SAFETY OF HETEROLOGOUS mRNA-1273 BOOST AFTER Ad26.COV2.S PRIME IN SOUTH AFRICA

Background: Given the paucity of data on safety and effectiveness of mRNA COVID-19 booster vaccinations in lower income settings with high HIV prevalence, we evaluated a heterologous mRNA-1273 (Moderna) boost after priming with 1 or 2 doses of Ad26.COV2.S (Janssen, Johnson & Johnson) vaccine among health care workers (HCWs) in South Africa. Method(s): SHERPA is an open-label, phase 3 mRNA-1273 booster study, nested in the Sisonke Phase 3b implementation trial, that vaccinated ~500000 HCWs with 1 or 2 doses of Ad26.COV2.S from Feb and Dec 2021. Sisonke participants were offered mRNA-1273 boosters between 23 May and 12 Nov 2022 (median 17 and 8 months after 1 and 2 Ad26.COV2.S, respectively), with data cut-off on 12 Dec 2022. Reactogenicity and adverse events (AEs) were self-reported via an online data entry link shared by SMS with participants 1, 7 and 28 days after boosting. Using national databases analyses are underway to compare effectiveness against COVID-19 infections and severe disease with Sisonke participants who did not receive the booster. Result(s): 12188 HCWs (79.5% female, 28.6% with self-reported previous COVID-19 diagnosis) received a mRNA-1273 booster, of whom 44.6% and 55.4% had received 1 and 2 prior Ad26.COV2.S vaccines in Sisonke, respectively. 3056 (25.2%) reported being HIV positive, more among those receiving only 1 previous Ad26.COV2.S (26.8% vs 23.9%), and 1.4% reported not being on antiretroviral therapy. 17.0% of participants reported hypertension and 6.4% diabetes mellitus. 262 participants (2.1% of women, 2.5% of men) reported 234 reactogenicity events and 95 AEs post-vaccination, with more reported by those with prior COVID-19 infection (3.5% vs 1.6%), HIV negative status (2.5% vs 1.2%) and those who received 2 prior doses of Ad26.COV2.S (2.4% vs 1.8%) (Table). Among 159 (1.3%) reporting injection site reactions the commonest were pain (59.7%), swelling (42.1%) and induration (20.1%). Of 177 (1.5%) systemic reactogenicity events (all grade 1 or 2 severity), the commonest were myalgia (69.5%), headache (67.8%) and fever (37.9%). 14 participants had AEs of special interest or serious AEs, of which 4 (all AESIs of ageusia or anosmia) were deemed related to the booster. 13 COVID-19 infections occurred a median of 125 days post booster vaccination (IQR 90-154) after 3477 person-years of follow up. Conclusion(s): A mRNA-1273 booster administered after 1 or 2 doses of Ad26. COV2.S was well tolerated regardless of HIV status, other chronic conditions or prior COVID-19 infection.

Geospatial variations in socioeconomic conditions and health outcomes in COVID-19 era: insights from South Africa (2020–2022)

South Africa also has the highest burden of coronavirus disease 2019 (COVID-19) related comorbidities in Africa. We aimed to quantify the temporal and geospatial changes in unemployment, food insecurity, and their combined impact on depressive symptoms among South Africans who participated into several rounds of national surveys. We estimated the population-attributable risk percent (PAR%) for the combinations of the risk factors after accounting for their correlation structure in multifactorial setting. Our study provided compelling evidence for immediate and severe effect of the pandemic where 60% of South Africans reported household food insecurity or household hunger, shortly after the pandemic emerged in 2020. Despite the grants provided by the government, these factors were also identified as the most influential risk factors (adjusted odds ratios (aORs) ranged from 2.06 to 3.10, p < 0.001) for depressive symptoms and collectively associated with 62% and 53% of the mental health symptoms in men and women, respectively. Similar pattern was observed among pregnant women and 41% of the depressive symptoms were exclusively associated with those who reported household hunger. However, aORs associated with the concerns around pandemic and vaccine were mostly not significant and ranged from 1.12 to 1.26 which resulted substantially lower impacts on depressive symptoms (PAR%:7%-and-14%). Our findings suggest that South Africa still has unacceptably high rates of hunger which is accelerated during the pandemic. These results may have significant clinical and epidemiological implications and may also bring partial explanation for the low vaccine coverage in the country, as priorities and concerns are skewed towards economic concerns and food insecurity. © 2023, The Author(s).

Population-level impact of beliefs and attitudes on vaccine decision-making in South Africa: results from the COVID-19 Vaccine Survey (2021/2022).

OBJECTIVES: In addition to being home to more than seven million HIV-infected individuals, South Africa also has a high burden of COVID-19 and related comorbidities worldwide. We aimed to identify the most influential "beliefs" and "attitudes" on vaccine decision-making behavior. STUDY DESIGN: This study used panel data from cross-sectional surveys. METHODS: We used the data from Black South Africans who participated in the "COVID-19 Vaccine Surveys" (November 2021 and February/March 2022) in South Africa. Besides standard risk factor analysis, such as multivariable logistic regression models, we also used the modified version of population attributable risk percent and estimated the population-level impacts of beliefs and attitudes on vaccine decision-making behavior using the methodology in multifactorial setting. RESULTS: A total of 1399 people (57% men and 43% women) who participated in both surveys were analyzed. Of these, 336 (24%) reported being vaccinated in survey 2. Overall low perceived risk, concerns around efficacy, and safety were identified as the most influential factors and associated with 52%-72% (<40 years) and 34%-55% (40+ years) of the unvaccinated individuals. CONCLUSION: Our findings highlighted the most influential beliefs and attitudes on vaccine decision-making and their population-level impacts, which are likely to have significant public health implications exclusively for this population.

Reduction in Health Care Facility Onset Catheter-Associated Urinary Tract Infections: A Resident-Led Quality Improvement Initiative

Background. Hospital-acquired catheter-associated urinary tract infection (CAUTI) was estimated to cause 19,700 cases in 2020 across the United States per the Centers for Disease Control and Prevention (CDC). While this is a 25% decrease in reported incidence rates since 2015, ad-hoc changes in care practices and limitations of surveillance definitions brought on by the giant burden of COVID-19 on the healthcare system possibly resulted in underreporting of CAUTIs. In a 290-bed tertiary, community hospital in the Detroit metropolitan area, there was a 200% increase CAUTIs from 2020 (5 CAUTIs) to 2021(16 CAUTIs). A multidisciplinary, resident-led team was assembled to reduce hospital-acquired CAUTIs. Methods. A multi-pronged quality improvement initiative was conducted from January 1, 2021, through March 31, 2022. CAUTIs were identified and reviewed via electronic health records using predefined criteria related to CDC surveillance definitions, urinary catheter insertion indications, laboratory data, and antibiotic use. Plan-Do-Study-Act (PDSA) Cycle model was used to guide the initiative. Thus far one PDSA cycle has been completed. The initial intervention bundle was designed by the multidisciplinary team and led by internal medicine and transitional year residents. The intervention bundle included 1. Provider (including physician and RN) education, 2. Design and implementation of an appropriate urinary catheter practice algorithm, and 3. Expert review of positive urine cultures and CAUTI cases. Results. Baseline data collected from January to December 2021 showed 16 CAUTIs. Post-implementation of the intervention bundle from January to March 2022 resulted in a 75% reduction in CAUTI incidence (1 CAUTI flagged). Conclusion. A targeted intervention bundle improved CAUTI incidence by reducing inappropriate urinary catheter insertion and prolonged removal. Ongoing local initiatives focused on hospital-acquired infections, such as this one, are paramount to the persistent optimization of infection prevention despite national trends.

Reflect of SARS-CoV-2 on the Industrial Sector on a Planetary Scale

SARS-CoV-2 (previously 2019-nCoV), an infectious coronavirus that emerged in late 2019, has triggered a global medical emergency. The WHO titled the new CoV-2 virus a global pandemic on March 11, 2020. Covid-19 isn't merely a worldwide pandemic and public health emergency. The global economy is adjusting to a new normal, but normal is no longer an option. On a worldwide basis, slowed economic activity has reduced demand for industrial items. Several industries have closed their doors. The food, fashion, automotive, information technology, logistics, and manufacturing industries are all important to consider. This article looks at how SARS has affected all the major industries, how they've changed, and what the future holds. © 2022 American Institute of Physics Inc.. All rights reserved.

Indirect Response of the Temperature, Humidity, and Rainfall on the Spread of COVID-19 over the Indian Monsoon Region.

This article examines the role of the meteorological variable in the spread of the ongoing pandemic coronavirus disease 2019 (COVID-19) across India. COVID-19 has created an unprecedented situation for public health and brought the world to a standstill. COVID-19 had caused more than 1,523,242 deaths out of 66,183,029 confirmed cases worldwide till the first week of December 2020. We have examined the surface temperature, relative humidity, and rainfall over five cities: Delhi, Mumbai, Kolkata, Bengaluru, and Chennai, which were severely affected by COVID-19. It is found that the prevailing southwest (SW) monsoon during the pandemic has acted as a natural sanitizer in limiting the spread of the virus. The mean rainfall is ~ 20-40 mm over the selected cities, resulting in an average decrease in COVID cases by ~ 18-26% for the next 3 days after the rainfall. The day-to-day variations of the meteorological parameters and COVID-19 cases clearly demonstrate that both surface temperature and relative humidity play a vital role in the indirect transport of the virus. Our analysis reveals that most COVID-19 cases fall within the surface temperature range from 24 to 30 °C and relative humidity range from 50% to 80%. At a given temperature, COVID-19 cases show a large dependency on the relative humidity; therefore, the coastal environments were more prone to infections. Wavelet transforms coherence analysis of the daily COVID-19 cases with temperature and relative humidity reveals a significant coherence within 8 days.

AI Integrated Blockchain Technology for Secure Health Care-Consent-Based Secured Federated Transfer Learning for Predicting COVID-19 on Wearable Devices

COVID-19 has been a major global challenge these days. The pandemic has changed human life, attitude, and behavior. This pandemic added a burden to people's life and health. With the new variants of SARS-CoV-2, a lot of people are even scared of going to the health centers to get the COVID-19 evaluation in fear of contamination and contagious, which caused the surge in the symptoms at later stages. Data collected across various sources can play an important role in predicting and identifying of COVID-19 virus based on the models and the classifications of this data using the most sophisticated machine learning models. The concern here is accessing or transferring an individual's data from their personal health devices which defers users' privacy. In the recent past, there are a lot of research that has been done these days on how blockchain can help to securely track and transfer the data across trusted sources. Adding to this, federated learning also is helping on-device data usage without any critical data to be transferred to various external sources. The proposed study directs the stability of frequent health status with the help of wearable devices that capture health metrics like heart rate, blood oxygen levels, breathing rate, muscle activities, stress, emotions, movement patterns, sleep activity, precipitation, and mind/cognitive functions with the introduction of the data streams and models that can seamlessly transfer the data, with the assurance of data integrity, privacy, and control which is the scope of this paper. The usage of both the emerging technologies provides a value addition in terms of health data exchange with effective data distribution with decentralized privacy and computation. We have also introduced a consent-based personal health device registration mechanism on a blockchain consensus network with digital identity to allow and take back controls over who can access their data. We believe that this solution and the implementation would help everyone to predict the possible COVID-19 infections keeping data privacy at the most priority.

ROLE OF VENOVENOUS EXTRACORPOREAL MEMBRANE OXYGENATION IN SALVAGE THERAPY FOR CENTRAL AIRWAY OBSTRUCTION: A CASE REPORT

SESSION TITLE: Pulmonary Procedures: Creativity and Complications SESSION TYPE: Rapid Fire Case Reports PRESENTED ON: 10/18/2022 10:15 am - 11:10 am INTRODUCTION: Recent advances in the management of airway disorders have provided additional therapeutic options for pathology, such as central airway obstruction (CAO). Symptomatic CAO has been managed by bronchoscopic interventions with a high risk of airway compromise and respiratory failure. Other alternatives such as mechanical and jet ventilation may not ensure adequate respiratory support during the procedure and cause delays in life-saving treatments. Venovenous extracorporeal membrane oxygenation (VV ECMO) has been used as an adjunct to preserve safety during these airway interventions [1,2]. We present a case of complete tracheal occlusion successfully intervened using VV ECMO support. CASE PRESENTATION: The patient is a 55-year-old male with a history of ventilator-dependent respiratory failure s/p tracheostomy, secondary to post COVID-19 fibrosis, who presented from a long-term acute care facility with worsening hypoxemia. The patient was transferred to the intensive care unit, where he underwent flexible bronchoscopy via the tracheostomy lumen, which did not reveal a patent airway. Orotracheal intubation was unsuccessful as there was complete occlusion of the airway below the vocal cords with abundant granulation tissue. Interventional pulmonology was consulted, and emergent recanalization of the airway with rigid bronchoscopy-mediated debulking was performed. Due to the severity of hypoxemia, cardiothoracic surgery was consulted, and the patient was placed on VV ECMO to support further intervention. The patient was intubated with EFER-DUMON 13 mm rigid bronchoscope. Complete recanalization was achieved using a rigid barrel and forceps with patency of both mainstems and all segmental bronchi. There were no postprocedural complications, and the patient returned to his baseline ventilator settings. DISCUSSION: VV ECMO has been used as an adjunct to preserve safety during high-risk bronchoscopic interventions, primarily in CAO. Acute respiratory decompensation remains a feared complication during these interventions in cases of CAO. Initiating ECMO before these interventions may reduce the incidence of respiratory failure and airway compromise. In a case series, ECMO has been described by Stokes et al. as a supportive measure facilitating such interventions [3]. Further guidelines are required to standardize ECMO initiation as procedural support during airway interventions. CONCLUSIONS: Planned preprocedural ECMO initiation can prevent respiratory emergencies and allow therapeutic high-risk airway interventions. The choices for this patient were stark- either airway recanalization without ECMO bridge with a risk of hypoxic brain injury vs. VV ECMO support and curative airway intervention. In the absence of large-scale data and based on local availability of excellent ECMO support and Interventional Pulmonology, the latter approach was used, leading to successful and safe airway recanalization. Reference #1: Zapol WM, Wilson R, Hales C, Fish D, Castorena G, Hilgenberg A et al.Venovenous bypass with a membrane lung to support bilateral lung lavage. JAMA 1984;251:3269–71. Reference #2: Fung R, Stellios J, Bannon PG, Ananda A, Forrest P. Elective use of venovenous extracorporeal membrane oxygenation and high-flow nasal oxygen for resection of subtotal malignant distal airway obstruction. Anaesth Intensive Care 2017;45:88–91. Reference #3: Stokes JW, Katsis JM, Gannon WD, Rice TW, Lentz RJ, Rickman OB, Avasarala SK, Benson C, Bacchetta M, Maldonado F. Venovenous extracorporeal membrane oxygenation during high-risk airway interventions. Interact Cardiovasc Thorac Surg. 2021 Nov 22;33(6):913-920. doi: 10.1093/icvts/ivab195. PMID: 34293146;PMCID: PMC8632782 DISCLOSURES: No relevant relationships by Vatsal Khanna No relevant relationships by Anurag Mehrotra No relevant relationships by Trishya Reddy No relevant relationships by Bernadette Schmidt

Predicting Covid-19 Trends Using Machine Learning based on Linear and Multiple Linear Regression Model

The COVID-19 pandemic has adversely affected the health and economy of almost all the countries in the world including India. Almost thousands of people are getting affected by this daily. In this paper, analysis of the daily statistics of people who got affected and this proposed work is going to predict the future trend of the active cases in Odisha and India. Machine Learning based forecasting algorithms have proved their significance in generating predictive outcomes which are used to make decisions on actions that are going to happen in the future. ML algorithms have been using for a long time to do this kind of task. This proposed work is going to do analysis and prediction on the dataset which was created by COVID India organization. Linear and Multiple Linear Regression models are used to predict the future trend of active cases and also the number of active cases in fore coming days and to visualize the trend of future active cases. Here, the performance of Linear and Multiple Linear regression models are compared by using the R2score. Linear and Multiple Linear regression got 0.99 and 1.0 as R2scores respectively which shows that these are the strongest prediction models that are used to predict the future active cases of COVID - 19. Both these models acquired remarkable accuracy in COVID - 19 prediction. A strong correlation factor shows that there is a very strong relationship between a dependent variable (Active cases) and independent variables (positive, deceases, recovered cases). © 2022 University of Stockholm. All rights reserved.

Pfizer's inventive Nirmatrelvir/Ritonavir (PaxlovidTM): Another arrow in the quiver against SARS-CoV-2

SARS-CoV-2 was a devastating global pandemic that swept the globe in late 2019, claiming the lives of an estimated 4 million people. Amidst challenging times, we are remembering Martin Luther King Jr.'s remark, "Mankind must put an end to war or war will put an end to mankind." In that sense, scientists are repurposing drugs meticulously to curb the nCOVID-19. New antiviral drugs on the other hand are being developed at unparallel rates. Among those Pfizer's inventive Nirmatrelvir/Ritonavir (PaxlovidTM), which inhibits the main protease (Mpro) of SARS-CoV-2, 3CL protease, will be another arrow in the quiver to mount resistance towards SARS-CoV-2. In patients treated with nirmatrelvir/ritonavir within five days of symptom onset, COVID-19-related hospitalizations and mortality were dramatically reduced. Paxlovid's high oral availability, allows it to be used in both hospitalized and outpatient patients. In this brief review, we presented pharmacokinetic, preclinical, and clinical evidence on Paxlovids for the treatment of moderate nCOVID-19.

Contactless Covid-19 Monitoring System Using IOT

The main purpose of this study is to track down corona virus interactions using the Internet of Things. The sickness is reported to be very contagious when it comes into touch with sick people. High fever, cough, and trouble breathing are the most common signs of COVID19. They've demonstrated how the sickness has evolved to conceal its signs. Because this sickness is highly contagious, it has the potential to spread rapidly, killing thousands of people. And the transmission chain must be identified as a top concern. The Internet of Things are collection that work together to accomplish a goal. Every object has its own identity, which will be used to record main Occurrences serve as a springboard for future learning and judgments. In the medical industry, IoT plays an indisputable role in disease identification and surveillance. A new epidemic is spreading across the globe. Amid a slew of other life-threatening illnesses Despite tight lockdown procedures, COVID-19, a respiratory syndrome virus discovered in 2019, is now posing a significant threat to countries. Conclusions - The authors of this study created a design for an IoT system that collects data from individuals via sensors and sends it to clinicians via mobile phones, computers, and other devices to predict the Covid-19 sickness. The main goal is to predict COVID-19 so that early health surveillance may be provided. Therefore, the writers are able to distinguish between the two. © 2022 IEEE.

Hybrid Classification Model by Using CNN to Detect COVID-19 and Chest Related Problems

The COVID-19 pandemic has created havoc on the lives of many people and their health all over the world. It has been increasing very rapidly, one must find an effective model/method to detect COVID-19 in order to help the Health Care System. Chest X-ray is one of the reliable diagnostic technologies, which helps in the identification of COVID-19. Despite the fact that there are numerous deep learning methodologies for identifying COVID -19, these methodologies are useless if they only detect one type of illness while ignoring the others. This study proposed a Hybrid Classification model based on CNN (Convolutional Neural Network) for more efficient detection of COVID-19 from Chest X-Rays. Using CNN, this study differentiates COVID-19 affected chest X-Ray images from normal chest X-Ray images and eight additional chest disorders (Cardiomegaly, Atelectasis, Infiltration, Effusion, Nodule, Pneumonia, Mass, Pneumothorax). The Hybrid Classification Model contains two classifiers, Classifier-1 and Classifier-2. In Classifier-1, it contains the information about Normal Chest X-rays images and chest X-ray images that have been affected by COVID-19 and whereas in the Classifier-2, it contains the information about other 8 chest diseases. For getting highest accuracy of Classifier-1 and Classifier-2 models, this research work utilizes several models i.e., ResNet50, InceptionResNetV2, VGG16, DensNet121 and Mobile Net. Based on all these models, this research work considers ResNet50 for Classifier-1, and DensNet121 for Classifier-2, Because these two models had given the highest accuracy compared to other models. © 2022 IEEE.

Bidirectional Encoding Contextual Approach for Identification of Relevant Document in Corpus

With the increasing advance of computer and information technologies, numerous documents have been published online as well as offline, and as new research fields have been continuingly created, users have a lot of trouble in finding their interesting documents. These documents can be in the form of blogs, research papers, and thesis. There is a heterogeneous set of documents which has information linked with each other. Traditional search is about taking an input of the query text from the user and checking if the subsequence is a part of any sentence in the set of documents and showing the set to the user. In this paper, we have proposed a Bidiection Encoding Contextual algorithm that can be applied to different types of documents and do a semantic search across the corpus. The algorithm used to understand the meaning of the word, their relative relationship between other words and provide the user with the documents that not just has the textual reference but also contain the relative meaning of the query. On the COVID-19 dataset, test been performed on the reliability of the interpretation through the function of linguistic similarities. The experimental findings demonstrate the strong association between the conceptual term interpretation of human consciousness in the role of measuring the similarity. Experiments show that the Bidirectional Encoding Contextual model has the best accuracy of 85.6% when compared with other traditional models like RNN, CNN and LSTM models.

ANATIONAL SEROPREVALENCE SURVEY of SARS-CoV-2 ANTIBODIES in SOUTH AFRICA: 2020-2021

Background: South Africa is one of the African countries most affected by the COVID-19 pandemic. SARS-CoV-2 seroprevalence surveys provide valuable epidemiological information given the existence of asymptomatic cases. We report the findings of the first nationwide household-based population estimates of SARS-CoV-2 seroprevalence among people aged 12 years and older in South Africa. Methods: The survey used a cross-sectional multi-stage stratified cluster design undertaken over two separate time periods (November 2020-February 2021 and April-June 2021) which coincided with the second and third waves of the pandemic in South Africa. The Abbott® and Euroimmun® ani-SARS CoV-2 antibody assays were used to test for SARS-CoV-2 antibodies, the latter being the final result. The survey data was weighted with final individual weights benchmarked against 2020 mid-year population estimates by age, race, sex, and province. Frequencies were used to describe characteristics of the study population and SARS-CoV-2 seroprevalence. Bivariate and multivariate logistics regression analysis were used to identify factors associated with SARS-CoV-2 seropositivity. Results: 13640 participants gave a blood sample. The SARS-CoV-2 seroprevalence using the Euroimmun assay was 19.6% (95% CI 17.9-21.3) over the study period, translating to an estimated 8 675 265 (95% CI 7 508 393-9 842 137) estimated infections among people aged 12 years and older across South Africa by June 2021. Seroprevalence was higher in the Free State (26.8%), and Eastern Cape (26.0%) provinces (Figure). Increased odds of seropositivity were associated with prior PCR testing [aOR=1.29 (95% CI: 0.99-1.66)], being female [aOR=1.28 (95% CI 1.00-1.64), p=0.048] and hypertension, [aOR=1.28 (95% CI 1.00-1.640, p=0.048]. Conclusion: These findings highlight the burden of infection in South Africa by June 2021, and support testing strategies that focus on individuals with known exposure or symptoms since universal testing is not feasible. Females and younger people were more likely to be infected suggesting need for additional strategies targeting these populations. The estimated number of infections was 6.5 times higher than the number of SARS-CoV-2 cases reported nationally, suggesting that the country's testing strategy and capacity partly explain the dynamics of the pandemic. It is therefore essential to bolster testing capacity and to rapidly scale up vaccinations in order to contain the spread of the virus in the country.

PERFORMANCE of SARS-CoV-2 ANTIBODY ASSAYS in A NATIONAL SEROSURVEY in SOUTH AFRICA

Background: Accurate and reliable serological assays are essential for epidemiological surveillance of SARS-CoV-2. Several commercial anti-SARS assays are available and use cases for serological testing includes surveillance. However, there is growing evidence of varying performance of SARS-CoV-2 assays dependent of their format. We compare the performance of 3 different assays used in a national serosurvey undertaken between April and June 2021, in South Africa before widescale vaccination roll out. Methods: Venous blood samples from participants ≥12 years were transported under cold chain to a central testing laboratory within 24 hours of collection. Samples were tested for SARS CoV-2 antibodies with the Abbott nucleocapsid (NC)-based Architect anti-SARS CoV-2 chemiluminescent microparticle immunoassay (CMIA), the EuroImmun Spike (S)-based assay and the Roche total IgG NC-based Elecsys Anti-SARS-CoV-2 electrochemiluminescence immunoassay (ECLIA) on the Cobas e411 platform. We compared antibody detection proportions. Results: 8146 participants (median age 40 years, IQR 26-55) 5.6% of whom reported ≥1 SARS-CoV-2 symptom in the preceding 3 months gave a blood sample. Samples were tested on the Abbott assay with different cut-offs:-15.5% tested positive at the 1.40 cut-off and 26.8% at the 0.49 lower cut-off. 21.6% of the samples tested positive on the Euroimmun and 39.0% tested positive on the Roche assay (Table). 286 samples were from respondents self-reporting a prior positive PCR test, and among them 149(52.1%), 156(54.6%), and 206(72.3%) were positive on the Abbott (1.40 cut-off), Euroimmun and Roche assays respectively. 116/286(40.6%) of these were positive on all three assays and with 21(7.3%) positive on Roche only. 224/286(78.3%) of those reporting prior PCR test positivity were positive at the lower Abbott cut-off, with 47(16.4%) positive on Abbott only. Conclusion: These samples collected before wide scale vaccination roll out in South Africa show variable performance of these assays with the Roche NC assay detecting more infections that both the Abbott NC assay(0.40 cut-off) and the Euroimmun S assay.This could be reflective of seroreversion previously reported with Abbott and Euroimmun, and the greater sensitivity of Roche assay targeting the more abundant NC as an epitope. Use of direct, double Antigen-sandwich-based assays that are stable and have increased sensitivity over time may be optimal to detect both natural and vaccine-induced immunity in serosurveys.

SAFETY and EFFECTIVENESS of the Ad26.COV2.S VACCINE in SOUTH AFRICA

Background: The Sisonke Phase IIIB open-label implementation study vaccinated health care workers (HCWs) with the single dose Ad26.COV2.S vaccine during two phases of the South African Covid-19 epidemic, dominated first by the Beta followed by the Delta variant of concern. Methods: HCWs were vaccinated over 3 months (17 February-17 May 2021). Safety was monitored by self-reporting, facility reporting and linkage to national databases. Vaccine effectiveness (VE) against Covid-19 related hospitalisation, hospitalisation requiring critical or intensive care and death, ascertained 28 days or more post vaccination was assessed up until 17 July 2021. Nested sub-cohorts (A and B) from two national medical schemes were evaluated to assess VE using a matched retrospective cohort design. Results: Over the 3-month period, 477234 HCWs were vaccinated in 122 vaccination sites across South Africa. VE derived from the sub-cohorts comprising 215 813 HCWs was 83% (95% CI 75-89) to prevent Covid-19 deaths, 75% (95% CI 69-82) to prevent hospital admissions requiring critical or intensive care and 67% (95% CI 62-71) to prevent Covid-19 related hospitalisations. The VE was maintained in older HCWs and those with comorbidities including HIV infection. VE remained consistent throughout the Beta and Delta dominant phases of the study. 10279 adverse events were reported and 139 (1.4%) were serious, including two cases of thrombosis with thrombocytopenia syndrome and four cases of Guillain-Barré syndrome who recovered. Conclusion: The single dose Ad26.COV2.S was safe and effective against severe Covid-19 disease and death post-vaccination, and against both Beta and Delta variants providing real-world evidence for its use globally.

Navigating Education in the Context of COVID-19 Lockdowns and School Closures: Challenges and Resilience Among Adolescent Girls and Young Women in South Africa

Gender related vulnerabilities and inequalities place female learners at high risk of school disengagement due to COVID-19 disruptions. Understanding the impacts of school closures and educational disruptions on female learners in South Africa is critical to inform appropriate, gender-sensitive policies, and programs, to mitigate further exacerbation of educational inequalities. We examined the effects that COVID-19 and lockdowns have had on the educational experiences of adolescent girls and young women (AGYW) aged 15–24, in six districts of South Africa characterized by high rates of HIV, teenage pregnancy and socio-economic hardship. Following a concurrent triangulation mixed-methods approach, we conducted a cross-sectional survey with 515 AGYW, and qualitative interviews with 50 AGYW. More than half of survey participants enrolled in education had been unable to continue with their studies. Factors associated with educational disruption included low socio-economic status, lack of cell phone access and household food insecurity. Qualitative narratives included challenges with online learning and studying at home in resource restricted settings, and insufficient support from schools and teachers. However, despite multiple barriers to continuing education, some AGYW demonstrated educational resilience, enabled by psychosocial and structural support, and resource access. Our findings lend support to an emerging evidence base showing that the closure of schools and tertiary institutions, combined with challenging home environments, and a lack of access to appropriate technology, has disproportionately impacted the most vulnerable AGYW, exacerbating pre-existing educational inequalities within the South African education system. Addressing structural barriers to educational equity, particularly in the pandemic context, including access of technology and the internet, is urgent. Copyright © 2022 Duby, Jonas, Bunce, Bergh, Maruping, Fowler, Reddy, Govindasamy and Mathews.

Deep Learning-based Mixed Data Approach for COVID-19 Detection

The outbreak of COVID-19 has caused an exponential increase in mortality rate globally and has dealt a devastating blow to nations all over the world. This unforeseen calamity needs to be tackled and early detection of this disease could help in this regard. Several research studies used Chest X-rays and CT scans to detect the disease, which can be made cost-effective by using cough samples. These systems can further be refined by using multiple health parameters to provide more accurate results. In this view, this paper proposes a constructive way for the early detection of COVID-19 by considering cough samples and clinical data (Saturation of Peripheral Oxygen (SpO2) level, body temperature, heart rate, and symptoms). The dataset was collected by using a Raspberry Pi and an online questionnaire. In this paper, we put forward two approaches being Manual feature extraction and Mixed data neural networks (Multi-layer Perceptron and Convolutional Neural Networks) for efficiently handling the problem. To help the user access the system more comfortably, a mobile application was developed. The Mixed data neural networks yielded the best performance with an Area Under the Curve (AUC) score of 0.94 and an accuracy of 0.85. © 2021 IEEE.