Impact of the Covid-19 Pandemic on Audiology Service Delivery: Observational Study of the Role of Social Media in Patient Communication (preprint)

The Covid-19 pandemic has highlighted an era in hearing health care that necessitates a comprehensive rethinking of audiology service delivery. There has been a significant increase in the number of individuals with hearing loss who seek information online. An estimated 430 million individuals worldwide suffer from hearing loss, including 11 million in the United Kingdom. The objective of this study was to identify NHS audiology service social media posts and understand how they were used to communicate service changes within audiology departments at the onset of the Covid-19 pandemic.Facebook and Twitter posts relating to audiology were extracted over a six week period (March 23 to April 30 2020) from the United Kingdom. We manually filtered the posts to remove those not directly linked to NHS audiology service communication. The extracted data was then geospatially mapped, and themes of interest were identified via a manual review. We also calculated interactions (likes, shares, comments) per post to determine the posts efficacy. A total of 981 Facebook and 291 Twitter posts were initially mined using our keywords, and following filtration, 174 posts related to NHS audiology change of service were included for analysis. The results were then analysed geographically, along with an assessment of the interactions within the included posts. NHS Trusts and Boards should consider incorporating and promoting social media to communicate service changes. Users would be notified of service modifications in real-time, and different modalities could be used (e.g. videos), resulting in a more efficient service.

Temporal Vascular Endothelial Growth Factor Sub-type gene Switching in SARS-CoV Pathogenesis. Interpretation through in vivo Murine C57BL Models (preprint)

Introduction: Increased Vascular Endothelial Growth Factor A (VEGF-A) levels are associated with Severe Acute Respiratory (SARS) infection. The aim was to investigate in vivo VEGF-A and VEGF-B (VEGF-A/B) gene expression (GE) in severe pulmonary disease pathogenesis. Method: Twelve temporal Mus musculus Wildtype (WT) C57BL/6 SARS-CoV MA15 lung studies were selected from the NCBI GEO database for GE profiling. Results: In murine dataset (GSE68820) Day 2 was compared to Day 7 demonstrating a downregulation trend in VEGF-A GE, with an opposite effect on VEGF-B GE (p=4.147e-03, p=7.580e-07, respectively). A v-shaped VEGF-B gene expression trajectory was noteworthy across certain datasets and after dORF6 stimulation. In addition, MA15 dose stimulation studies showed that a higher antigenic load caused more profound effects on VEGF-A resulting in a steeper fall in GE compared to other antigens. Conclusions: Distinct temporal trajectory patterns of VEGF-A and VEGF-B gene expression were associated with SARS-CoV MA15 stimulation. Unraveling the importance of VEG-A/B dynamics offers exciting prospects for improved bio-marking and therapeutic precision.

Vasoactive Endothelial Growth Factor and Heat Shock Protein Gene Expression Response in Kawasaki Disease Associated Coronary Arteritis (preprint)

Background and Aim Coronary involvement in Kawasaki Disease (KD), whether its after SARS-CoV2 infection or not, can result in significant complications. There is the risk of aneurysm formation associated with inflammation and an unremitting fever. We wished to study the Vasoactive Endothelial Growth Factor (VEGF) and Heat Shock Response from a gene-expression perspective. Thereby aiming to furnish to insights that might be useful in the treatment of Kawasaki Disease. Method KD datasets based on previous work, were selected including microarray studies KD1 (GSE63881), KD2 (GSE73461), KD3 (GSE68004) and the RNAseq dataset KD4 (GSE64486) from the NCBI online repository. Based on clinical literature. HSP genes shown to be associated with angiogenesis were chosen for analysis as well as gene expression for VEFGA and VEGFB. Further in order to gain an impression of inflammatory patterns, gene expression for NFKB1 and TNF were also chosen. Tools for analysis included Gene Set Expression Analysis (GSEA). A KEGG pathway, outlining a relationship between VEGF and endothelial migration and proliferation was assumed. Results A KD dataset showed increased VEGFA and decreased VEGFB in acute versus convalescent samples. In all three KD datasets, HSPA1A and HSBAP1 genes were upregulated in acute versus convalescent samples. In KD4, cases of KD versus controls, VEGFB was down-regulated (p = 4.932e-02) and HSPBAP1 up-regulated (p = 1.202e-03). GSEA of KD1, KD2 and KD3, using Hallmark gene sets, suggested an inflammatory response with TNFA signaling via NFKB, IL6 JAK STAT 3 signaling, apoptosis, angiogenesis, and unfolded protein response. Conclusions A novel application of a model of VEGF and HSP to KD was presented. Coronary pathogenesis based on VEGF and HSP was explored. The ability to follow angiogenesis at the molecular level using a VEGF-HSP model may have therapeutic implications. Further, the significance of gene expression between VEGFA, VEGFB in KD and the relationship of HSP gene expression to angiogenesis in KD requires further study.

A novel internet of things based on deep neural network framework using soft-attention convolutional neural networks for COVID-19 detection (preprint)

Background: The impact of coronavirus (COVID-19) pandemic on health care is universal. The risks resulting from emerging contagious viruses and the efficacy of vaccines are persisting due to the presence of different variants. Learning of deeper and more interpretable models from COVID-19 data are conducive to understand this disease and to study the virus spread, individual diagnosis and may be other engrossing relating issues. However, some difficulties and intricacies are arising from the scarcity of precisely labelled data. Previous works have exploited existing Deep Neural Network (DNN) models that are pre-trained on large datasets like ImageNet.  Method: In this paper, a new framework is proposed in order to monitor and predict COVID-19 cases and other diseases, pursuing medical data. The currently proposed framework essentially relies on (1) an Internet of Things (IoT) processing model to collect data and operate on them later, (2) a DNN model for data processing, known as REGATT. This proposed model is based on a pre-trained REGNet model finely tuned by spatial, channel ATTention and convolutional layers, boosting feature representation and discrimination.  Results: Comparative experimental results on four different benchmark datasets show that the proposed model leads to a promising solution for diagnosing COVID-19.  Conclusion: It is concluded that an IoT and DNN-based solution are a viable way for the diagnosis of not only COVID-19 but also other diseases. It is advisable that future works explore the development of interpretable models.

Forecasting COVID-19 Cases in Egypt Using ARIMA-Based Time-Series Analysis

Objectives: The World Health Organization declared the novel coronavirus (COVID-19) outbreak a public health emer?gency of international concern on January 30, 2020. Since it was first identified, COVID-19 has infected more than one hundred million people worldwide, with more than two million fatalities. This study focuses on the interpretation of the distribution of COVID-19 in Egypt to develop an effective forecasting model that can be used as a decision-making mechanism to administer health interventions and mitigate the transmission of COVID-19. Methods: A model was developed using the data collected by the Egyptian Ministry of Health and used it to predict possible COVID-19 cases in Egypt. Results: Statistics obtained based on time-series and kinetic model analyses suggest that the total number of CO?VID-19 cases in mainland Egypt could reach 11076 per week (March 1, 2020 through January 24, 2021) and the number of simple regenerations could reach 12. Analysis of the ARIMA (2, 1, 2) and (2, 1, 3) sequences shows a rise in the number of COVID-19 events. Conclusion: The developed forecasting model can help the government and medical personnel plan for the imminent conditions and ensure that healthcare systems are prepared to deal with them.

CovidTracker: A comprehensive Covid-related social media dataset for NLP tasks (preprint)

The Covid-19 pandemic presented an unprecedented global public health emergency, and concomitantly an unparalleled opportunity to investigate public responses to adverse social conditions. The widespread ability to post messages to social media platforms provided an invaluable outlet for such an outpouring of public sentiment, including not only expressions of social solidarity, but also the spread of misinformation and misconceptions around the effect and potential risks of the pandemic. This archive of message content therefore represents a key resource in understanding public responses to health crises, analysis of which could help to inform public policy interventions to better respond to similar events in future. We present a benchmark database of public social media postings from the United Kingdom related to the Covid-19 pandemic for academic research purposes, along with some initial analysis, including a taxonomy of key themes organised by keyword. This release supports the findings of a research study funded by the Scottish Government Chief Scientists' Office that aims to investigate social sentiment in order to understand the response to public health measures implemented during the pandemic.

iWorkSafe: Towards Healthy Workplaces during COVID-19 with an Intelligent pHealth App for Industrial Settings (preprint)

The recent outbreak of the novel Coronavirus Disease (COVID-19) has given rise to diverse health issues due to its high transmission rate and limited treatment options. Almost the whole world, at some point of time, was placed in lock-down in an attempt to stop the spread of the virus, with resulting psychological and economic sequela. As countries start to ease lock-down measures and reopen industries, ensuring a healthy workplace for employees has become imperative. Thus, this paper presents a mobile app-based intelligent portable healthcare (pHealth) tool, called iWorkSafe, to assist industries in detecting possible suspects for COVID-19 infection among their employees who may need primary care. Developed mainly for low-end Android devices, the iWorkSafe app hosts a fuzzy neural network model that integrates data of employees' health status from the industry's database, proximity and contact tracing data from the mobile devices, and user-reported COVID-19 self-test data. Using the built-in Bluetooth low energy sensing technology and K Nearest Neighbor and K-means techniques, the app is capable of tracking users’ proximity and trace contact with other employees. Additionally, it uses a logistic regression model to calculate the COVID-19 self-test score and a Bayesian Decision Tree model for checking real-time health condition from intelligent e-health platform for further clinical attention of the employees. Rolled out in an apparel factory on 12 employees as a test case, the pHealth tool generates an alert to maintain social distancing among employees inside the industry. In addition, the app helps employee to estimate risk with possible COVID-19 infection based on the collected data and found that the score is effective in estimating personal health condition of the app user.

Artificial intelligence-enabled analysis of UK and US public attitudes on Facebook and Twitter towards COVID-19 vaccinations (preprint)

BackgroundGlobal efforts towards the development and deployment of a vaccine for SARS-CoV-2 are rapidly advancing. We developed and applied an artificial-intelligence (AI)-based approach to analyse social-media public sentiment in the UK and the US towards COVID-19 vaccinations, to understand public attitude and identify topics of concern. MethodsOver 300,000 social-media posts related to COVID-19 vaccinations were extracted, including 23,571 Facebook-posts from the UK and 144,864 from the US, along with 40,268 tweets from the UK and 98,385 from the US respectively, from 1st March - 22nd November 2020. We used natural language processing and deep learning based techniques to predict average sentiments, sentiment trends and topics of discussion. These were analysed longitudinally and geo-spatially, and a manual reading of randomly selected posts around points of interest helped identify underlying themes and validated insights from the analysis. ResultsWe found overall averaged positive, negative and neutral sentiment in the UK to be 58%, 22% and 17%, compared to 56%, 24% and 18% in the US, respectively. Public optimism over vaccine development, effectiveness and trials as well as concerns over safety, economic viability and corporation control were identified. We compared our findings to national surveys in both countries and found them to correlate broadly. ConclusionsAI-enabled social-media analysis should be considered for adoption by institutions and governments, alongside surveys and other conventional methods of assessing public attitude. This could enable real-time assessment, at scale, of public confidence and trust in COVID-19 vaccinations, help address concerns of vaccine-sceptics and develop more effective policies and communication strategies to maximise uptake.