification method that can be used in clinical or manufacturing laboratories. Our results indicate that the DARQ assay is a highly reproducible (coefficient of variation ∼0.5-3%) and rapid (<10 min) test, whose sensitivity (∼0.23-2.5 ng/mL), limit of detection (23-250 ng/mL), and dynamic range (70-1300 ng/mL) are independent of sample complexity, thus representing a valuable tool for monitoring anti-SARS-CoV-2 antibodies.

COVID-19 related acute necrotizing encephalopathy presenting in the early postoperative period

Besides respiratory and gastrointestinal symptoms, SARS-CoV-2 also has potential neurotropic effects. Acute hemorrhagic necrotizing encephalopathy is a rare complication of Covid-19. This article presents a case of an 81-year-old female, fully vaccinated, who underwent laparoscopic transhiatal esophagectomy due to gastroesophageal junction cancer. In the early postoperative period, the patient developed persistent fever accompanied by acute quadriplegia, impaired consciousness, and no signs of respiratory distress. Imaging with Computed Tomography and Magnetic Resonance revealed multiple bilateral lesions both in gray and white matter, as well as pulmonary embolism. Covid-19 infection was added to the differential diagnosis three weeks later, after other possible causes were excluded. The molecular test obtained at that time for coronavirus was negative. However, the high clinical suspicion index led to Covid-19 antibody testing (IgG and IgA), which confirmed the diagnosis. The patient was treated with corticosteroids with noticeable clinical improvement. She was discharged to a rehabilitation center. Six months later, the patient was in good general condition, although a neurological deficit was still present. This case indicates the significance of a high clinical suspicion index, based on a combination of clinical manifestations and neuroimaging, and the confirmation of the diagnosis with molecular and antibody testing. Constant awareness of a possible Covid-19 infection among hospitalized patients is mandatory.

Comparison of diagnostic performance of RT-qPCR, RT-LAMP and IgM/IgG rapid tests for detection of SARS-CoV-2 among healthcare workers in Brazil

BACKGROUND

COVID-19 has become a major public health problem after the outbreak caused by SARS-CoV-2 virus. Great efforts to contain COVID-19 transmission have been applied worldwide. In this context, accurate and fast diagnosis is essential.

METHODS

In this prospective study, we evaluated the clinical performance of three different RNA-based molecular tests - RT-qPCR (Charité protocol), RT-qPCR (CDC (USA) protocol) and RT-LAMP - and one rapid test for detecting anti-SARS-CoV-2 IgM and IgG antibodies.

RESULTS

Our results demonstrate that RT-qPCR using the CDC (USA) protocol is the most accurate diagnostic test among those evaluated, while oro-nasopharyngeal swabs are the most appropriate biological sample. RT-LAMP was the RNA-based molecular test with lowest sensitivity while the serological test presented the lowest sensitivity among all evaluated tests, indicating that the latter test is not a good predictor of disease in the first days after symptoms onset. Additionally, we observed higher viral load in individuals who reported more than 3 symptoms at the baseline. Nevertheless, viral load had not impacted the probability of testing positive for SARS-CoV-2.

CONCLUSION

Our data indicates that RT-qPCR using the CDC (USA) protocol in oro-nasopharyngeal swabs samples should be the method of choice to diagnosis COVID-19.

Border Control for Infectious Respiratory Disease Pandemics: A Modelling Study for H1N1 and Four Strains of SARS-CoV-2

Post-pandemic economic recovery relies on border control for safe cross-border movement. Following the COVID-19 pandemic, we investigate whether effective strategies generalize across diseases and variants. For four SARS-CoV-2 variants and influenza A-H1N1, we simulated 21 strategy families of varying test types and frequencies, quantifying expected transmission risk, relative to no control, by strategy family and quarantine length. We also determined minimum quarantine lengths to suppress relative risk below given thresholds. SARS-CoV-2 variants showed similar relative risk across strategy families and quarantine lengths, with at most 2 days' between-variant difference in minimum quarantine lengths. ART-based and PCR-based strategies showed comparable effectiveness, with regular testing strategies requiring at most 9 days. For influenza A-H1N1, ART-based strategies were ineffective. Daily ART testing reduced relative risk only 9% faster than without regular testing. PCR-based strategies were moderately effective, with daily PCR (0-day delay) testing requiring 16 days for the second-most stringent threshold. Viruses with high typical viral loads and low transmission risk given low viral loads, such as SARS-CoV-2, are effectively controlled with moderate-sensitivity tests (ARTs) and modest quarantine periods. Viruses with low typical viral loads and substantial transmission risk at low viral loads, such as influenza A-H1N1, require high-sensitivity tests (PCR) and longer quarantine periods.

Effectiveness of BNT162b2 after extending the primary series dosing interval in children and adolescents aged 5-17

Extended intervals between the first and second doses of mRNA Covid-19 vaccines may reduce the risk of myocarditis in children and adolescents. However, vaccine effectiveness after this extension remains unclear. To examine this potential variable effectiveness, we conducted a population-based nested case-control study of children and adolescents aged 5-17 years who had received two doses of BNT162b2 in Hong Kong. From January 1 to August 15, 2022, 5396 Covid-19 cases and 202 Covid-19 related hospitalizations were identified and matched with 21,577 and 808 controls, respectively. For vaccine recipients with extended intervals [≥28 days, adjusted odds ratio 0.718, 95% Confidence Interval: 0.619, 0.833] there was a 29.2%-reduced risk of Covid-19 infection compared to those with regular intervals (21-27 days). If the threshold was set at eight weeks, the risk reduction was estimated at 43.5% (aOR 0.565, 95% CI: 0.456, 0.700). In conclusion, longer dosing intervals for children and adolescents should be considered.

An adversarial training framework for mitigating algorithmic biases in clinical machine learning

Machine learning is becoming increasingly prominent in healthcare. Although its benefits are clear, growing attention is being given to how these tools may exacerbate existing biases and disparities. In this study, we introduce an adversarial training framework that is capable of mitigating biases that may have been acquired through data collection. We demonstrate this proposed framework on the real-world task of rapidly predicting COVID-19, and focus on mitigating site-specific (hospital) and demographic (ethnicity) biases. Using the statistical definition of equalized odds, we show that adversarial training improves outcome fairness, while still achieving clinically-effective screening performances (negative predictive values >0.98). We compare our method to previous benchmarks, and perform prospective and external validation across four independent hospital cohorts. Our method can be generalized to any outcomes, models, and definitions of fairness.

Economic Analysis of Border Control Policies during COVID-19 Pandemic: A Modelling Study to Inform Cross-Border Travel Policy between Singapore and Thailand

With countries progressing towards high COVID-19 vaccination rates, strategies for border reopening are required. This study focuses on Thailand and Singapore, two countries that share significant tourism visitation, to illustrate a framework for optimizing COVID-19 testing and quarantine policies for bilateral travel with a focus on economic recovery. The timeframe is the month of October 2021, when Thailand and Singapore were preparing to reopen borders for bilateral travel. This study was conducted to provide evidence for the border reopening policy decisions. Incremental net benefit (INB) compared to the pre-opening period was quantified through a willingness-to-travel model, a micro-simulation COVID-19 transmission model and an economic model accounting for medical and non-medical costs/benefits. Multiple testing and quarantine policies were examined, and Pareto optimal (PO) policies and the most influential components were identified. The highest possible INB for Thailand is US $125.94 million, under a PO policy with no quarantine but with antigen rapid tests (ARTs) pre-departure and upon arrival to enter both countries. The highest possible INB for Singapore is US $29.78 million, under another PO policy with no quarantine on both sides, no testing to enter Thailand, and ARTs pre-departure and upon arrival to enter Singapore. Tourism receipts and costs/profits of testing and quarantine have greater economic impacts than that from COVID-19 transmission. Provided healthcare systems have sufficient capacity, great economic benefits can be gained for both countries by relaxing border control measures.

SARS-CoV-2 Omicron (B.1.1.529) Variant: A Challenge with COVID-19

Since the beginning of the coronavirus disease 2019 (COVID-19) pandemic, there have been multiple peaks of the SARS-CoV-2 (severe acute respiratory syndrome coronavirus virus 2) infection, mainly due to the emergence of new variants, each with a new set of mutations in the viral genome, which have led to changes in the pathogenicity, transmissibility, and morbidity. The Omicron variant is the most recent variant of concern (VOC) to emerge and was recognized by the World Health Organization (WHO) on 26 November 2021. The Omicron lineage is phylogenetically distinct from earlier variants, including the previously dominant Delta SARS-CoV-2 variant. The reverse transcription-polymerase chain reaction (RT-PCR) test, rapid antigen assays, and chest computed tomography (CT) scans can help diagnose the Omicron variant. Furthermore, many agents are expected to have therapeutic benefits for those infected with the Omicron variant, including TriSb92, molnupiravir, nirmatrelvir, and their combination, corticosteroids, and interleukin-6 (IL-6) receptor blockers. Despite being milder than previous variants, the Omicron variant threatens many lives, particularly among the unvaccinated, due to its higher transmissibility, pathogenicity, and infectivity. Mounting evidence has reported the most common clinical manifestations of the Omicron variant to be fever, runny nose, sore throat, severe headache, and fatigue. This review summarizes the essential features of the Omicron variant, including its history, genome, transmissibility, clinical manifestations, diagnosis, management, and the effectiveness of existing vaccines against this VOC.

The Deceptive COVID-19: Lessons from Common Molecular Diagnostics and a Novel Plan for the Prevention of the Next Pandemic

The COVID-19 pandemic took place during the years 2020-2022 and the virus, named SARS-CoV-2, seems likely to have resulted in an endemic disease. Nevertheless, widespread COVID-19 has given rise to several major molecular diagnostics' facts and concerns that have emerged during the overall management of this disease and the subsequent pandemic. These concerns and lessons are undeniably critical for the prevention and control of future infectious agents. Furthermore, most populaces were introduced to several new public health maintenance strategies, and again, some critical events arose. The purpose of this perspective is to thoroughly analyze all these issues and the concerns, such as the molecular diagnostics' terminologies, their role, as well as the quantity and quality issues with a molecular diagnostics' test result. Furthermore, it is speculated that society will be more vulnerable in the future and prone to emerging infectious diseases; thus, a novel preventive medicine's plan for the prevention and control of future (re)emerging infectious diseases is presented, so as to aid the early prevention of future epidemics and pandemics.

Monitoring SARS-CoV-2 seroprevalence over time among pregnant women admitted to delivery units: Suitability for surveillance

OBJECTIVES

To determine SARS-CoV-2 seroprevalence over time and risk factors among pregnant women at delivery in São Paulo, Brazil; and to evaluate the suitability of pregnant women as a sentinel population for SARS-CoV-2 serosurveillance.

METHODS

Unselected consecutive pregnant women presenting at the labor ward of a single large hospital between July 20th 2020 to February 21st 2021 were enrolled and tested for SARS-CoV-2 serology using two assays: the rapid chromatic Wondfo One Step (for total IgA and IgG detection) and Roche Elecsys assay (detecting anti-nucleoprotein [N] IgG). SARS-CoV-2 seroprevalence was computed as smooth spline function over time with 95% confidence intervals (CI). Risk factors were evaluated for positivity by each assay. We compared timepoint seroprevalence by the two assays with four concomitant community household surveys (HHS), in which the Roche assay was used, to determine the sensitivity and relevance of the pregnant women population as sentinel population.

RESULTS

Overall SARS-CoV-2 seroprevalence was 28.9% (221/763) by Roche and 17.9% (137/763) by Wondfo. Reported symptoms experienced during pregnancy were all significantly correlated with being SARS-CoV-2 seropositive at delivery with any assay (with odds-ratios ranging from 3.0 [95% CI: 2.1-4.3] for coryza to 22.8 [95% CI: 12.3-46.6] for ageusia). Seropositivity by either assay was high in women at delivery in the early period of the pandemic (June 2020), compared with seropositivity in women from the concomitant HHS: 44.1% (95% CI: 21.8-66.4) for Roche, 54.1% (30.9-78.5) for Wondfo, versus 11.4% (95% CI: 9.2-13.6) for HHS. For later periods (October 2020 and January 2021), the seropositivity in women at delivery measured by Roche corresponded well with the prevalence found among women in the HHS using the same assay, whilst prevalence measured by Wondfo dropped.

CONCLUSIONS

Women at delivery represent a highly exposed and readily accessible population for sentinel surveillance of emerging infections such as SARS-CoV-2.

Cost benefit analysis of alternative testing and quarantine policies for travelers for infection control: A case study of Singapore during the COVID-19 pandemic

Background

Border control mitigates local infections but bears a heavy economic cost, especially for tourism-reliant countries. While studies have supported the efficacy of border control in suppressing cross-border transmission, the trade-off between costs from imported and secondary cases and from lost economic activities has not been studied. This case study of Singapore during the COVID-19 pandemic aims to understand the impacts of varying quarantine length and testing strategies on the economy and health system. Additionally, we explored the impact of permitting unvaccinated travelers to address emerging equity concerns. We assumed that community transmission is stable and vaccination rates are high enough that inbound travelers are not dissuaded from traveling.

Methods

The number of travelers was predicted considering that longer quarantine reduces willingness to travel. A micro-simulation model predicted the number of COVID-19 cases among travelers, the resultant secondary cases, and the probability of being symptomatic in each group. The incremental net monetary benefit (INB) of Singapore was quantified under each border-opening policy compared to pre-opening status, based on tourism receipts, cost/profit from testing and quarantine, and cost and health loss due to COVID-19 cases.

Results

Compared to polymerase chain reaction (PCR), rapid antigen test (ART) detects fewer imported cases but results in fewer secondary cases. Longer quarantine results in fewer cases but lower INB due to reduced tourism receipts. Assuming the proportion of unvaccinated travelers is small (8% locally and 24% globally), allowing unvaccinated travelers will accrue higher INB without exceeding the intensive care unit (ICU) capacity. The highest monthly INB from all travelers is $2,236.24 m, with 46.69 ICU cases per month, achieved with ARTs at pre-departure and on arrival without quarantine. The optimal policy in terms of highest INB is robust under changes to various model assumptions. Among all cost-benefit components, the top driver for INB is tourism receipts.

Conclusions

With high vaccination rates locally and globally alongside stable community transmission, opening borders to travelers regardless of vaccination status will increase economic growth in the destination country. The caseloads remain manageable without exceeding ICU capacity, and costs of cases are offset by the economic value generated from travelers.

Detection of Antibody versus Antigen, Optimal Option of Different Serological Assays Based Tests for COVID-19 Diagnosis: A Meta-Analysis

Background

In this study, the diagnostic efficacy of antigen test and antibody test were assessed. Additionally, the difference of sensitivity, specificity, and diagnostic odds ratio were compared concerning efficacy of antibody test versus antigen test for Corona Virus Disease 2019 (COVID-19) diagnosis.

Methods

Online databases were searched for full-text publications and STATA software was used for data pooling and analysis before Sep 1st, 2022. Forrest plot was used to show the pooled sensitivity, specificity and diagnostic odds ratio. Combined receiver operating characteristic (ROC) curve was used to show the area of under curve of complex data.

Results

Overall, 25 studies were included. The sensitivity (0.68, 95% CI: 0.53-0.80) and specificity (0.99, 95% CI: 0.98-0.99) in antibody or antigen was calculated. The time point of test lead to heterogeneity. The area under curve (AUC) was 0.98 (95% CI: 0.96-0.99), and the diagnostic odds ratio (DOR) was 299.54 (95% CI: 135.61-661.64). Subgroup analysis indicated antibody test with sensitivity (0.59, 95% CI: 0.44-0.73) and specificity (0.98, 95% CI: 0.95-0.99) and antigen test with sensitivity of 0.77 (95% CI: 0.53-0.91) and specificity of 0.99 (95% CI: 0.98-1.00). Higher AUC and DOR were proved in antigen test.

Conclusion

The present study compared the efficacy of antibody test versus antigen test for COVID-19 diagnosis. Better diagnostic efficacy, lower heterogeneity, and less publication bias of rapid antigen testing was suggested in this study. This study would help us to make better strategy about choosing rapid and reliable testing method in diagnosis of the COVID-19 disease.

SARS-CoV-2 Seroprevalence among Health-Care Workers in Isfahan, Iran

Background

Health-care workers (HCWs) are in the frontline for fighting the coronavirus disease 2019 (COVID-19) pandemic and are at higher risk of acquiring the infection. Therefore, the defining immunity status among HCWs helps mitigate the exposure risk. In this study, we investigated the anti-SARS-CoV-2 immunoglobulin G (IgG) and immunoglobulin M (IgM) and also the associated risk factors in the HCWs working in Isfahan University of Medical Sciences COVID-19 referral hospitals.

Materials and Methods

In a cross-sectional study, demographics, COVID-19 symptoms during the past 2 weeks, and health-care details were collected from 200 consenting health workers of COVID-center-hospitals of Isfahan University of Medical Sciences from 23 October to 21 December 2020. The recombinant SARS-CoV2 nucleocapsid protein enzyme-linked immunosorbent assay-based IgM, and IgG antibody tests were evaluated. Data were analyzed using Chi-square and independent-t-student tests, and P < 0.05 was considered significant.

Results

One hundred and forty-one women and 59 men with a mean age of 36.4 ± 7.77 years participated in the study. IgG Ab and IgM Ab were positive in 77 (38.5%) and 12 (6%) of samples, respectively, and both antibodies were detected in 9 (4.5%). Higher ages, direct contact with the patients with COVID-19, muscle pain, loss of taste and smell, fever, and cough were the factors associated with antibody seropositivity against SARS-CoV2.

Conclusion

This study demonstrated that the prevalence of HCWs with antibodies against SARS-CoV-2 is relatively high in Isfahan University referral hospitals. The development of safety protocols and screening and vaccination strategies in the frontline HCWs must be implemented to reduce the burden of infection.

The Seroprevalence and Seropositivity of SARS-CoV-2 among Healthcare Workers during the Third Pandemic Wave

BACKGROUND

Due to the unclear protective role of produced antibodies and the need for seroepidemiologic studies, we surveyed the COVID-19 seroprevalence among healthcare professionals who had direct or indirect contact with COVID-19 patients.

METHODS

From 19 October 2020 to 17 February 2021, 300 healthcare workers were enrolled and tested for serum antibodies in this prospective cohort study. Demographic information, risk factors, and infection history were collected. Anti- SARS-CoV-2 IgG and IgM antibody titers were determined to estimate the seroconversion rate.

RESULTS

During the first and second phases of the study, the positive seroconversion rates were 31.7 and 26.6%, respectively. In seronegative individuals, sixteen (10.6%) new cases of COVID-19 and five (6.3%) reinfections were identified. Among those with a positive antibody level, forty-one (36.9%) healthcare workers reported no symptoms in the preceding months. There was no association between occupational exposure and an increased probability of seroconversion.

CONCLUSIONS

The seropositivity rate and the rate of asymptomatic individuals with seroconversion was remarkable and could be an indicator of a high infection rate among healthcare workers.

Reliable estimation of SARS-CoV-2 anti-spike protein IgG titers from single dilution optical density values in serologic surveys

Reliable and scalable seroepidemiology methods are needed to estimate SARS-CoV-2 incidence and monitor the dynamics of population-level immunity as the pandemic evolves. We aimed to evaluate the reliability of SARS-CoV-2 normalized ELISA optical density (nOD) at a single dilution compared to titers derived from serial dilutions. We conducted serial serosurveys within a community-based cohort in Salvador, Brazil. Anti-S IgG ELISA (Euroimmun AG) was performed with 5 serial 3-fold dilutions of paired sera from 54 participants. Changes in nOD reliably predicted increases and decreases in titers (98.1% agreement, κ = 95.8%). Fitting the relationship between nOD and interpolated titers to a log-log curve yields highly accurate predictions of titers (r2 = 0.995) and changes in titers (r2 = 0.975), using only 1 to 2 dilutions. This approach can significantly reduce the time, labor and resources needed for large-scale serosurveys to ascertain population-level changes in exposure and immunity.

Frequent mandatory COVID-19 testing may increase risky behavior

Mandatory surveillance testing programs are popular policies aimed to control SARS-CoV-2 and may be considered for future epidemics. However, if people believe that testing lowers their risk of infection, such policies could increase risky behavior and may even cause increased pathogen spread. Using data from two US universities, we find that frequent mandatory testing is associated with greater participation in events linked to COVID-19 spread. Women seem to be driving this association, and mediation analyses suggest this is partly due to women's higher perception of COVID-related health risks. Our results show the potential for adverse effects from epidemic control policies, both on average and across population subgroups. Undertaking mitigation measures to reduce such unintended consequences may therefore be important.

Reinfection and reactivation of SARS-CoV-2

As the cases of SARS-CoV-2 infection escalates, the essence of in-depth knowledge around acquired immunity and emergence of reinfection and reactivation have to be captured. While being a rare phenomenon, reinfection occurs as the result of diminishing protection conferred by antibodies, especially IgG. Reactivation is more concerned with the role of various elements including shedding lingering viral RNA for a prolonged time and incomplete resolution of infection along with the insight of dormant viral exosomes’ role. The concept of testing positive after two consecutive negative results requires proper discrimination of reinfection from reactivation. In this review, we summarized the current evidence for possible mechanisms leading to viral reactivation or test re-positivity. We also pointed out risk factors associated with both reinfection and reactivation.

Epidemiological and clinical characteristics of pregnant women and neonates with COVID-19 in Northwest Mexico

INTRODUCTION

The SARS-CoV-2 virus, which causes COVID-19, has spread quickly worldwide, causing millions of cases and thousands of deaths. Some risk factors in the general population are related to the development of severe COVID-19 or death, but in pregnant women and neonates, the information is limited.

OBJECTIVE

To determine the epidemiological and clinical characteristics of pregnant women and neonates diagnosed with COVID-19 by RT-PCR and serological tests, and analyze the relationship between the influenza vaccination and COVID-19 symptoms in infected pregnant women in Sinaloa state.

METHODS

We collected samples from 116 pregnant women and 84 neonates from the Women´s Hospital of Sinaloa. They were diagnosed with COVID-19 by RT-PCR and serological tests (IgG), and sociodemographic, clinical and laboratory parameters were recorded.

RESULTS

A total of 11.2% (13/116) of the pregnant women were RT-PCR+, 25% (29/116) were IgG+ and 4.3% (5/116) were positive for both tests. Symptoms such as rhinorrhea (P = .04), cough (P = .02) and polypnea (P = .04) in pregnant women were related to COVID-19, also leukocyte index was higher in pregnant women with COVID-19 (P = .03), but the associations were lost after the Bonferroni correction. No laboratory parameters or underlying diseases were associated with COVID-19, and most infected pregnant women had mild cases. We found an association between the influenza vaccine and less common COVID-19 symptoms in pregnant women who were infected (P = .01). A total of 7.2% (6/84) of neonates were RT-PCR+, 35.7% (30/84) were IgG+, and there were no symptoms or underlying diseases associated with neonates who were infected. In conclusion, this work demonstrated that some symptoms were related to COVID-19, most pregnant women and neonates had mild cases, and the influenza vaccine could decrease the severity of COVID-19 cases in pregnant women.

A rapid, easy, and scalable whole blood monocyte CD169 assay for outpatient screening during SARS-CoV-2 outbreak, and potentially other emerging disease outbreaks

Objective:

The COVID-19 corona virus disease outbreak is globally challenging health systems and societies. Its diagnosis relies on molecular methods, with drawbacks revealed by mass screening. Upregulation of neutrophil CD64 or monocyte CD169 has been abundantly reported as markers of bacterial or acute viral infection, respectively. We evaluated the sensitivity of an easy, one-step whole blood flow cytometry assay to measure these markers within 10 min, as a potential screening test for COVID-19 patients.

Methods:

Patients (n = 177) with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection were tested on 10 µL blood and results were compared with reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR).

Results:

We observed 98% and 100% sensitivity in early-stage (n = 52) and asymptomatic patients (n = 9), respectively. Late-stage patients, who presented for a second control RT-qPCR, were negative for both assays in most cases. Conversely, neutrophil CD64 expression was unchanged in 75% of cases, without significant differences between groups.

Conclusion:

Monocyte CD169 evaluation was highly sensitive for detecting SARS-CoV-2 infection in first-presentation patients; and it returns to basal level upon infection clearance. The potential ease of fingerprick collection, minimal time-to-result, and low cost rank this biomarker measurement as a potential viral disease screening tool, including COVID-19. When the virus prevalence in the tested population is usually low (1%−10%), such an approach could increase the testing capacity 10 to 100-fold, with the same limited molecular testing resources, which could focus on confirmation purposes only.

Testing for COVID-19 is Much More Effective When Performed Immediately Prior to Social Mixing

Objective: To quantify the utility of RT-PCR and rapid antigen tests in preventing post-arrival transmission based on timing of the pre-departure test.

Methods: We derived analytical expressions to compute post-arrival transmission when no test is performed, and when either an RT-PCR or any of 18 rapid antigen tests is performed at specified times before arrival. We determined the diagnostic sensitivity of the rapid antigen tests by propagating their RT-PCR percent positive agreement onto known RT-PCR diagnostic sensitivity.

Results: Depending on the rapid antigen test used, conducting a rapid antigen test immediately before departure reduces post-arrival transmission between 37.4% (95% CrI: 28.2%–40.7%) and 46.7% (95% CrI:40.0%–49.3%), compared to a 31.1% (95% CrI: 26.3%–33.5%) reduction using an RT-PCR 12 h before arrival. Performance of each rapid antigen test differed by diagnostic sensitivity over the course of disease. However, these differences were smaller than those engendered by testing too early.

Conclusion: Testing closer to arrival—ideally on the day of arrival—is more effective at reducing post-arrival transmission than testing earlier. Rapid antigen tests perform the best in this application due to their short turnaround time.

SARS-CoV-2 Seroprevalence among Healthcare Workers after the First and Second Pandemic Waves

Background: The Grand Hôpital de Charleroi is a large non-academic Belgian hospital that treated a large number of COVID-19 inpatients. In the context of this pandemic, all professions-combined healthcare workers (HCWs), and not only direct caregivers, are a frontline workforce in contact with suspected and confirmed COVID-19 cases and seem to be a high-risk group for exposure. The aim of our study was to estimate the prevalence of anti-SARS-CoV-2 antibodies in HCWs in our hospital after the first and second pandemic waves and to characterize the distribution of this seroprevalence in relation to various criteria. Methods: At the end of the two recruitment periods, a total of 4008 serological tests were performed in this single-center cross-sectional study. After completing a questionnaire including demographic and personal data, possible previous COVID-19 diagnostic test results and/or the presence of symptoms potentially related to COVID-19, the study participants underwent blood sampling and serological testing using DiaSorin’s LIAISON^® SARS-CoV-2 S1/S2 IgG test for the first phase and LIAISON^® SARS-CoV-2 TrimericS IgG test for the second phase of this study. Results: In total, 302 study participants (10.72%) in the first round of the study and 404 (33.92%) in the second round were positive for SARS-CoV-2-IgG antibodies. The prevalence of seropositivity observed after the second wave was 3.16 times higher than after the first wave. We confirmed that direct, prolonged, and repeated contact with patients or their environment was a predominant seroconversion factor, but more unexpectedly, that this was the case for all HCWs and not only caregivers. Finally, the notion of high-risk contact seemed more readily identifiable in one’s workplace than in one’s private life. Conclusions: Our study confirmed that HCWs are at a significantly higher risk of contracting COVID-19 than the general population, and suggests that repeated contacts with at-risk patients, regardless of the HCWs’ professions, represents the most important risk factor for seroconversion (Clinicaltrials.gov number, NCT04723290).

Comparison of temporal evolution of computed tomography imaging features in COVID-19 and influenza infections in a multicenter cohort study

Purpose

To compare temporal evolution of imaging features of coronavirus disease 2019 (COVID-19) and influenza in computed tomography and evaluate their predictive value for distinction.

Methods

In this retrospective, multicenter study 179 CT examinations of 52 COVID-19 and 44 influenza critically ill patients were included. Lung involvement, main pattern (ground glass opacity, crazy paving, consolidation) and additional lung and chest findings were evaluated by two independent observers. Additional findings and clinical data were compared patient-wise. A decision tree analysis was performed to identify imaging features with predictive value in distinguishing both entities.

Results

In contrast to influenza patients, lung involvement remains high in COVID-19 patients > 14 days after the diagnosis. The predominant pattern in COVID-19 evolves from ground glass at the beginning to consolidation in later disease. In influenza there is more consolidation at the beginning and overall less ground glass opacity (p = 0.002). Decision tree analysis yielded the following: Earlier in disease course, pleural effusion is a typical feature of influenza (p = 0.007) whereas ground glass opacities indicate COVID-19 (p = 0.04). In later disease, particularly more lung involvement (p < 0.001), but also less pleural (p = 0.005) and pericardial (p = 0.003) effusion favor COVID-19 over influenza. Regardless of time point, less lung involvement (p < 0.001), tree-in-bud (p = 0.002) and pericardial effusion (p = 0.01) make influenza more likely than COVID-19.

Conclusions

This study identified differences in temporal evolution of imaging features between COVID-19 and influenza. These findings may help to distinguish both diseases in critically ill patients when laboratory findings are delayed or inconclusive.

•

Decision tree analysis helps to distinguish COVID-19 and Influenza.

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Pleural effusion is a typical feature of influenza in early disease.

•

Ground glass opacities indicate COVID-19 in early disease.

•

Lung involvement remains high in COVID-19 patients > 14 days after the diagnosis.

•

Pleural and pericardial effusion favor influenza over COVID-19 in later disease.

Estimating global, regional, and national daily and cumulative infections with SARS-CoV-2 through Nov 14, 2021: a statistical analysis

BACKGROUND

Timely, accurate, and comprehensive estimates of SARS-CoV-2 daily infection rates, cumulative infections, the proportion of the population that has been infected at least once, and the effective reproductive number (R_effective) are essential for understanding the determinants of past infection, current transmission patterns, and a population's susceptibility to future infection with the same variant. Although several studies have estimated cumulative SARS-CoV-2 infections in select locations at specific points in time, all of these analyses have relied on biased data inputs that were not adequately corrected for. In this study, we aimed to provide a novel approach to estimating past SARS-CoV-2 daily infections, cumulative infections, and the proportion of the population infected, for 190 countries and territories from the start of the pandemic to Nov 14, 2021. This approach combines data from reported cases, reported deaths, excess deaths attributable to COVID-19, hospitalisations, and seroprevalence surveys to produce more robust estimates that minimise constituent biases.

METHODS

We produced a comprehensive set of global and location-specific estimates of daily and cumulative SARS-CoV-2 infections through Nov 14, 2021, using data largely from Johns Hopkins University (Baltimore, MD, USA) and national databases for reported cases, hospital admissions, and reported deaths, as well as seroprevalence surveys identified through previous reviews, SeroTracker, and governmental organisations. We corrected these data for known biases such as lags in reporting, accounted for under-reporting of deaths by use of a statistical model of the proportion of excess mortality attributable to SARS-CoV-2, and adjusted seroprevalence surveys for waning antibody sensitivity, vaccinations, and reinfection from SARS-CoV-2 escape variants. We then created an empirical database of infection-detection ratios (IDRs), infection-hospitalisation ratios (IHRs), and infection-fatality ratios (IFRs). To estimate a complete time series for each location, we developed statistical models to predict the IDR, IHR, and IFR by location and day, testing a set of predictors justified through published systematic reviews. Next, we combined three series of estimates of daily infections (cases divided by IDR, hospitalisations divided by IHR, and deaths divided by IFR), into a more robust estimate of daily infections. We then used daily infections to estimate cumulative infections and the cumulative proportion of the population with one or more infections, and we then calculated posterior estimates of cumulative IDR, IHR, and IFR using cumulative infections and the corrected data on reported cases, hospitalisations, and deaths. Finally, we converted daily infections into a historical time series of R_effective by location and day based on assumptions of duration from infection to infectiousness and time an individual spent being infectious. For each of these quantities, we estimated a distribution based on an ensemble framework that captured uncertainty in data sources, model design, and parameter assumptions.

FINDINGS

Global daily SARS-CoV-2 infections fluctuated between 3 million and 17 million new infections per day between April, 2020, and October, 2021, peaking in mid-April, 2021, primarily as a result of surges in India. Between the start of the pandemic and Nov 14, 2021, there were an estimated 3·80 billion (95% uncertainty interval 3·44-4·08) total SARS-CoV-2 infections and reinfections combined, and an estimated 3·39 billion (3·08-3·63) individuals, or 43·9% (39·9-46·9) of the global population, had been infected one or more times. 1·34 billion (1·20-1·49) of these infections occurred in south Asia, the highest among the seven super-regions, although the sub-Saharan Africa super-region had the highest infection rate (79·3 per 100 population [69·0-86·4]). The high-income super-region had the fewest infections (239 million [226-252]), and southeast Asia, east Asia, and Oceania had the lowest infection rate (13·0 per 100 population [8·4-17·7]). The cumulative proportion of the population ever infected varied greatly between countries and territories, with rates higher than 70% in 40 countries and lower than 20% in 39 countries. There was no discernible relationship between R_effective and total immunity, and even at total immunity levels of 80%, we observed no indication of an abrupt drop in R_effective, indicating that there is not a clear herd immunity threshold observed in the data.

INTERPRETATION

COVID-19 has already had a staggering impact on the world up to the beginning of the omicron (B.1.1.529) wave, with over 40% of the global population infected at least once by Nov 14, 2021. The vast differences in cumulative proportion of the population infected across locations could help policy makers identify the transmission-prevention strategies that have been most effective, as well as the populations at greatest risk for future infection. This information might also be useful for targeted transmission-prevention interventions, including vaccine prioritisation. Our statistical approach to estimating SARS-CoV-2 infection allows estimates to be updated and disseminated rapidly on the basis of newly available data, which has and will be crucially important for timely COVID-19 research, science, and policy responses.

FUNDING

Bill & Melinda Gates Foundation, J Stanton, T Gillespie, and J and E Nordstrom.

Preoperative monocytosis is predictable at diagnosis for RT-PCR negative COVİD-19 paediatric cardiac surgery patients

Other markers investigated in this population have gained importance in the diagnosis of the disease since the course of COVID-19 disease is atypical in the paediatric population and PCR may be misleading. The leukocyte profile is one of these biochemical tests. Children did not have lymphopenia in hemogram count, whereas relatively neutropenia and monocytosis were detected, unlike the adult population. The reason why children do not have lymphopenia is thought to be due to the fact that the thymus is more active in the first years of life.Two-hundred and four patients operated in our paediatric cardiac surgery clinic from 11March, 2020 to 1 April, 2021 were retrospectively examined and 11 patients with preoperative asymptomatic and PCR (-), but with PCR (+) in the post-operative period (patients with incubation period or false PCR negativity) were included in our study. Patients requiring emergency operation and operated from PCR (+) patients in the preoperative period were excluded from the study.The neutrophil ratio in the lymphocytic series of 7 patients out of 11 patients was slightly below the normal range in the preoperative period, the lymphocyte ratio of 3 patients was slightly above the normal range, and the relative monocyte ratio of 10 patients was slightly above the normal range.We think that evaluating the leukocyte profile combined with RT-PCR will give more accurate results in the diagnosis of incubation period and false RT-PCR negative patients. In addition, we believe that the algorithms for non-complex paediatric cardiac surgery procedures and timing in the paediatric population with a better course of COVID-19 disease with a positive post-operative course.

Anti-SARS-CoV-2 Titers Predict the Severity of COVID-19

Coronavirus disease 2019 (COVID-19) due to SARS-CoV-2 is associated with a wide spectrum of disease, ranging from asymptomatic infection to acute respiratory distress syndrome. Some biomarkers may predict disease severity. Among them, the anti-SARS-CoV-2 antibody response has been related to severe disease. The aim of this study was to assess the correlation between the anti-SARS-CoV-2 serological response and COVID-19 outcome. Demographic, clinical, and biological data from nasopharyngeal-PCR confirmed COVID-19 hospitalized patients were prospectively collected between April and August 2020 at our institution. All patients had serial weekly serology testing for a maximum of three blood samples or until discharge. Two different serological assays were used: a chemiluminescent assay and an in-house developed Luminex immunoassay. Kinetics of the serological response and correlation between the antibody titers and outcome were assessed. Among the 70 patients enrolled in the study, 22 required invasive ventilation, 29 required non-invasive ventilation or oxygen supplementation, and 19 did not require any oxygen supplementation. Median duration of symptoms upon admission for the three groups were 13, 8, and 9 days, respectively. Antibody titers gradually increased for up to 3 weeks since the onset of symptoms for patients requiring oxygen supplementation with significantly higher antibody titers for patients requiring invasive ventilation. Antibody titers on admission were also significantly higher in severely ill patients and serology performed well in predicting the necessity of invasive ventilation (AUC: 0.79, 95% CI: 0.67–0.9). Serology testing at admission may be a good indicator to identify severe COVID-19 patients who will require invasive mechanical ventilation.

An olfactory self-test effectively screens for COVID-19

Background

Key to curtailing the COVID-19 pandemic are wide-scale screening strategies. An ideal screen is one that would not rely on transporting, distributing, and collecting physical specimens. Given the olfactory impairment associated with COVID-19, we developed a perceptual measure of olfaction that relies on smelling household odorants and rating them online.

Methods

Each participant was instructed to select 5 household items, and rate their perceived odor pleasantness and intensity using an online visual analogue scale. We used this data to assign an olfactory perceptual fingerprint, a value that reflects the perceived difference between odorants. We tested the performance of this real-time tool in a total of 13,484 participants (462 COVID-19 positive) from 134 countries who provided 178,820 perceptual ratings of 60 different household odorants.

Results

We observe that olfactory ratings are indicative of COVID-19 status in a country, significantly correlating with national infection rates over time. More importantly, we observe indicative power at the individual level (79% sensitivity and 87% specificity). Critically, this olfactory screen remains effective in participants with COVID-19 but without symptoms, and in participants with symptoms but without COVID-19.

Conclusions

The current odorant-based olfactory screen adds a component to online symptom-checkers, to potentially provide an added first line of defense that can help fight disease progression at the population level. The data derived from this tool may allow better understanding of the link between COVID-19 and olfaction.

From early on in the COVID-19 pandemic, a symptom associated with infection was rapid and often complete loss of the sense of smell. This rendered smell testing a potentially helpful tool in large-scale screening for SARS-CoV-2 infection. We built an online tool (smelltracker.org) that enables assessment of the sense of smell using commonly available household odorants. Initial use by 13,484 participants (462 COVID-19 positive) from 134 countries corroborated that SARS-CoV-2 infection is associated with impaired smell. Moreover, the tool detected infection in the absence of any other symptoms, including subjective loss in smell. Use of this tool may provide an added instrument for screening SARS-CoV-2 infection, and the data generated by the tool may provide for deeper understanding of the brain mechanisms involved with loss of smell associated with COVID-19.

Snitz et al. develop a web-based olfactory screening tool for COVID-19, which relies on users smelling household odorants. Based on data from participants in 134 countries, the authors report that olfactory ratings are indicative of COVID-19 status.

Self-Sampled Gargle Water Direct RT-LAMP as a Screening Method for the Detection of SARS-CoV-2 Infections

We assessed the viability of self-sampled gargle water direct RT-LAMP (LAMP) for detecting SARS-CoV-2 infections by estimating its sensitivity with respect to the gold standard indirect RT-PCR of paired oro-nasopharyngeal swab samples. We also assessed the impact of symptom onset to test time (STT)—i.e., symptom days at sampling, on LAMP. In addition, we appraised the viability of gargle water self-sampling versus oro-nasopharyngeal swab sampling, by comparing paired indirect RT-PCR results. 202 oro-nasopharyngeal swab and paired self-sampled gargle water samples were collected from hospital patients with COVID-19 associated symptoms. LAMP, indirect and direct RT-PCR were performed on all gargle water samples, and indirect RT-PCR was performed on all oro-nasopharyngeal samples. LAMP presented a sensitivity of 80.8% (95% CI: 70.8–90.8%) for sample pairs with sub-25 Ct oro-nasopharyngeal indirect RT-PCR results, and 77.6% (66.2–89.1%) sensitivity for sub-30 Ct samples with STT ≤ 7 days. STT, independently of Ct value, correlated negatively with LAMP performance. 80.7% agreement was observed between gargle water and oro-nasopharyngeal indirect RT-PCR results. In conclusion, LAMP presents an acceptable sensitivity for low Ct and low STT samples. Gargle water may be considered as a viable sampling method, and LAMP as a screening method, especially for symptomatic persons with low STT values.

Characteristics and impact of Long Covid: Findings from an online survey

BACKGROUND

Long Covid is a public health concern that needs defining, quantifying, and describing. We aimed to explore the initial and ongoing symptoms of Long Covid following SARS-CoV-2 infection and describe its impact on daily life.

METHODS

We collected self-reported data through an online survey using convenience non-probability sampling. The survey enrolled adults who reported lab-confirmed (PCR or antibody) or suspected COVID-19 who were not hospitalised in the first two weeks of illness. This analysis was restricted to those with self-reported Long Covid. Univariate comparisons between those with and without confirmed COVID-19 infection were carried out and agglomerative hierarchical clustering was used to identify specific symptom clusters, and their demographic and functional correlates.

RESULTS

We analysed data from 2550 participants with a median duration of illness of 7.6 months (interquartile range (IQR) 7.1-7.9). 26.5% reported lab-confirmation of infection. The mean age was 46.5 years (standard deviation 11 years) with 82.8% females and 79.9% of participants based in the UK. 89.5% described their health as good, very good or excellent before COVID-19. The most common initial symptoms that persisted were exhaustion, chest pressure/tightness, shortness of breath and headache. Cognitive dysfunction and palpitations became more prevalent later in the illness. Most participants described fluctuating (57.7%) or relapsing symptoms (17.6%). Physical activity, stress, and sleep disturbance commonly triggered symptoms. A third (32%) reported they were unable to live alone without any assistance at six weeks from start of illness. 16.9% reported being unable to work solely due to COVID-19 illness. 37.0% reported loss of income due to illness, and 64.4% said they were unable to perform usual activities/duties. Acute systems clustered broadly into two groups: a majority cluster (n = 2235, 88%) with cardiopulmonary predominant symptoms, and a minority cluster (n = 305, 12%) with multisystem symptoms. Similarly, ongoing symptoms broadly clustered in two groups; a majority cluster (n = 2243, 88.8%) exhibiting mainly cardiopulmonary, cognitive symptoms and exhaustion, and a minority cluster (n = 283, 11.2%) exhibiting more multisystem symptoms. Belonging to the more severe multisystem cluster was associated with more severe functional impact, lower income, younger age, being female, worse baseline health, and inadequate rest in the first two weeks of the illness, with no major differences in the cluster patterns when restricting analysis to the lab-confirmed subgroup.

CONCLUSION

This is an exploratory survey of Long Covid characteristics. Whilst this is a non-representative population sample, it highlights the heterogeneity of persistent symptoms, and the significant functional impact of prolonged illness following confirmed or suspected SARS-CoV-2 infection. To study prevalence, predictors and prognosis, research is needed in a representative population sample using standardised case definitions.

Performance Evaluation of Lateral Flow Assays for Coronavirus Disease-19 Serology

The coronavirus disease of 2019 (COVID-19) pandemic, caused by infection with the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has undoubtedly resulted in significant morbidities, mortalities, and economic disruptions across the globe. Affordable and scalable tools to monitor the transmission dynamics of the SARS-CoV-2 virus and the longevity of induced antibodies will be paramount to monitor and control the pandemic as multiple waves continue to rage in many countries. Serologic assays detect humoral responses to the virus, to determine seroprevalence in target populations, or induction of antibodies at the individual level following either natural infection or vaccination. With multiple vaccines rolling out globally, serologic assays to detect anti-SARS-CoV-2 antibodies will be important tools to monitor the development of herd immunity. To address this need, serologic lateral flow assays (LFAs), which can be easily implemented for both population surveillance and home use, will be vital to monitor the evolution of the pandemic and inform containment measures. Such assays are particularly important for monitoring the transmission dynamics and durability of immunity generated by natural infections and vaccination, particularly in resource-limited settings. In this review, we discuss considerations for evaluating the accuracy of these LFAs, their suitability for different use cases, and implementation opportunities.

Diagnostic sensitivity of RT-PCR assays on nasopharyngeal specimens for detection of SARS-CoV-2 infection: A Systematic Review and Meta-Analysis

Background

Reverse transcription polymerase chain reaction (RT-PCR) is the current standard of reference in the diagnosis of SARS-CoV-2 infection. In outpatient clinical practice, nasopharyngeal swab RT-PCR testing is still the most common procedure. The purpose of this systematic review and meta-analysis was to evaluate the sensitivity of RT-PCR nasopharyngeal assays.

Methods

We searched three databases, including PubMed/MEDLINE, EMBASE, and Cochrane Library, using a comprehensive strategy. Studies investigating the sensitivity of SARS-CoV-2 RT-PCR nasopharyngeal assays in adults were included. Two reviewers extracted data and assessed trial quality independently. Pooled sensitivity and its confidence interval were computed using the meta package in R.

Results

Thirteen studies were found eligible for the inclusion in the systematic review. Out of these, 25 different sub-studies were identified and included in the meta-analysis, which reported the sensitivities of 25 different nasopharyngeal RT-PCR assays. Finally, the overall pooled sensitivity resulted 89% (95% CI, 85.4 to 91.8%).

Conclusion

Our study suggests that RT-PCR assays on nasopharyngeal specimens have a substantial sensitivity for diagnosing SARS-CoV-2 infection.

Association of the Serum Levels of the Nucleocapsid Antigen of SARS-CoV-2 With the Diagnosis, Disease Severity, and Antibody Titers in Patients With COVID-19: A Retrospective Cross-Sectional Study

Background: Several types of laboratory tests for COVID-19 have been established to date; however, the clinical significance of the serum SARS-CoV-2 nucleocapsid (N) antigen levels remains to be fully elucidated. In the present study, we attempted to elucidate the usefulness and clinical significance of the serum N antigen levels.

Methods: We measured the serum N antigen levels in 391 serum samples collected from symptomatic patients with a confirmed diagnosis of COVID-19 and 96 serum samples collected from patients with non-COVID-19, using a fully automated chemiluminescence immunoassay analyzer.

Results: Receiver operating characteristic analysis identified the optimal cutoff value of the serum N antigen level (cutoff index, based on Youden’s index) as 0.255, which yielded a sensitivity and specificity for the diagnosis of COVID-19 of 91.0 and 81.3%, respectively. The serum N antigen levels were significantly higher in the patient groups with moderate and severe COVID-19 than with mild disease. Moreover, a significant negative correlation was observed between the serum N antigen levels and the SARS-CoV-2 IgG antibody titers, especially in patients with severe COVID-19.

Conclusion: Serum N antigen testing might be useful both for the diagnosis of COVID-19 and for obtaining a better understanding of the clinical features of the disease.

Characteristics of patients with suspected COVID-19 pneumonia and repeatedly negative RT-PCR

Objectives

Challenges remain and there are still a sufficient number of cases with epidemiological, clinical features and radiological data suggestive of COVID-19 pneumonia that persist negative in their RT-PCR results. The aim of the study was to define the distinguishing characteristics between patients developing a serological response to SARS-CoV-2 and those who did not.

Methods

RT-PCR tests used were TaqPath 2019-nCoV Assay Kit v1 (ORF-1ab, N and S genes) from Thermo Fisher Diagnostics and SARS-COV-2 Kit (N and E genes) from Vircell. Serological response was tested using the rapid SARS-CoV2 IgG/IgM Test Cassette from T and D Diagnostics Canada and CMC Medical Devices and Drugs, S.L, CE.

Results

In this cross-sectional study, we included a cohort of 52 patients recruited from 31 March 2020 to 23 April 2020. Patients with positive serology had an older average age (73.29) compared to those who were negative (54.82) (P<0.05). Sat0₂ in 27 of 34 patients with positive serology were below 94% (P<0.05). There was a frequency of 1.5% negative SARS-CoV-2 RT-PCRs during the study period concurring with 36.7% of positivity.

Conclusion

Clinical features and other biomarkers in a context of a positive serology can be considered crucial for diagnosis.

Assessing the accuracy of an imperfect dichotomous test in a multiple testing context

In this paper we address the situation where a well-established, but invasive, expensive or time-consuming diagnostic test may be replaced by multiple repetitions of a different diagnostic test which is known to be imperfect but less invasive or expensive. With an imperfect diagnostic test repeated several times on the same patient, we first introduce the sensitivity and specificity of that test for any given number of test repetitions. We also derive the corresponding asymptotic limits for sensitivity and specificity as the number n of test repetitions increases. Given those limits, we then derive sharp upper bounds for the differences between the sensitivity and specificity, obtained for a given number n of test repetitions, and the corresponding asymptotic limit. Specifically, we provide exact rates for the convergence of the empirical sensitivities and specificities. Our analysis is motivated, among other things, by the current discussion on rapid SARS-CoV-2 testing where the real-time polymerase chain reaction (RT-PCR) test may be replaced with a sequence of rapid lateral-flow antigen tests.