Novel approaches to estimate compliance with lockdown measures in the COVID-19 pandemic

A lockdown is a social distancing intervention that aims to minimise physical contact between individuals and groups in order to reduce transmission of a communicable disease [1]. Social distancing measures are typically introduced in an attempt to reduce and/or delay the peak of an epidemic/pandemic, to minimise the potential for surges in healthcare utilisation and to protect vulnerable groups. In the context of COVID-19, the World Health Organization has encouraged use of the term ‘physical distancing’ instead of social distancing to highlight that the aim of this intervention is only to reduce physical contact, not social contact which is often still possible through telephone and video calls, and social media [2]. There are a range of physical distancing measures, which can be broadly categorised as operating at the individual (eg, to support self-isolation of confirmed or suspected cases) or population levels (eg, closing of schools or workplaces)

Artificial intelligence-enabled social media analysis for pharmacovigilance of COVID-19 vaccinations in the United Kingdom: Observational Study

Background:The roll-out of vaccines for SARS-CoV-2 in the United Kingdom, started in December 2020. Uptake has been high, and there has been a subsequent reduction in infections, hospitalisations and deaths in vaccinated individuals. However, vaccine hesitancy remains a concern, in particular relating to adverse effects following immunisation (AEFI). Social media analysis has the potential to inform policymakers on AEFIs being discussed by the public, and on public attitudes towards the national immunisation campaign.Objective:We sought to describe the frequency and nature of COVID-19 AEFI related mentions on social media, and provide insights on public sentiment towards COVID-19 vaccines, in the UK.Methods:We extracted and analysed over 121,406 relevant Twitter and Facebook posts, from 8 December 2020 to 30 April 2021. These were thematically filtered using a two-step approach, initially using COVID-related keywords and then using vaccines and manufacturer related keywords. We identified AEFI related keywords and modelled their word frequency to monitor their trends over two-week periods. We also adapted and utilised our recently developed hybrid ensemble model, which combines a state-of-the-art lexicon rule based and deep learning based approaches, to analyse sentiment trends relating to the main vaccines available in the UK.Results:We identified an increasing trend in the number of mentions for each AEFI on social media over the period of study. The most frequent AEFI mentions were found to be: appetite (14%, n=79,132), allergy (9%, n=53,924), injection site (10%, n=56,152), and clots (8%, n=43,907) related symptoms. We also found more rarely reported AEFIs, such as Bell’s Palsy (2%, n=11,909) and Guillain Barre Syndrome (GBS) (2%, n=9,576), being discussed as frequently as more well-known side effects, such as headache (2%, n=10,641), fever (2%, n=12,707) and diarrhea (3%, n=16,559). Overall, we found public sentiment towards vaccines and their manufacturers to be largely positive (58%), with negative (22%) and neutral (19%) sentiment equally split. The sentiment trend was relatively steady over time and had minor variations, likely based on political and regulatory announcements and debates.Conclusions:The most frequently discussed COVID-19 AEFIs on social media were found to be broadly consistent with those reported in the literature and by government pharmacovigilance. We also detected potential safety signals from our analysis, that have been detected elsewhere and are currently being investigated. As such, we believe our findings support the use of social media analysis to provide a complementary data source to conventional knowledge sources being used for pharmacovigilance purposes

Validity of clinical severity scores for respiratory syncytial virus: a systematic review

Background: Respiratory syncytial virus (RSV) is a widespread respiratory pathogen, and RSV-related acute lower respiratory tract infections are the most common cause of respiratory hospitalization in children <2 years of age. Over the last 2 decades, a number of severity scores have been proposed to quantify disease severity for RSV in children, yet there remains no overall consensus on the most clinically useful score. Methods: We conducted a systematic review of English-language publications in peer-reviewed journals published since January 2000 assessing the validity of severity scores for children (≤24 months of age) with RSV and/or bronchiolitis, and identified the most promising scores. For included articles, (1) validity data were extracted, (2) quality of reporting was assessed using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis checklist (TRIPOD), and (3) quality was assessed using the Prediction Model Risk Of Bias Assessment Tool (PROBAST). To guide the assessment of the validity data, standardized cutoffs were employed, and an explicit definition of what we required to determine a score was sufficiently validated. Results: Our searches identified 8541 results, of which 1779 were excluded as duplicates. After title and abstract screening, 6670 references were excluded. Following full-text screening and snowballing, 32 articles, including 31 scores, were included. The most frequently assessed scores were the modified Tal score and the Wang Bronchiolitis Severity Score; none of the scores were found to be sufficiently validated according to our definition. The reporting and/or design of all the included studies was poor. The best validated score was the Bronchiolitis Score of Sant Joan de Déu, and a number of other promising scores were identified. Conclusions: No scores were found to be sufficiently validated. Further work is warranted to validate the existing scores, ideally in much larger datasets

Effectiveness of virtual and augmented reality for improving knowledge and skills in medical students: protocol for a systematic review

Introduction Virtual reality (VR) and augmented reality (AR) technologies are increasingly being used in undergraduate medical education. We aim to evaluate the effectiveness of VR and AR technologies for improving knowledge and skills in medical students.Methods and analysis Using Best Evidence in Medical Education (BEME) collaboration guidelines, we will search MEDLINE (via PubMed), Education Resources Information Center, PsycINFO, Web of Knowledge, Embase and the Cochrane Central Register of Controlled Trials for English-language records, from January 1990 to March 2021. Randomised trials that studied the use of VR or AR devices for teaching medical students will be included. Studies that assessed other healthcare professionals, or did not have a comparator group, will be excluded. The primary outcome measures relate to medical students’ knowledge and clinical skills. Two reviewers will independently screen studies and assess eligibility based on our prespecified eligibility criteria, and then extract data from each eligible study using a modified BEME coding form. Any disagreements will be resolved by discussion or, if necessary, the involvement of a third reviewer. The BEME Quality Indicators checklist and the Cochrane Risk of Bias Tool will be used to assess the quality of the body of evidence. Where data are of sufficient homogeneity, a meta-analysis using a random-effects model will be conducted. Otherwise, a narrative synthesis approach will be taken and studies will be evaluated based on Kirkpatrick’s levels of educational outcomes and the Synthesis Without Meta-analysis guidelines.Ethics and dissemination Ethical approval is not required for this systematic review as no primary data are being collected. We will disseminate the findings of this review through scientific conferences and through publication in a peer-reviewed journal

Real-Time Artifacts Reduction during TMS-EEG Co-Registration: A Comprehensive Review on Technologies and Procedures

Transcranial magnetic stimulation (TMS) excites neurons in the cortex, and neural activity can be simultaneously recorded using electroencephalography (EEG). However, TMS-evoked EEG potentials (TEPs) do not only reflect transcranial neural stimulation as they can be contaminated by artifacts. Over the last two decades, significant developments in EEG amplifiers, TMS-compatible technology, customized hardware and open source software have enabled researchers to develop approaches which can substantially reduce TMS-induced artifacts. In TMS-EEG experiments, various physiological and external occurrences have been identified and attempts have been made to minimize or remove them using online techniques. Despite these advances, technological issues and methodological constraints prevent straightforward recordings of early TEPs components. To the best of our knowledge, there is no review on both TMS-EEG artifacts and EEG technologies in the literature to-date. Our survey aims to provide an overview of research studies in this field over the last 40 years. We review TMS-EEG artifacts, their sources and their waveforms and present the state-of-the-art in EEG technologies and front-end characteristics. We also propose a synchronization toolbox for TMS-EEG laboratories. We then review subject preparation frameworks and online artifacts reduction maneuvers for improving data acquisition and conclude by outlining open challenges and future research directions in the field

Artificial Intelligence–Enabled Analysis of Public Attitudes on Facebook and Twitter Toward COVID-19 Vaccines in the United Kingdom and the United States: Observational Study

Background: Global efforts toward the development and deployment of a vaccine for COVID-19 are rapidly advancing. To achieve herd immunity, widespread administration of vaccines is required, which necessitates significant cooperation from the general public. As such, it is crucial that governments and public health agencies understand public sentiments toward vaccines, which can help guide educational campaigns and other targeted policy interventions.Objective: The aim of this study was to develop and apply an artificial intelligence–based approach to analyze public sentiments on social media in the United Kingdom and the United States toward COVID-19 vaccines to better understand the public attitude and concerns regarding COVID-19 vaccines.Methods: Over 300,000 social media posts related to COVID-19 vaccines were extracted, including 23,571 Facebook posts from the United Kingdom and 144,864 from the United States, along with 40,268 tweets from the United Kingdom and 98,385 from the United States from March 1 to November 22, 2020. We used natural language processing and deep learning–based techniques to predict average sentiments, sentiment trends, and topics of discussion. These factors were analyzed longitudinally and geospatially, and manual reading of randomly selected posts on points of interest helped identify underlying themes and validated insights from the analysis.Results: Overall averaged positive, negative, and neutral sentiments were at 58%, 22%, and 17% in the United Kingdom, compared to 56%, 24%, and 18% in the United States, respectively. Public optimism over vaccine development, effectiveness, and trials as well as concerns over their safety, economic viability, and corporation control were identified. We compared our findings to those of nationwide surveys in both countries and found them to correlate broadly.Conclusions: Artificial intelligence–enabled social media analysis should be considered for adoption by institutions and governments alongside surveys and other conventional methods of assessing public attitude. Such analyses could enable real-time assessment, at scale, of public confidence and trust in COVID-19 vaccines, help address the concerns of vaccine sceptics, and help develop more effective policies and communication strategies to maximize uptake