Social Media is not a Health Proxy: Differences Between Social Media and Electronic Health Record Reports of Post-COVID Symptoms

The COVID-19 pandemic transformed many aspects of health and daily life. A subset of people who were infected with the virus have ongoing chronic health issues that range in type of symptom and severity. In this study, we conducted a qualitative assessment of self-reported post-COVID symptoms from patients' electronic health records (EHR, n=564) and a randomized collection of Reddit and Twitter posts (n=500 for each). We show the inconsistencies in what types of symptoms are shared between platforms in addition to assessing the severity of the symptoms and how social media characterizations of post-COVID do not tell a complete story of this phenomenon. This research contributes to CSCW health literature by connecting digital traces of post-COVID with EHR data, critiquing the use of social media as a health proxy and points to its potential to add context to the analysis of traditional health data extracted from the EHR. © 2023 ACM.

Applying Human-Centered Data Science to Healthcare: Hyperlocal Modeling of COVID-19 Hospitalizations

Algorithms as a component of decision-making in healthcare are becoming increasingly prevalent and AI in healthcare has become a topic of mass consideration. However, pursuing these methods without a human-centered framework can lead to bias, thus incorporating discrimination on behalf of the algorithm upon implementation. By examining each step of the design process from a human-centered perspective and incorporating stakeholder motivations, algorithmic implementation can become vastly useful, and more accurately tailored to stakeholder needs. We examine previous work in healthcare executed with a human-centered design, to analyze the multiple frameworks which effectively create human-centered application, as extended to healthcare. © 2023 Owner/Author.

Computational Deep Learning Models for Detection of COVID-19 Using Chest X-Ray Images

Traditional deep learning architectures after the AlexNet have added more layers to achieve higher accuracy. However, with increasing number of layers, we are likely to encounter vanishing/exploding gradient problems in these architectures which significantly impact the training performance. This was solved by the introduction of residual networks which make use of "skip connections” by adding the output from the previous layer to the layer ahead. ResNets are often combined with the Inception v4 model and was first used by Google researchers as Inception-ResNet. Inception v4 aimed to reduce the complexity of Inception v3 model which gave the state-of-the-art accuracy on ILSVRC 2015 challenge. The initial set of layers before the Inception block in Inception v4, referred to as "stem of the architecture,” was modified to make it more uniform. This model can be trained without partition of replicas unlike the previous versions of inceptions which required different replica in order to fit in memory. This architecture uses memory optimization on back propagation to reduce the memory requirement. In this paper, we propose two approaches for detection of COVID-19 using chest X-ray images by implementing ResNet16 and Inception v4 and providing a comparison of their performances. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Key Factors of Adoption of Alternative Business Model: A Study on Handicraft Artisans During COVID-19 Pandemic

This chapter is an attempt to analyse the key factors of adoption of alternative business models by the handicraft artisans during COVID-19 pandemic. This paper is a part of doctoral study and is based on the part of primary research conducted by the scholar. The study primarily analyses the strength of the relationship of the factors. A structured questionnaire was employed to collect data for conducting the study. Descriptive statistics, correlation and multiple linear regressions have been used as statistical tools for analysis. The findings of the study demonstrate the linkage and effect of critical aspects on the performance of alternative business models, resulting in conclusions that leave room for future research. The novelty of this study is that it has made an initial attempt to identify the key factors of adoption of the alternative business model for the artisans. The study is limited to a specific field of the craft sector. The policy-makers will have substantial theoretical consequences for the development of the artisans regarding their alternative business model. It is important to improve artisan’s entrepreneurial skills and capabilities to strengthen them in the global market today. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Practitioner Attitudes towards an Early Warning System : From Professional Distraction to Relational Support

As the general population ages and life expectancy increases in the United States, demand for virtual health care is on the rise. Undoubtedly, the next several decades will see increases in automated patient care and use of data-driven warning systems, trends which have already accelerated in the wake of Covid-19. Thus, understanding how traditionally trained healthcare practitioners respond to predictive analytics, like early warning systems, is vital for their successful implementation in the future. © 2021 IEEE.

Exploiting Diverse Contextual Features through Transformers for Detecting Informative Tweets

Twitter is deemed the most reliable and convenient microblogging platform for getting real-time news and information. During the COVID-19 pandemic, people are keen to share various information ranging from new cases, healthcare guidelines, medication, and vaccine news on Twitter. However, a major portion of the shared tweets is uninformative and misleading which may create mass panic. Hence, it is an important task to distinguish and label a COVID-19 tweet as informative or uninformative. Prior works mostly focused on various pretrained transformer models and different types of contextual feature extractors to address this task. However, most of the works applied these models one at a time and didn't employ any effective neural layer at the bottom to distill the tweet contexts effectively. Since a tweet may contain a multifarious context, therefore, representing a tweet using only one kind of feature extractor may not work well. To overcome this limitation, we present an approach that leverages an ensemble of various cutting-edge transformer models to capture the diverse contextual dimension of the tweets. We exploit the BERT, CTBERT, BERTweet, RoBERTa, and XLM-RoBERTa models in our proposed method. Next, we perform a pooling operation on those extracted embedding features to transform them into document embedding vectors. Then, we utilize a feed-forward neural architecture with a linear activation function for the classification task. To generate final prediction, we utilize the majority voting-driven ensemble technique. Experiments on WNUT-2020 COVID-19 English Tweet dataset manifested the efficacy of our method over other state-of-the-art methods. © 2022 IEEE.

Exploiting Ensemble of Transformer Models for Detecting Informative Tweets

Microblogging platforms especially Twitter is considered as one of the prominent medium of getting user-generated information. Millions of tweets were posted daily during COVID-19 pandemic days and the rate increases gradually. Tweets include a wide range of information including healthcare information, recent cases, and vaccination updates. This information helps individuals stay informed about the situation and assists safety personnel in making decisions. Apart from these, large amounts of propaganda and misinformation have spread on Twitter during this period. The impact of this infodemic is multifarious. Therefore, it is considered a formidable task to determine whether a tweet related to COVID-19 is informative or uninformative. However, the noisy and nonformal nature of tweets makes it difficult to determine the tweets' informativeness. In this paper, we propose an approach that exploits the benefits of finetuned transformer models for informative tweet identification. Upon extracting features from pre-trained COVID-Twitter-BERT and RoBERTa models, we leverage the stacked embedding technique to combine them. The features are then fed to a BiLSTM module to learn the contextual dimension effectively. Finally, a simple feed-forward linear architecture is employed to obtain the predicted label. Experimental result on WNUT-2020 benchmark informative tweet detection dataset demonstrates the potency of our method over various state-of-the-art approaches. © 2021 IEEE.

Clinical and autoimmune characteristics of COVID-19 patients in a tertiary care centre: a cross-sectional study

Introduction: Since the end of 2019, the world is witnessing the emergence of the coronavirus disease 2019 (COVID-19) outbreak and pandemic caused by a novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This disease presented with a wide array of clinical, inflammatory and possible autoimmune manifestations. Currently, a very few data is available about the involvement of autoimmunity in patients affected by coronavirus disease 2019 (COVID-19). Aim: To find out the clinical and inflammatory status of COVID-19 patients and whether this disease (SARS-CoV-2) stimulates autoantibody production and contributes to autoimmunity activation. Methodology: A hospital based retrospective study conducted on 60 COVID-19 patients. All patients were clinically and radiologically evaluated and screened for common inflammatory markers and auto antibodies. Result: Patients included were 39 men (65%) and 21 women (35%). 33 patients were mild cases, 15 were moderate and 12 were severe cases with a mean age of 44.27. Fever and shortness of breath were the dominant symptoms;most patients had at least one coexisting disorder on admission;the most common characteristic on chest CT was groundglass opacity;the most common findings on laboratory measurements of inflammatory markers were elevated levels of CRP, LDH, ferritin and altered neutrophil lymphocyte ratio;and prevalence of autoantibodies, anti SSA/Ro antibody, anti SSB/La antibody, and antinuclear antibody was 20%, 10%, and 15%, respectively and Anti-TPO antibody was positive in 33.3% patients.

RNA-extraction-free diagnostic method to detect SARS-CoV-2: An assessment from two States, India

With increasing demand for large numbers of testing during COVID-19 pandemic, came alternative protocols with shortened turn-around time. We evaluated the performance of such a protocol wherein 1138 consecutive clinic attendees were enrolled;584 and 554 respectively from two independent study sites in the cities of Pune and Kolkata. Paired nasopharyngeal and oropharyngeal swabs were tested by using both reference and index methods in blinded fashion. Prior to conducting RT-PCR, swabs collected in viral transport medium (VTM) were processed for RNA extraction (reference method) and swabs collected in dry tube without VTM were incubated in Tris-EDTA-Proteinase K buffer for 30 minutes and heat inactivated at 98oC for 6 minutes (index method). Overall sensitivity and specificity of the index method were 78.9% (95% CI 71% to 86%) and 99 % (95% CI 98% to 99.6%) respectively. Agreement between the index and reference method was 96.8 % (k = 0.83, SE=0.030). The reference method exhibited enhanced detection of viral genes (E, N and RdRP) with lower Ct values compared to the index method. The index method can be used for detecting SARS-CoV-2 infection with appropriately chosen primer-probe set and heat treatment approach in pressing time;low sensitivity constrains its potential wider use. © 2021 Cambridge University Press. All rights reserved.

Place attachment and decisions to move: A study of indian migrants during Covid-19 pandemic and directions to future research

The relation between attachment to places and human mobility are not straightforward or linear, but is frequently indirect and mediated by social, cultural and economic drivers. Migration affects people positively, but most migratory movement are due to economic issue, financial problem, unemployment, vulnerability, stress or shocks. This migratory movement has a place attachment angle that is now becoming increasingly noticed for several times. Place attachments are based on interpersonal interaction and can alter a person's perception of risk and coping techniques in areas prone to natural disasters. The COVID-19 pandemic had a significant impact on migrants. The interplay of COVID-19 and the decline in economic activity have led to both domestic and international instability. Due to environmental change, migration explains complex interactions, hazards and unpredictability. This study examines the problems and opportunities in terms of place for the immobile population that undergo environmental degradation and clarifies its significance in the pandemic situation in Indian context. © 2021 by SPC (Sami Publishing Company).

COVID-19 lockdown effect on land surface temperature and normalized difference vegetation index

Coronavirus disease (COVID-19) has changed the human lifestyle just like a disaster in 2020. Many people died throughout the world due to its severe attack. Lockdown is the most common term used in today's life to prevent the adverse effect of COVID-19. However, during the lockdown period, a significant improvement in the urban environment was noticed in almost every part of the world. During the lockdown period, the decrease in the number of running vehicles and moving people on the road lowers the pollution level and it has a direct positive impact on the urban environment. The study examines the changes found in land surface temperature (LST) and normalized difference vegetation index (NDVI) during the lockdown period in Raipur city, India with the earlier periods (2013–19) to compare the environmental status. The results indicate that the LST is reduced and NDVI is increased significantly during the lockdown period, and the negativity of the LST-NDVI correlation is increased remarkably. The study also shows a better ecological status of the city during the lockdown period. The study is useful for environmental strategists and urban planners. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Cardiological Society of India: Document on acute MI care during COVID-19.

The unprecedented and rapidly spreading Coronavirus Disease-19 (COVID-19) pandemic has challenged public health care systems globally. Based on worldwide experience, India has initiated a nationwide lockdown to prevent the exponential surge of cases. During COVID-19, management of cardiovascular emergencies like acute Myocardial Infarction (MI) may be compromised. Cardiological Society of India (CSI) has ventured in this moment of crisis to evolve a consensus document for care of acute MI. However, this care should be individualized, based on local expertise and governmental advisories.