Trend of Social Media News: A Viewpoint of COVID-19 Tweets Using Natural Language Processing -

The meteoric rise of social media news during the ongoing COVID-19 is worthy of advanced research. Freedom of speech in many parts of the world, especially the developed countries and liberty of socialization, calls for noteworthy information sharing during the panic pandemic. However, as a communication intervention during crises in the past, social media use is remarkable; the Tweets generated via Twitter during the ongoing COVID-19 is incomparable with the former records. This study examines social media news trends and compares the Tweets on COVID-19 as a corpus from Twitter. By deploying Natural Language Processing (NLP) methods on tweets, we were able to extract and quantify the similarities between some tweets over time, which means that some people say the same thing about the pandemic while other Twitter users view it differently. The tools we used are Spacy, Networkx, WordCloud, and Re. This study contributes to the social media literature by understanding the similarity and divergence of COVID-19 tweets of the public and health agencies such as the World Health Organization (WHO). The study also sheds more light on the COVID-19 sparse and densely text network and their implications for the policymakers. The study explained the limitations and proposed future studies.

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On the Prediction of Isolation, Release, and Decease States for COVID-19 Patients: A Case Study in South Korea -

A respiratory syndrome COVID-19 pandemic has become a serious public health issue nowadays. The COVID-19 virus has been affecting more than twenty-two million people worldwide. Some of them have recovered and have been released. Others have been isolated and few others have been unfortunately deceased. In this paper, we apply and compare different machine learning approaches such as decision tree models, random forest, and multinomial logistic regression to predict isolation, release, and decease states for COVID-19 patients in South Korea. The prediction can help health providers and decision makers to distinguish the states of infected patients based on their features in early intervention to take an action either by releasing or isolating the patient after the infection. The proposed approaches are evaluated using Data Science for COVID-19 (DS4C) dataset. An analysis of DS4C dataset is also provided. Experimental results and evaluation show that multinomial logistic regression outperforms other approaches with 95% in a state prediction accuracy and a weighted average F1-score of 95%.

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Saliva viral load is a dynamic unifying correlate of COVID-19 severity and mortality -

While several clinical and immunological parameters correlate with disease severity and mortality in SARS-CoV-2 infection, work remains in identifying unifying correlates of coronavirus disease 2019 (COVID-19) that can be used to guide clinical practice. Here, we examine saliva and nasopharyngeal (NP) viral load over time and correlate them with patient demographics, and cellular and immune profiling. We found that saliva viral load was significantly higher in those with COVID-19 risk factors; that it correlated with increasing levels of disease severity and showed a superior ability over nasopharyngeal viral load as a predictor of mortality over time (AUC=0.90). A comprehensive analysis of immune factors and cell subsets revealed strong predictors of high and low saliva viral load, which were associated with increased disease severity or better overall outcomes, respectively. Saliva viral load was positively associated with many known COVID-19 inflammatory markers such as IL-6, IL-18, IL-10, and CXCL10, as well as type 1 immune response cytokines. Higher saliva viral loads strongly correlated with the progressive depletion of platelets, lymphocytes, and effector T cell subsets including circulating follicular CD4 T cells (cTfh). Anti-spike (S) and anti-receptor binding domain (RBD) IgG levels were negatively correlated with saliva viral load showing a strong temporal association that could help distinguish severity and mortality in COVID-19. Finally, patients with fatal COVID-19 exhibited higher viral loads, which correlated with the depletion of cTfh cells, and lower production of anti-RBD and anti-S IgG levels. Together these results demonstrated that viral load, as measured by saliva but not nasopharyngeal, is a dynamic unifying correlate of disease presentation, severity, and mortality over time.

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COVID-HEART: Development and Validation of a Multi-Variable Model for Real-Time Prediction of Cardiovascular Complications in Hospitalized Patients with COVID-19 -

Cardiovascular (CV) manifestations of COVID-19 infection carry significant morbidity and mortality. Current risk prediction for CV complications in COVID-19 is limited and existing approaches fail to account for the dynamic course of the disease. Here, we develop and validate the COVID-HEART predictor, a novel continuously-updating risk prediction technology to forecast CV complications in hospitalized patients with COVID-19. The risk predictor is trained and tested with retrospective registry data from 2178 patients to predict two outcomes: cardiac arrest and imaging-confirmed thromboembolic events. In repeating model validation many times, we show that it predicts cardiac arrest with an average median early warning time of 18 hours (IQR: 13-20 hours) and an AUROC of 0.92 (95% CI: 0.91-0.92), and thromboembolic events with a median early warning time of 72 hours (IQR: 12-204 hours) and an AUROC of 0.70 (95% CI: 0.67-0.73). The COVID-HEART predictor is anticipated to provide tangible clinical decision support in triaging patients and optimizing resource utilization, with its clinical utility potentially extending well beyond COVID-19.

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Remote Cognitive Assessment in Psychiatry: A Scoping Review -

Background: Many individuals living with mental illness present cognitive deficits and reasoning biases negatively impacting clinical and functional trajectories. Within the context of the current COVID-19 pandemic, clinicians and researchers must adapt traditional in-person assessments for remote delivery, but little guidance is available for this endeavor. To synthesize the literature and facilitate guideline development, we conducted a scoping review of remote cognitive assessment in psychiatry. Objectives: We sought to (1) map the literature, (2) identify potential barriers and facilitators, and (3) highlight current knowledge gaps of remote cognitive assessment in psychiatry. Eligibility Criteria: Articles were selected based on the following criteria: (a) peer-reviewed; (b) included individuals with a diagnosis of a severe mental illness (e.g., schizophrenia-spectrum disorders); and (c) reported on remote assessment of cognitive capacity or cognitive biases. Information and Evidence Sources: Our literature search was conducted using the OVID and EBSCO databases. Additionally, repositories of tests (PsycTESTS, Health and Psychosocial Instruments, Mental Measurements Yearbook) were searched, experts in the field were contacted for unpublished findings, and reference lists of selected articles were examined for additional studies. Evidence sources included original research articles, reviews and letters to the editor. Charting Methods: Data extraction was performed according to a predetermined form, defining study parameters, remote measure characteristics, psychometric properties, facilitators, barriers, and future directions. Extracted data were synthesized and illustrated using the logic model methodology. Results: Of 25,374 retrieved articles, 32 articles were selected, including 80 different measures across 11 cognitive domains and four remote platforms. Remote measures were generally comparable to traditional versions, though psychometric properties were infrequently reported. Facilitators included standardized procedures and wider recruitment, whereas barriers included imprecise measure adaptations, technology inaccessibility, low patient engagement and poor digital literacy. Conclusions: Our review identified several potential remote cognitive assessment measures used in psychiatry across all cognitive domains. However, there is a need for more rigorous validation of these measures as well as development of new open source alternatives, and consideration of potential influential factors, such as sex and gender. We discuss implications for conducting remote cognitive assessment in psychiatry and fostering high quality research using digital technologies.

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Pre-pandemic suicidal ideation and preventive behavior during COVID-19: A prospective cohort study -

Background: Cross-sectional studies have shown associations between suicide risk factors and COVID-19 preventive behaviors. Aims: We investigated the impact of suicidal ideation on COVID-19 preventive behaviors to augment literature focused on suicide risk factors and COVID-19 preventive health measures. Significant findings may reveal factors that explain dangerous behavior and inspire policy to mitigate negative health outcomes. Method: This study used a prospective observational longitudinal design (n = 6683). A baseline survey (January 24, 2020 = before COVID-19 pandemic, T1) and a follow-up survey (between April 27 and April 30, 2020 = during COVID-19 pandemic, T2) were conducted among the general adult population in Japan. Results: Adjusted for demographic and socioeconomic factors, high suicidal ideation at T1 was associated with significantly low levels of engagement in COVID-19 preventive behaviors at T2. Limitations: As this was an Internet survey and contained selection bias, the sample may not be representative of the general population in Japan. Pandemic severity may impact results in other geographies, reducing the applicability of study findings. Conclusion: Individuals with strong suicidal ideation before the pandemic were less likely to engage in COVID-19 preventive behaviors. These individuals are at risk not only for suicide but also for infectious disease.

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A systematic review and meta-analysis of Anakinra, Sarilumab, Siltuximab and Tocilizumab for Covid-19 -

Background There is accumulating evidence for an overly activated immune response in severe Covid-19, with several studies exploring the therapeutic role of immunomodulation. Through systematic review and meta-analysis, we assess the effectiveness of specific interleukin inhibitors for the treatment of Covid-19. Methods Electronic databases were searched on 7th January 2021 to identify studies of immunomodulatory agents (anakinra, sarilumab, siltuximab and tocilizumab) for the treatment of Covid-19. The primary outcomes were severity on an ordinal scale measured at day 15 from intervention and days to hospital discharge. Key secondary endpoints included overall mortality. Results 71 studies totalling 22,058 patients were included, six were randomised trials. Most explored outcomes in patients who received tocilizumab (59/71). In prospective studies, tocilizumab was associated with improved unadjusted survival (RR 0.83 95%CI 0.72;0.96 I2 = 0.0%), but conclusive benefit was not demonstrated for other outcomes. In retrospective studies, tocilizumab was associated with less severe outcomes on an ordinal scale (Generalised odds ratio 1.34 95%CI 1.10;1.64, I2=98%) and adjusted mortality risk (HR 0.54 95%CI 0.40;0.72, I2 =86.6%). The mean difference in duration of hospitalisation was 0.36 days (95%CI -0.07;0.80, I2=93.8%). There was substantial heterogeneity in retrospective studies, and estimates should be interpreted cautiously. Other immunomodulatory agents showed similar effects to tocilizumab, but insufficient data precluded meta-analysis by agent. Conclusion Tocilizumab was associated with a lower relative risk of mortality in prospective studies, but effects were inconclusive for other outcomes. Current evidence for the efficacy of anakinra, siltuximab or sarilumab in Covid-19 is insufficient, with further studies urgently needed for conclusive findings.

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Vietnam's economy toward a new age: healthcare and environmental challenges -

Vietnam's economy is in a special position facing the COVID-19 pandemic and climate change impacts.

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Digital News Media Coverage on Sexual and Gender-Based Violence in Kenya: Scoping the Impact of the COVID-19 Pandemic -

Background: Sexual and gender-based violence (SGBV) in Kenya is frequently covered in digital news media. As a powerful influencer of public opinion, news media coverage can have a significant societal impact. This scoping review identifies the extent, nature, and themes of all available digital news media coverage on SGBV in Kenya from June 2019 to July 2020. It analyzes changes in coverage since the start of the COVID-19 pandemic. Method: The methodological framework for scoping reviews developed by Arksey and O’Malley (2005) guided the scoping review. The selected articles were analyzed using NVivo. Results: Analysis of the 340 included articles indicates clear trends in news media coverage on SGBV in Kenya. Before the COVID-19 pandemic, trends include high numbers of news reports, feature articles, and opinion pieces on SGBV, female genital mutilation (FGM) as the most covered form of SGBV, and opinion pieces in line with the Kenya Vision 2030 agenda. Since the implementation of the COVID-19 mitigation measures in Kenya, trends include an increase in news media coverage on SGBV, extensive reporting on the causes of the rise in SGBV cases in Kenya, and increased media attention for domestic and sexual violence. Conclusions: Analysis of the news media coverage demonstrates the health advocacy/agenda-setting role of the media. The increased reporting on SGBV since the implementation of the COVID-19 mitigation measures could facilitate broad-based awareness.

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Exposure to the Covid-19 pandemic and generosity -

We report data from an online experiment, which allow us to study whether generosity has changed during the early Covid-19 pandemic. We have gathered data from Spanish participants over a six-day period in which Covid-19-associated deaths in Spain, one of the most affected countries, increased fourfold. In our experiment, participants could donate a fraction of a €100 prize to a charity. Our data are particularly rich in the age distribution and we complement them with daily public information about the Covid-19-related deaths, infections, and hospital admissions. We find that donations decreased in the period under study and scale down with the public information about the life and health impact of the pandemic. The effect is particularly pronounced among older subjects. Our analysis of the mechanisms behind the detected decrease in solidarity highlights the key—but independent—role of expectations about others’ behavior, perceived mortality risk, and (alarming) information in behavioral adaptation.

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COVID-19 seroprevalence among healthcare workers of a large COVID Hospital in Rome reveals strengths and limits of two different serological tests -

In several hospitals worldwide, healthcare workers are currently at the forefront against coronavirus disease 2019 (COVID-19). Since Fondazione Policlinico Universitario A. Gemelli (FPG) IRCCS has been enlisted as a COVID hospital, healthcare workers deployed to COVID wards were separated from those with limited or no exposure, whereas administrative staff was destined to work-from-home. Between June 4 and July 3 2020, an investigation was carried out to evaluate seroprevalence of SARS-CoV-2 IgG antibodies among employees of the FPG using point-of-care (POC) and venous blood tests. Sensitivity, specificity and predictive values were determined with reverse-transcription polymerase chain reaction (RT-PCR) on nasal/oropharyngeal swabs as gold standard. Four thousand, seven hundred seventy-seven participants were enrolled. Seroprevalence was 3.66% using the POC test and 1.19% using venous blood test, with a significant difference between the two (p < 0.05). POC sensitivity and specificity were, respectively, 63.64% (95% confidence interval (CI): 62.20% to 65.04%) and 96.64% (95% CI: 96.05% to 97.13%), while those of the venous blood test were, respectively, 78.79% (95% CI: 77.58% to 79.94%) and 99.36% (95% CI: 99.07% to 99.55%). Among low-risk population, point-of-care9s predictive values were 58.33% (positive) and 98.23% (negative) whereas venous blood test9s were 92.86% (positive) and 98.53% (negative). In conclusion, point-of-care tests have low diagnostic accuracy, while venous blood tests seem to show an overall poor reliability.

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Mathematical assessment of the roles of vaccination and non-pharmaceutical interventions on COVID-19 dynamics: a multigroup modeling approach -

A novel coronavirus emerged in December of 2019 (COVID-19), causing a pandemic that continues to inflict unprecedented public health and economic burden in all nooks and corners of the world. Although the control of COVID-19 has largely focused on the use of basic public health measures (primarily based on using non-pharmaceutical interventions, such as quarantine, isolation, social-distancing, face mask usage and community lockdowns), a number of exceptionally-promising vaccines are about to be approved for use in humans by the U.S. Food and Drugs Administration. We present a new mathematical model for assessing the population-level impact of the candidate vaccines, particularly for the case where the vaccination program is complemented with a social-distancing control measure at a certain compliance level. The model stratifies the total population into two subgroups, based on whether or not they habitually wear face mask in public. The resulting multigroup model, which takes the form of a compartmental, deterministic system of nonlinear differential equations, is parametrized using COVID-19 cumulative mortality data. Conditions for the asymptotic stability of the associated disease-free equilibrium, as well as expression for the vaccine-derived herd immunity threshold, are derived. This study shows that the prospect of COVID-19 elimination using any of the three candidate vaccines is quite promising, and that such elimination is more feasible if the vaccination program is combined with social-distancing control measures (implemented at moderate to high level of compliance).

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A systematic review and meta-analysis of Anakinra, Sarilumab, Siltuximab and Tocilizumab for Covid-19 -

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Towards reduction in bias in epidemic curves due to outcome misclassification through Bayesian analysis of time-series of laboratory test results: Case study of COVID-19 in Alberta, Canada and Philadelphia, USA -

The aim of our work was to better understand misclassification errors in identification of true cases of COVID-19 and to study the impact of these errors in epidemic curves. We examined publically available time-series data of laboratory tests for SARS-CoV-2 viral infection, the causal agent for COVID-19, to try to explore, using a Bayesian approach, about the sensitivity and specificity of the PCR-based diagnostic test. Data originated from Alberta, Canada (available on 3/28/2020) and city of Philadelphia, USA (available on 3/31/2020). Our analysis revealed that the data were compatible with near-perfect specificity but it was challenging to gain information about sensitivity (prior and posterior largely overlapped). We applied these insights to uncertainty/bias analysis of epidemic curves into jurisdictions under the assumptions of both improving and degrading sensitivity. If the sensitivity improved from 60 to 95%, the observed and adjusted epidemic curves likely fall within the 95% confidence intervals of the observed counts. However, bias in the shape and peak of the epidemic curves can be pronounced, if sensitivity either degrades or remains poor in the 60-70% range. In the extreme scenario, hundreds of undiagnosed cases, even among tested, are possible, potentially leading to further unchecked contagion should these cases not self-isolate. The best way to better understand bias in the epidemic curves of COVID-19 due to errors in testing is to empirically evaluate misclassification of diagnosis in clinical settings and apply this knowledge to adjustment of epidemic curves, a task for which the Bayesian method we presented is well-suited.

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On the importance of early testing even when imperfect in a pandemic such as COVID-19 -

Diagnosis of COVID-19 may be based on clinical observations, laboratory findings, and epidemiological linkage.1 Underscoring this diagnosis is accurate data from laboratory tests: this can make the difference between a false positive based on the clinical findings that are attributable to another cause, or a false negative based on lack of clinical findings that are attributable to SARS-CoV-2 viral infection, the causal agent for COVID-19. We conducted a simulation depicting how predictive values vary in the face of unknown data surrounding test sensitivity, specificity, and reported cases. in the USA. There are two main implications of our findings. First, the prevalence of COVID-19 in the tested population is likely inflated: clinical disease attributable to other sources of infection such as influenza, or other respiratory viruses, is a plausible explanation. Second, we should be less alarmed by not being able to trace chain of transmission for many who test positive early in this pandemic when population prevalence is low.

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Covid-19 Sentry

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