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Intro: Different vaccines against COVID-19 have been approved by the World Health Organization (WHO) at different stages, however, limited data is available on long-term kinetics of antibodies induced by vaccines. This study was performed to investigate the persistence and dynamicity of BBV-152 (Covaxin)- and AZD1222 (Covishield)-induced immunoglobulin-G (IgG) antibodies over the year and neutralizing antibodies' status after the one-month post booster dose. Method(s): This 52-week longitudinal cohort study documented antibody persistence and neutralizing antibody status among 278 health-care workers (HCWs) from four different healthcare and research facilities in Odisha, enrolled in January 2021 and continued until March 2022. An automated chemiluminescence immune assay (CLIA) platform from Abbott Diagnostics was used to quantify IgG antibodies against SARS-CoV-2's spike receptor-binding domain (RBD) and a surrogate virus neutralization test (sVNT) was performed by enzyme-linked immunosorbent assay (ELISA). If any participants developed any symptoms of COVID-19, nasopharyngeal swabs were collected and sent to ICMR- RMRC, Bhubaneswar for RT-PCR confirmation. Finding(s): Among the 243 participants, 119 HCWs (48.97%) were Covaxin recipients and the remaining 124 (51.02%) were Covishield recipients. During the seven follow- ups, 104 participants (42.79%) were identified as vaccine breakthrough cases. In 139 non-infected HCWs, the median antibody titer significantly waned after ten months of double dose, both for Covaxin (342.7 AU/mL at DD1 vs 43.9 AU/mL at DD10) and Covishield (2325.8 AU/mL at DD3 vs 595.2 AU/mL at DD10). No statistically significant differences in antibody titers were observed based on age, gender, comorbidities, and blood groups. The median inhibition activity of sVNT was increased significantly for Covaxin and Covishield booster recipients. Among the booster dose recipients, 24 had breakthrough cases by the Omicron variant. Conclusion(s): Results of this longitudinal cohort study can be used to implement vaccination strategies and could also aid in tracking and designing vaccine mandates to minimize vaccine escape.Copyright © 2023
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Active micro-mobility decreases traffic, bolsters personal health, and helps communities thrive by protecting the environment Moreover, sustainable micro-mobility demand is expected to get boosted in the present and post-COVID society. In this work we highlight the micro-mobility modes of walkability and bicycling to city administrators controlling urban city-space, by adapting the mobility parameters and their use cases through a map-based interface. Software tools and web-based applications are introduced for easy policy decisions by city managers. Present study scope is circumscribed by exploration of different parameters in traditional and state of art data science models, for resource planning like cycle usage prediction and planning. These parameters show hazard safe-distance pedestrian flow, optimal resource planning, amenity reach (10 min cycling and walking distance) and mobility using walking and cycling modes. Parameters of the traditional Social Force Model for Pedestrian Dynamics are also inspected, according to COVID social norms, to capture safe pedestrian flow density. Finally, the analysis of two case studies, of Bhubaneshwar city and New Delhi, in India, are discussed for policy suggestions to enhance mobility via sustainable micro-mobility modes. The developed system assists managers in decisions based on urban data intelligence, and at user end eases commute related mental tension, anxiety and dependencies. The developed application is running live on our server maintained at Edinburgh University. © 2022 ACM.
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The COVID-19 outbreak resulted in multiple waves of infections that have been associated with different SARS-CoV-2 variants. Studies have reported differential impact of the variants on respiratory health of patients. We explore whether acoustic signals, collected from COVID-19 subjects, show computationally distinguishable acoustic patterns suggesting a possibility to predict the underlying virus variant. We analyze the Coswara dataset which is collected from three subject pools, namely, i) healthy, ii) COVID-19 subjects recorded during the delta variant dominant period, and iii) data from COVID-19 subjects recorded during the omicron surge. Our findings suggest that multiple sound categories, such as cough, breathing, and speech, indicate significant acoustic feature differences when comparing COVID-19 subjects with omicron and delta variants. The classification areas-under-the-curve are significantly above chance for differentiating subjects infected by omicron from those infected by delta. Using a score fusion from multiple sound categories, we obtained an area-under-the-curve of 89% and 52.4% sensitivity at 95% specificity. Additionally, a hierarchical three class approach was used to classify the acoustic data into healthy and COVID-19 positive, and further COVID-19 subjects into delta and omicron variants providing high level of 3-class classification accuracy. These results suggest new ways for designing sound based COVID-19 diagnosis approaches. Copyright © 2022 ISCA.
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In this paper, we describe an approach for representation learning of audio signals for the task of COVID-19 detection. The raw audio samples are processed with a bank of 1-D convolutional filters that are parameterized as cosine modulated Gaussian functions. The choice of these kernels allows the interpretation of the filterbanks as smooth band-pass filters. The filtered outputs are pooled, log-compressed and used in a self-attention based relevance weighting mechanism. The relevance weighting emphasizes the key regions of the time-frequency decomposition that are important for the downstream task. The subsequent layers of the model consist of a recurrent architecture and the models are trained for a COVID-19 detection task. In our experiments on the Coswara data set, we show that the proposed model achieves significant performance improvements over the baseline system as well as other representation learning approaches. Further, the approach proposed is shown to be uniformly applicable for speech and breathing signals and for transfer learning from a larger data set. Copyright © 2022 ISCA.
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The COVID-19 pandemic has accelerated research on design of alternative, quick and effective COVID-19 diagnosis approaches. In this paper, we describe the Coswara tool, a website application designed to enable COVID-19 detection by analysing respiratory sound samples and health symptoms. A user using this service can log into a website using any device connected to the internet, provide there current health symptom information and record few sound sampled corresponding to breathing, cough, and speech. Within a minute of analysis of this information on a cloud server the website tool will output a COVID-19 probability score to the user. As the COVID-19 pandemic continues to demand massive and scalable population level testing, we hypothesize that the proposed tool provides a potential solution towards this. Copyright © 2022 ISCA.
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The Second Diagnosis of COVID-19 using Acoustics (DiCOVA) Challenge aimed at accelerating the research in acoustics based detection of COVID-19, a topic at the intersection of acoustics, signal processing, machine learning, and healthcare. This paper presents the details of the challenge, which was an open call for researchers to analyze a dataset of audio recordings consisting of breathing, cough and speech signals. This data was collected from individuals with and without COVID-19 infection, and the task in the challenge was a two-class classification. The development set audio recordings were collected from 965 (172 COVID-19 positive) individuals, while the evaluation set contained data from 471 individuals (71 COVID-19 positive). The challenge featured four tracks, one associated with each sound category of cough, speech and breathing, and a fourth fusion track. A baseline system was also released to benchmark the participants. In this paper, we present an overview of the challenge, the rationale for the data collection and the baseline system. Further, a performance analysis for the systems submitted by the 21 participating teams in the leaderboard is also presented. © 2022 IEEE
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Background: There are limited data on how COVID-19 disease severity and vaccination throughout different trimesters in pregnancy impact maternal neutralizing antibody responses and transplacental transfer to the neonate at birth. Further characterization of the antibody response of in utero SARS-CoV-2 may inform vaccination schedules in pregnancy in order to optimize maternal and neonatal protection. Methods: The COVID-19 Outcomes in Mother-Infant Pairs (COMP) study is a longitudinal cohort of mother-infant dyads diagnosed with PCR-confirmed SARS-CoV-2 at any point during pregnancy. Maternal and cord sera from delivery, as well as infant sera collected at 24 hours of life, were analyzed by enzyme-linked immunosorbent assay (ELISA) for IgA, IgG, and IgM targeting receptor binding domain (RBD) of the SARS-CoV-2 spike protein. Neutralizing antibody (NAb) activity against the original L strain was evaluated in a subset of unvaccinated mother-infant dyads with evidence of IgG transfer or history of severe/critical COVID-19 in pregnancy. Results: Among 115 pregnant women, the NIH COVID-19 severity of illness categories were: 12% asymptomatic, 70% mild/moderate, 11% severe/critical disease, and 7% vaccinated prior to delivery following recovery. Fifty percent of the cohort was diagnosed in the 3rd trimester, and the median diagnosis date to delivery was 61.5 days (IQR 27.75-122.25). The majority (74%) of the cohort produced all three anti-SARS-CoV-2 isotypes, although 5% had no detectable antibody class. Transplacental transfer ratios increased with increasing duration between onset of infection and delivery (Figure 1, r2=0.17). Infant IgG levels (ng/mL) were the highest among neonates born to vaccinated mothers (Figure 1), and maternal IgG levels increased with disease severity, although vaccination elicited a comparable maternal antibody response to severe/critical disease (Figure 1). Among 50 maternal specimens, 80% demonstrated in vitro neutralization activity, and 52% of 33 neonatal specimens had NAb. Conclusion: While transplacental transfer of IgG was high with natural infection and correlates with increasing duration between onset of infection and delivery, only half of analyzed neonatal specimens demonstrated in vitro neutralization activity. Further research is needed to characterize the functionality and kinetics of both maternal and neonatal antibody responses elicited by in utero SARS-CoV-2 natural infection compared with COVID-19 vaccination.
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Time-series forecasting is a vital concern for any data having temporal variations. Comparing with the other conventional time-series methodologies, the fuzzy time-series (FTS) proved its superiority. Substantial research using time-series forecasting to predict the stock index data has been found in the earlier works. The fuzzy sets approach alone cannot explain the data thoroughly. In this article, we have proposed three different methods of time-series forecasting. The first method is based on a rough set of FTS, a rule induction-based method;the second method is based on intuitionistic FTS. The last method is the extension of the second method using differential evolution. In the first model, a fuzzy algorithm based on rules is used to derive prediction rules from the time-series data and adopt an adaptive expectation model that replaces the fuzzy logical relationships or groups. In the second method, to split the universe of discourse into a non-uniform interval, a clustering algorithm-based intuitionistic fuzzy approach is used, taking care of the membership and non-membership function. Finally, the last method has been tuned for a better outcome using differential evolution. To examine the results, contrast analyses on the Taiwan stock exchange data and daily cases of COVID-19 pandemic prediction have been carried out. The outcome of the proposed approaches validates that the first and second techniques, showing promising results. However, the third method outperforms the other methods and the present techniques concerning the root-mean-square error metric. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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In March 2020, the UN Secretary General issued a call for a global ceasefire to help tackle the COVID-19 pandemic. This call was expected to result in a variety of responses from governments, diplomats, armed groups, NGOs, humanitarian actors, and mediators. Since these organizations are typically focused on specific countries and contexts, it was important to provide them and researchers of conflict and peacemaking dynamics with clear, concise, and well-presented data on the full variety of conflict parties' responses to the UNSG's call, the COVID-19 pandemic and to track the impacts of the pandemic on attempts to end armed conflict. Our tracker, called 'Ceasefires in a time of COVID-19' supports these efforts and SDG 16, i.e., promotion of just, peaceful, and inclusive societies. It features a timeline, an interactive map, and a search tool that displays qualitative data about the ceasefires and related events. This tool is unique in its application, bringing together ceasefires declarations and the COVID-19 infection rates from the Johns Hopkins COVID-19 database, and in its design, with input from the academic and practitioner communities. In this paper, we further describe the methodology used in designing the tool and argue in favor of broad interdisciplinary and cross-industry participation in dataset and user interface design, in order to reflect the requirements of the interested publics.
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The goal of the 20th International Workshop on Data Mining in Bioinformatics (BIOKDD 2021) is to encourage KDD researchers to tackle the numerous problems and challenges in Bioinformatics using Data Mining technologies. Based on the organizers' expertise and the BIOKDD communities, BIOKDD 2021 features the theme of "Artificial Intelligence in Medicine". This topic focuses on the use of machine learning and data mining techniques for the analysis of large amounts of heterogeneous, complex, biological and medical data, with a particular focus on deep learning methods that have seen rapid advance and wider adoption in Bioinformatics (e.g., DeepVariant, AlphaFold 2). We also particularly welcome COVID-19 related research. The key goal is to accelerate the convergence between Data Mining and Bioinformatics communities to expedite discoveries in basic biology, medicine and healthcare. © 2021 Owner/Author.
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Stage 5 covid-19 case with ground glass effect of lungs is considered as fatal/end stage. An Indian medicine (Ayurvedic\herbal) made of the dermis of the indo native Punica Granatum (dalim) was given. Named AVIR (anti virus india research). Contains ellagic aacid & ellagitannins (e.acid {gallagic group} being the principal therapeutic moiety. X-Ray & HRCT were availed pre to treatment. X-Ray only post recovery. Resulted in eventless smooth swift recovery. The results are presented & discussed. Posits as a possible for home remedy. Rapid Brief Communication.
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Contact tracing has been a main topic of conversation in the COVID-19 pandemic. While implementation of app-based contact tracing can be beneficial, it raises concerns of privacy and confidentiality. To better understand how these issues were addressed, a qualitative study was conducted which analyzes the current status of contact tracing apps from Iceland, Italy, Germany, India, Singapore, Japan, and four states within the United States. The comparisons made amongst the contact tracing apps will be surveyed across numerous criteria. The results show contact tracing apps are able to assist in the COVID-19 caseloads by determining self-isolation periods. Future developments can change these apps into a tool for returning to normalcy that may require more user information disclosure, but limited protections of privacy and confidentiality issues have not been addressed at a worldwide level. © 2021 IEEE Computer Society. All rights reserved.
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Background: The kinetics of antibody responses to SARS-CoV-2 infection are not fully understood. We analyzed IgG responses to the SARS-CoV-2 Spike protein receptor binding domain (RBD) in COVID-19 patients admitted to VA Greater Los Angeles (VAGLA) and correlated with clinical outcomes. Methods: Serially admitted patients from March 20-May 10, 2020 with at least one available residual serum specimen were included in this analysis. Serum samples selected for analysis included first, last, and intermediaries spaced ≥ 5 days apart, as available. Anti-RBD IgG was detected with an enzyme immunoassay (EIA) using recombinant RBD protein. Serum from an uninfected individual collected April 2019 was used as control. The average optical density of the control in triplicate plus 3 standard deviations was considered the threshold positive/negative value. The highest dilution above the threshold value was considered the IgG titer. Clinical groups were defined as asymptomatic, moderate/severe (no ICU) or critical (mechanical ventilation, cytokine storm and/or death). Results: Of the 43 consecutive patients admitted to VAGLA with COVID-19 in this analysis, 40 developed detectable RBD IgG responses with maximum inverse titers (MIT) ranging 100-819,200, geometric mean 12,152. Five patients remained asymptomatic but had positive EIAs with median MIT 3200 (IQR 800-3200). Twenty-five had moderate-severe illness with median MIT 25600 (IQR 6400-102400). Ten patients with critical disease had median MIT 38400 (IQR 8800-51200). The median time to positive IgG was 10 days for asymptomatic (IQR 10,10), 4 days for moderate-severe (IQR 3,15), and 7 days for critical (IQR 3.5,14.5). The figure depicts RBD IgG titers over time after onset of symptoms. Asymptomatic patients had a more gradual rate of increase and lower peak titers, while critical patients had the fastest rate of rise and the highest peak titers. Of the 21 patients with samples > 30 days after symptom onset (range 31-67 days), there was no evidence for decrease in anti-RBD IgG. Kinetics of IgG to SARS-CoV-2 receptor binding domain by clinical severity Conclusion: Following infection with SARS-CoV-2, disease severity correlates with both the rate of increase and peak in antibody titers. Anti-RBD IgG titers did not decrease over the observation period.
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Background: Despite numerous outbreaks, antibody responses to SARS-CoV-2 in residents of skilled nursing facilities (SNF) are not well described. We reviewed serological test results in a cohort of SNF residents who had been repetitively screened for SARS-CoV-2 infection by nasopharyngeal swab PCR. Methods: In late March 2019, we identified symptomatic SARS-CoV-2 PCR positive residents at a SNF. In response, all remaining SNF patients were serially screened, and all SARS-CoV-2 PCR positive patients were transferred to the acute care hospital or cohorted in a separate COVID Recovery Unit (CRU) in the SNF. In early June, all SNF residents (SARS-CoV-2 PCR positive and negative) underwent serologic testing for SARS-CoV-2 Spike (S1/S2) IgG (DiaSorin). DiaSorin IgG-positive results for patients that were SARS-CoV-2 PCR-negative were reflexed to nucleocapsid IgG (Abbott). Antibody testing occurred a median of 69 days (63-70 IQR) after PCR positivity. Results: Nineteen SARS-CoV-2 PCR positive residents were identified from the outbreak and an additional 9 were transferred from the acute care hospital to the CRU;1 died and 1 received convalescent plasma leaving 26 SARS-CoV-2 PCR positive residents, including 6 who were asymptomatic, that were eligible for serologic testing. Twenty-four of the 26 were positive for IgG by the DiaSorin assay;one seronegative resident was one of the asymptomatic residents. There were an additional 121 residents in the SNF whose SARS-CoV-2 PCR was negative at least once. Among these 121 SNF residents with negative SARS-CoV-2 RT-PCR, all but two were seronegative by the Diasorin assay. The two seropositive residents had no nucleocapsid antibodies when reflex tested by the Abbott assay. Conclusion: In a limited sample of SNF residents with SARS-CoV-2 PCR positivity, the sensitivity of the Diasorin assay was 92% (24/26) and the specificity was 98% (119/121). None of the residents with negative SARS-CoV-2 PCR had confirmed positive antibody results using reflex testing (DiaSorin/Abbott). Despite high risk exposure in congregate living facilities, we found no evidence of additional SARS-CoV-2 exposure, reinforcing the importance of serial surveillance SARS-CoV-2 testing and early cohorting in SNF settings. (Table Presented).
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Background: The SARS-CoV2 pandemic has increased interest in telemedicine use among hepatology providers On 3/17/2020, all patient visits at VA Greater Los Angeles (GLA) Healthcare System were converted from face-to-face to telephone or video visits in our hepatology clinic As part of a quality improvement effort, we evaluated our ability to reach and provide consultative care to GLA Veterans with cirrhosis during this time Methods: We examined outpatient encounters from 1/1/2020-5/31/2020 in our two weekly hepatology specialty clinics Validated ICD- 10 codes and manual chart review were used to confirm a diagnosis of cirrhosis We abstracted demographics, encounter characteristics (type, successful contact), clinical characteristics, and documented care provided during encounters (health care maintenance counseling;medication refills or changes;ordering of bloodwork, imaging, or endoscopy;and referrals). We classified a patient as having been 'reached' if we were able to complete at least one encounter with them over the study period We compared, with t-tests and Chi-squared tests, our ability to reach patients, patient characteristics, and care delivered before and after 3/17/2020, using STATA 14 2 Results: We identified 145 Veterans with cirrhosis scheduled over 322 encounters Most Veterans were male (98%), with a mean age of 68 (SD=9 9) years;40% were White Thirty six percent of Veterans had decompensated cirrhosis and 21% had hepatocellular carcinoma Before 3/17/2020, 93/104 (89%) of Veterans with scheduled encounters were successfully reached, of which all were in-person consultations Among Veterans with scheduled encounters March 17th, 2020 and after, 83/89 (93%), were reached, of which 87% used telemedicine (84% phone, 3% video) There were no major demographic differences among Veterans reached before and after telehealth implementation (see Table) Rates of health care maintenance counseling, medication changes or refills, as well as ordering of bloodwork, imaging, endoscopy, and new referrals, were similar Veterans reached post-telemedicine adoption had a significantly higher number of encounters with our clinic (1 35 vs 1 07, p=0 01) Conclusion: Adoption of telemedicine practices during the SARS-CoV2 pandemic led to similar rates of care being provided to Veterans pre-pandemic, though over more encounters. We observed no significant disparities in age, gender, or race/ethnicity in reaching Veterans using thispractice However, use of video visits was low These results suggest that telemedicine is a feasible strategy to manage patients with cirrhosis in our clinic However, more information is needed on the reasons for low uptake of video visits, as well as Veteran experiences, adherence, and outcomes with telemedicine-delivered cirrhosis care.