Tracking the COVID-19 outbreak in India through Twitter: Opportunities for social media based global pandemic surveillance.

We investigated the utility of Twitter for conducting multi-faceted geolocation-centric pandemic surveillance, using India as an example. We collected over 4 million COVID19-related tweets related to the Indian outbreak between January and July 2021. We geolocated the tweets, applied natural language processing to characterize the tweets (eg., identifying symptoms and emotions), and compared tweet volumes with the numbers of confirmed COVID-19 cases. Tweet numbers closely mirrored the outbreak, with the 7-day average strongly correlated with confirmed COVID-19 cases nationally (Spearman r=0.944; p=0.001), and also at the state level (Spearman r=0.84, p=0.0003). Fatigue, Dyspnea and Cough were the top symptoms detected, while there was a significant increase in the proportion of tweets expressing negative emotions (eg., fear and sadness). The surge in COVID-19 tweets was followed by increased number of posts expressing concern about black fungus and oxygen supply. Our study illustrates the potential of social media for multi-faceted pandemic surveillance.

Acupuncture in Addiction Medicine: Its History, Evidence, and Possibilities.

Substance-use disorders (SUDs) and drug addiction are not only national, but also global health concerns that have worsened during and after the COVID-19 pandemic. Acupuncture augments the endogenous opioid system and, therefore, has a theoretical basis as a treatment for opioid use disorders (OUDs). The basic science of acupuncture, its clinical research in addiction medicine, and decades of success of the National Acupuncture Detoxification Association protocol offer positive findings supporting this protocol's utility for treating SUDs. Considering the mounting opioid/substance-use concerns and deficiencies in SUD treatment availability in the United States, acupuncture can be a safe, feasible treatment option and adjunct in addiction medicine. Furthermore, large governmental agencies are lending support to acupuncture for treating acute and chronic pain, which, in turn, could translate to prevention of SUDs and addictions. This article is a narrative review of the background, the basic science and clinical research, and future direction of acupuncture in addiction medicine.

Characteristics of Intimate Partner Violence and Survivor's Needs During the COVID-19 Pandemic: Insights From Subreddits Related to Intimate Partner Violence.

Intimate partner violence (IPV) increased during the COVID-19 pandemic. Collecting actionable IPV-related data from conventional sources (e.g., medical records) was challenging during the pandemic, generating a need to obtain relevant data from non-conventional sources, such as social media. Social media, like Reddit, is a preferred medium of communication for IPV survivors to share their experiences and seek support with protected anonymity. Nevertheless, the scope of available IPV-related data on social media is rarely documented. Thus, we examined the availability of IPV-related information on Reddit and the characteristics of the reported IPV during the pandemic. Using natural language processing, we collected publicly available Reddit data from four IPV-related subreddits between January 1, 2020 and March 31, 2021. Of 4,000 collected posts, we randomly sampled 300 posts for analysis. Three individuals on the research team independently coded the data and resolved the coding discrepancies through discussions. We adopted quantitative content analysis and calculated the frequency of the identified codes. 36% of the posts (n = 108) constituted self-reported IPV by survivors, of which 40% regarded current/ongoing IPV, and 14% contained help-seeking messages. A majority of the survivors' posts reflected psychological aggression, followed by physical violence. Notably, 61.4% of the psychological aggression involved expressive aggression, followed by gaslighting (54.3%) and coercive control (44.3%). Survivors' top three needs during the pandemic were hearing similar experiences, legal advice, and validating their feelings/reactions/thoughts/actions. Albeit limited, data from bystanders (survivors' friends, family, or neighbors) were also available. Rich data reflecting IPV survivors' lived experiences were available on Reddit. Such information will be useful for IPV surveillance, prevention, and intervention.

The Early Detection of Fraudulent COVID-19 Products From Twitter Chatter: Data Set and Baseline Approach Using Anomaly Detection.

Background: Social media has served as a lucrative platform for spreading misinformation and for promoting fraudulent products for the treatment, testing, and prevention of COVID-19. This has resulted in the issuance of many warning letters by the US Food and Drug Administration (FDA). While social media continues to serve as the primary platform for the promotion of such fraudulent products, it also presents the opportunity to identify these products early by using effective social media mining methods. Objective: Our objectives were to (1) create a data set of fraudulent COVID-19 products that can be used for future research and (2) propose a method using data from Twitter for automatically detecting heavily promoted COVID-19 products early. Methods: We created a data set from FDA-issued warnings during the early months of the COVID-19 pandemic. We used natural language processing and time-series anomaly detection methods for automatically detecting fraudulent COVID-19 products early from Twitter. Our approach is based on the intuition that increases in the popularity of fraudulent products lead to corresponding anomalous increases in the volume of chatter regarding them. We compared the anomaly signal generation date for each product with the corresponding FDA letter issuance date. We also performed a brief manual analysis of chatter associated with 2 products to characterize their contents. Results: FDA warning issue dates ranged from March 6, 2020, to June 22, 2021, and 44 key phrases representing fraudulent products were included. From 577,872,350 posts made between February 19 and December 31, 2020, which are all publicly available, our unsupervised approach detected 34 out of 44 (77.3%) signals about fraudulent products earlier than the FDA letter issuance dates, and an additional 6 (13.6%) within a week following the corresponding FDA letters. Content analysis revealed misinformation, information, political, and conspiracy theories to be prominent topics. Conclusions: Our proposed method is simple, effective, easy to deploy, and does not require high-performance computing machinery unlike deep neural network-based methods. The method can be easily extended to other types of signal detection from social media data. The data set may be used for future research and the development of more advanced methods.

The effect of COVID-19 vaccines on sperm parameters: a systematic review and meta-analysis.

Published data were gathered for a meta-analysis to determine the difference in sperm parameters before and after administration of different types of coronavirus disease 2019 (COVID-19) vaccines, because the reproductive toxicity of COVID-19 vaccines has not yet been evaluated in clinical trials and COVID-19 has been associated with decreases in sperm quality. The preferred procedures for systematic reviews and meta-analyses were followed in the conduct and reporting of this study. The average sperm parameters of all sperm donors' multiple sperm donations were compared before and after receiving various COVID-19 vaccinations. Semen volume, total sperm motility, total sperm count, morphological change, and sperm concentration were the primary outcome measures. We compiled and analyzed the results of six studies on total sperm motility, six studies on semen volume, six studies on sperm concentration, two studies on morphological change, and two studies on total sperm count. Parameter comparisons with patients who had and had not been vaccinated were only reported in one of the included studies. When different types of COVID-19 vaccine injections were compared, no discernible differences in parameters were observed. According to the available data, the parameters of semen are unaffected by inactivated or messenger RNA (mRNA) COVID-19 vaccinations. To support these findings, additional prospectively designed research is required.

Automatic Detection of Twitter Users Who Express Chronic Stress Experiences via Supervised Machine Learning and Natural Language Processing.

Americans bear a high chronic stress burden, particularly during the COVID-19 pandemic. Although social media have many strengths to complement the weaknesses of conventional stress measures, including surveys, they have been rarely utilized to detect individuals self-reporting chronic stress. Thus, this study aimed to develop and evaluate an automatic system on Twitter to identify users who have self-reported chronic stress experiences. Using the Twitter public streaming application programming interface, we collected tweets containing certain stress-related keywords (eg, "chronic," "constant," "stress") and then filtered the data using pre-defined text patterns. We manually annotated tweets with (without) self-report of chronic stress as positive (negative). We trained multiple classifiers and tested them via accuracy and F1 score. We annotated 4195 tweets (1560 positives, 2635 negatives), achieving an inter-annotator agreement of 0.83 (Cohen's kappa). The classifier based on Bidirectional Encoder Representation from Transformers performed the best (accuracy of 83.6% [81.0-86.1]), outperforming the second best-performing classifier (support vector machines: 76.4% [73.5-79.3]). The past tweets from the authors of positive tweets contained useful information, including sources and health impacts of chronic stress. Our study demonstrates that users' self-reported chronic stress experiences can be automatically identified on Twitter, which has a high potential for surveillance and large-scale intervention.

The Role of Natural Language Processing during the COVID-19 Pandemic: Health Applications, Opportunities, and Challenges.

The COVID-19 pandemic is the most devastating public health crisis in at least a century and has affected the lives of billions of people worldwide in unprecedented ways. Compared to pandemics of this scale in the past, societies are now equipped with advanced technologies that can mitigate the impacts of pandemics if utilized appropriately. However, opportunities are currently not fully utilized, particularly at the intersection of data science and health. Health-related big data and technological advances have the potential to significantly aid the fight against such pandemics, including the current pandemic's ongoing and long-term impacts. Specifically, the field of natural language processing (NLP) has enormous potential at a time when vast amounts of text-based data are continuously generated from a multitude of sources, such as health/hospital systems, published medical literature, and social media. Effectively mitigating the impacts of the pandemic requires tackling challenges associated with the application and deployment of NLP systems. In this paper, we review the applications of NLP to address diverse aspects of the COVID-19 pandemic. We outline key NLP-related advances on a chosen set of topics reported in the literature and discuss the opportunities and challenges associated with applying NLP during the current pandemic and future ones. These opportunities and challenges can guide future research aimed at improving the current health and social response systems and pandemic preparedness.

Exploring low-income migrant workers' health information-seeking behavior during COVID-19 in Taiwan: A qualitative study.

Background: COVID-19 pandemic is reported to exacerbate existing vulnerabilities of marginalized groups, and the lack of self-care can lead to the spread of the virus across society. Therefore, effective responses to the challenges imposed by the health crisis should consider the health information needs of migrant workers. Objective: We aimed to explore how migrant low-income workers seek health information and how their health-related information needs were met during a health crisis. We also investigated migrant workers' preferred information sources and types of content with the theoretical concept of health literacy to understand the development of health competencies among migrant workers. Methods: We conducted semi-structured interviews with Thai low-income migrant workers. A total of 13 Thai migrant workers participated in the study, among whom five were undocumented. The interviews were audio-taped, transcribed, and analyzed with the thematic analysis approach. Results: Our findings indicated that migrant workers' health literacy and health information behavior could be improved through technology when facing a health crisis. We found that participants sought health information proactively to reduce their perceived risks. However, there is still space for design to support the ability to process jargon information and apply local policy, such as providing easy-to-understand, accurate, and timely information. The findings of this study provide some insights for the authority and technological design to respond to migrant workers' health information needs. Conclusions: This study acknowledges and understands the needs of vulnerable migrant workers in society. The findings of this study provide insights for the authority and technological design to respond to migrant workers' health information needs. We also outline the areas worth further investigation, such as the communication between information seekers and providers, and the navigation of the healthcare system for migrants in the host country.

Tracking the COVID-19 outbreak in India through Twitter: Opportunities for social media based global pandemic surveillance

We investigated the utility of Twitter for conducting multi-faceted geolocation-centric pandemic surveillance, using India as an example. We collected over 4 million COVID19-related tweets related to the Indian outbreak between January and July 2021. We geolocated the tweets, applied natural language processing to characterize the tweets (eg., identifying symptoms and emotions), and compared tweet volumes with the numbers of confirmed COVID-19 cases. Tweet numbers closely mirrored the outbreak, with the 7-day average strongly correlated with confirmed COVID-19 cases nationally (Spearman r=0.944;p=0.001), and also at the state level (Spearman r=0.84, p=0.0003). Fatigue, Dyspnea and Cough were the top symptoms detected, while there was a significant increase in the proportion of tweets expressing negative emotions (eg., fear and sadness). The surge in COVID-19 tweets was followed by increased number of posts expressing concern about black fungus and oxygen supply. Our study illustrates the potential of social media for multi-faceted pandemic surveillance.

Early detection of fraudulent COVID-19 products from Twitter chatter (preprint)

BackgroundSocial media have served as lucrative platforms for spreading misinformation and for promoting fraudulent products for the treatment, testing, and prevention of COVID-19. This has resulted in the issuance of many warning letters by the United States Food and Drug Administration (FDA). While social media continue to serve as the primary platform for the promotion of such fraudulent products, they also present the opportunity to identify these products early by employing effective social media mining methods. In this study, we employ natural language processing and time series anomaly detection methods for automatically detecting fraudulent COVID-19 products early from Twitter. Our approach is based on the intuition that increases in the popularity of fraudulent products lead to corresponding anomalous increases in the volume of chatter regarding them. ResultsWe utilized an anomaly detection method on streaming COVID-19-related Twitter data to detect potentially anomalous increases in mentions of fraudulent products. We compared the anomaly signal generation date for each product with the corresponding FDA letter issuance date. Issue dates ranged from March 6, 2020 to June 22, 2021, and 44 key phrases representing fraudulent products were included. From 577,872,350 posts made between February 19, 2020 to December 31, 2020, our unsupervised approach detected 34/44 (77.3%) signals about fraudulent products earlier than the FDA letter issuance dates, and an additional 6/44 (13.6%) within a week following the corresponding FDA letters.  ConclusionsOur proposed method is simple, effective, and easy to deploy, and does not require high-performance computing machinery, unlike deep neural network-based methods. The method can be easily extended to other types of signal detection from social media data.

Early detection of fraudulent COVID-19 products from Twitter chatter (preprint)

Social media have served as lucrative platforms for misinformation and for promoting fraudulent products for the treatment, testing and prevention of COVID-19. This has resulted in the issuance of many warning letters by the United States Food and Drug Administration (FDA). While social media continue to serve as the primary platform for the promotion of such fraudulent products, they also present the opportunity to identify these products early by employing effective social media mining methods. In this study, we employ natural language processing and time series anomaly detection methods for automatically detecting fraudulent COVID-19 products early from Twitter. Our approach is based on the intuition that increases in the popularity of fraudulent products lead to corresponding anomalous increases in the volume of chatter regarding them. We utilized an anomaly detection method on streaming COVID-19-related Twitter data to detect potentially anomalous increases in mentions of fraudulent products. Our unsupervised approach detected 34/44 (77.3%) signals about fraudulent products earlier than the FDA letter issuance dates, and an additional 6/44 (13.6%) within a week following the corresponding FDA letters. Our proposed method is simple, effective and easy to deploy, and do not require high performance computing machinery unlike deep neural network-based methods.

A Light-Weight Text Summarization System for Fast Access to Medical Evidence.

As the volume of published medical research continues to grow rapidly, staying up-to-date with the best-available research evidence regarding specific topics is becoming an increasingly challenging problem for medical experts and researchers. The current COVID19 pandemic is a good example of a topic on which research evidence is rapidly evolving. Automatic query-focused text summarization approaches may help researchers to swiftly review research evidence by presenting salient and query-relevant information from newly-published articles in a condensed manner. Typical medical text summarization approaches require domain knowledge, and the performances of such systems rely on resource-heavy medical domain-specific knowledge sources and pre-processing methods (e.g., text classification) for deriving semantic information. Consequently, these systems are often difficult to speedily customize, extend, or deploy in low-resource settings, and they are often operationally slow. In this paper, we propose a fast and simple extractive summarization approach that can be easily deployed and run, and may thus aid medical experts and researchers obtain fast access to the latest research evidence. At runtime, our system utilizes similarity measurements derived from pre-trained medical domain-specific word embeddings in addition to simple features, rather than computationally-expensive pre-processing and resource-heavy knowledge bases. Automatic evaluation using ROUGE-a summary evaluation tool-on a public dataset for evidence-based medicine shows that our system's performance, despite the simple implementation, is statistically comparable with the state-of-the-art. Extrinsic manual evaluation based on recently-released COVID19 articles demonstrates that the summarizer performance is close to human agreement, which is generally low, for extractive summarization.

Tumor Necrosis Factor-Alpha Exacerbates Viral Entry in SARS-CoV2-Infected iPSC-Derived Cardiomyocytes.

The coronavirus disease 2019 (COVID-19) pandemic with high infectivity and mortality has caused severe social and economic impacts worldwide. Growing reports of COVID-19 patients with multi-organ damage indicated that severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) may also disturb the cardiovascular system. Herein, we used human induced pluripotent stem cell (iPSC)-derived cardiomyocytes (iCMs) as the in vitro platform to examine the consequence of SARS-CoV2 infection on iCMs. Differentiated iCMs expressed the primary SARS-CoV2 receptor angiotensin-converting enzyme-II (ACE2) and the transmembrane protease serine type 2 (TMPRSS2) receptor suggesting the susceptibility of iCMs to SARS-CoV2. Following the infection of iCMs with SARS-CoV2, the viral nucleocapsid (N) protein was detected in the host cells, demonstrating the successful infection. Bioinformatics analysis revealed that the SARS-CoV2 infection upregulates several inflammation-related genes, including the proinflammatory cytokine tumor necrosis factor-α (TNF-α). The pretreatment of iCMs with TNF-α for 24 h, significantly increased the expression of ACE2 and TMPRSS2, SASR-CoV2 entry receptors. The TNF-α pretreatment enhanced the entry of GFP-expressing SARS-CoV2 pseudovirus into iCMs, and the neutralization of TNF-α ameliorated the TNF-α-enhanced viral entry. Collectively, SARS-CoV2 elevated TNF-α expression, which in turn enhanced the SARS-CoV2 viral entry. Our findings suggest that, TNF-α may participate in the cytokine storm and aggravate the myocardial damage in COVID-19 patients.

The Effect of Post-Graduate Year Training on the Self-Efficacy and Emotional Traits of Physicians Facing the COVID-19 Pandemic.

Background: Taiwan implemented the post-graduate year (PGY) training to reform the medical education system to provide holistic medical care after severe acute respiratory syndrome in 2003. In late 2019, COVID-19 quickly spread across the globe and became a pandemic crisis. This study aimed to investigate whether the establishment of the PGY training had positive effects on the self-efficacy and emotional traits of medical workers. Methods: One hundred and ten physicians, including PGY, residents, and visiting staff, were investigated using the General Self-Efficacy Scale (GSES) and Emotional Trait and State Scale (ETSS), and their feedback and suggestions were collected. An exploratory factor analysis was done to reduce the factor dimensions using the varimax rotation method, which was reduced to four factors: "the ability to cope with ease", "proactive ability", "negative emotion", and "positive emotion". A comparison with and without PGY training when facing the COVID-19 pandemic was conducted. Results: Those who had received PGY training (n = 77) were younger, had a lower grade of seniority, and had less practical experience than those who had not received PGY (n = 33). Those who had received PGY training had significantly higher scores for the factors "ability to cope with ease", "proactive ability", and "positive emotion" than those who had not received PGY training. Conclusion: The study revealed that PGY training may have had positive effects on the personal self-efficacy and emotional traits of physicians coping with the COVID-19 pandemic.

Participation in Virtual Urology Conferences During the COVID-19 Pandemic: Cross-sectional Survey Study.

BACKGROUND: Due to the influence of the COVID-19 pandemic, conventional face-to-face academic conferences have been restricted, and many of these conferences have moved onto the internet. OBJECTIVE: The aim of this study was to investigate the virtual conferences in the field of urology during the COVID-19 pandemic and provide suggestions for better organization of such conferences. METHODS: A cross-sectional survey was conducted from May 30 to June 15, 2020, in China. Our team designed a 23-item questionnaire to investigate the conferences attended by urologists during the COVID-19 pandemic. SPSS 22.0 (IBM Corporation) was applied to analyze the data collected. RESULTS: A total of 330 Chinese urologists participated in our survey, and the response rate was 89.7% (330/368). Among the participants, 40.9% (135/330) were associate chief physicians. The proportion of participants who took part in conventional face-to-face academic conferences decreased from 92.7% (306/330) before the COVID-19 pandemic to 22.1% (73/330) during the pandemic (P<.001). In contrast, the proportion of urologists who took part in virtual conferences increased from 69.4% (229/330) to 90% (297/330) (P<.001). Most urologists (70.7%, 210/297) chose to participate in the virtual conferences at home and thought that a meeting length of 1-2 hours was most appropriate. Among the urologists, 73.7% (219/297) reported that their participation in the virtual conferences went smoothly, while the remaining respondents reported that they had experienced lags in video and audio streaming during the virtual conferences. When comparing conventional face-to-face conferences with virtual conferences, 70.7% (210/297) of the respondents thought that both conference formats were acceptable, while 17.9% (53/297) preferred virtual conferences and 11.5% (34/297) preferred conventional face-to-face meetings. CONCLUSIONS: Virtual conferences are increasing in popularity during the COVID-19 pandemic; however, many aspects of these conferences could be improved for better organization.

Building an OMOP common data model-compliant annotated corpus for COVID-19 clinical trials.

Clinical trials are essential for generating reliable medical evidence, but often suffer from expensive and delayed patient recruitment because the unstructured eligibility criteria description prevents automatic query generation for eligibility screening. In response to the COVID-19 pandemic, many trials have been created but their information is not computable. We included 700 COVID-19 trials available at the point of study and developed a semi-automatic approach to generate an annotated corpus for COVID-19 clinical trial eligibility criteria called COVIC. A hierarchical annotation schema based on the OMOP Common Data Model was developed to accommodate four levels of annotation granularity: i.e., study cohort, eligibility criteria, named entity and standard concept. In COVIC, 39 trials with more than one study cohorts were identified and labelled with an identifier for each cohort. 1,943 criteria for non-clinical characteristics such as "informed consent", "exclusivity of participation" were annotated. 9767 criteria were represented by 18,161 entities in 8 domains, 7,743 attributes of 7 attribute types and 16,443 relationships of 11 relationship types. 17,171 entities were mapped to standard medical concepts and 1,009 attributes were normalized into computable representations. COVIC can serve as a corpus indexed by semantic tags for COVID-19 trial search and analytics, and a benchmark for machine learning based criteria extraction.

The COVID-19 Trial Finder.

Clinical trials are the gold standard for generating reliable medical evidence. The biggest bottleneck in clinical trials is recruitment. To facilitate recruitment, tools for patient search of relevant clinical trials have been developed, but users often suffer from information overload. With nearly 700 coronavirus disease 2019 (COVID-19) trials conducted in the United States as of August 2020, it is imperative to enable rapid recruitment to these studies. The COVID-19 Trial Finder was designed to facilitate patient-centered search of COVID-19 trials, first by location and radius distance from trial sites, and then by brief, dynamically generated medical questions to allow users to prescreen their eligibility for nearby COVID-19 trials with minimum human computer interaction. A simulation study using 20 publicly available patient case reports demonstrates its precision and effectiveness.

Self-reported COVID-19 symptoms on Twitter: an analysis and a research resource.

OBJECTIVE: To mine Twitter and quantitatively analyze COVID-19 symptoms self-reported by users, compare symptom distributions across studies, and create a symptom lexicon for future research. MATERIALS AND METHODS: We retrieved tweets using COVID-19-related keywords, and performed semiautomatic filtering to curate self-reports of positive-tested users. We extracted COVID-19-related symptoms mentioned by the users, mapped them to standard concept IDs in the Unified Medical Language System, and compared the distributions to those reported in early studies from clinical settings. RESULTS: We identified 203 positive-tested users who reported 1002 symptoms using 668 unique expressions. The most frequently-reported symptoms were fever/pyrexia (66.1%), cough (57.9%), body ache/pain (42.7%), fatigue (42.1%), headache (37.4%), and dyspnea (36.3%) amongst users who reported at least 1 symptom. Mild symptoms, such as anosmia (28.7%) and ageusia (28.1%), were frequently reported on Twitter, but not in clinical studies. CONCLUSION: The spectrum of COVID-19 symptoms identified from Twitter may complement those identified in clinical settings.

Self-reported COVID-19 symptoms on Twitter: An analysis and a research resource (preprint)

Objective To mine Twitter to quantitatively analyze COVID-19 symptoms self-reported by users, compare symptom distributions against clinical studies, and create a symptom lexicon for the research community. Materials and methods We retrieved tweets using COVID-19-related keywords, and performed semi-automatic filtering to curate self-reports of positive-tested users. We extracted COVID-19-related symptoms mentioned by the users, mapped them to standard concept IDs (UMLS), and compared the distributions to those reported in early studies from clinical settings. Results We identified 203 positive-tested users who reported 1002 symptoms using 668 unique expressions. The most frequently-reported symptoms were fever/pyrexia (66.1%), cough (57.9%), body ache/pain (42.7%), fatigue (42.1%), headache (37.4%), and dyspnea (36.3%) amongst users who reported at least 1 symptom. Mild symptoms, such as anosmia (28.7%) and ageusia (28.1%) were frequently reported on Twitter, but not in clinical studies. Conclusion The spectrum of COVID-19 symptoms identified from Twitter may complement those identified in clinical settings.