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COVID-19 global pandemic has been significantly damaging the human well-being, life style of people, and the global economy. At the beginning of the epidemic, there were no successful candidates available even though scientists rushed to find one since the start of the official declaration of the pandemic by the World Health Organization (WHO). Thus the countries around the world mostly practiced nonpharmaceutical interventions (NPIs) in various combinations to combat the virus expecting to minimize the public health burden. Since the identification of the first COVID-19 local case on March 11, 2020, the government of Sri Lanka introduced serious social distancing and public health interventions in their fullest capacity as a developing nation to effectively combat the disease spread. These measures had been considerably successful as they contributed to flattening the epidemic curve within two months. However, the nation was at significant risk to the virus due to evolving global epidemic situation, and several clusters of cases have subsequently emerged from a number of jurisdictions in the country. Mathematical models are used to predict the epidemic, to assess the efficacy of public health interventions and social distancing measures recommended by health authorities, to evaluate the national policies to ease lock-down measures in highly affected countries, and the model-based outcomes are extremely useful in various optimal decision-making in public health. Multiple SEIR (Susceptible-Exposed-Infected-Recovered) class models were applied to investigate the early stage of the COVID-19 transmission dynamic in Sri Lanka. These models coupled with optimization techniques were used to estimate the initial transmission rate and the initial epidemic size of the outbreak. A theoretical optimal control problem was developed to identify best allocation of resources in three types of control measures that minimizes the infection in the community. A sensitivity analysis of parameters such as the efficacy of NPIs over the early phase of the outbreak, their timing of implementation, and management of imported overseas cases was carried out mainly addressing their importance in mild and critically ill cases projections. © 2023 Elsevier Inc. All rights reserved.
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Spatial skills are critical for understanding the relations among objects and people, playing an important role in how we interact with the world. Spatial relationships are built through interactions with physical objects;however, in computational/online environments, these change to bi-dimensional media and computer-assisted design comprised of 3D representations viewable through a flat screen. Due to spatial immersion and interaction limitations, a traditional 2D and 3D approach presents challenges to partially sighted, blind, and sighted individuals. This paper presents the prototyping of a co-design Augmented Reality (AR) authoring tool by recruiting inclusive emerging affordances of consumer-level AR technologies within the context of current e-learning provisions in subject matters, including inclusive design, engineering design, game hardware design, and health sciences. This work has been inspired by the COVID-19 pandemic that has shown the need to level the field in inclusive design for teaching a subject typically oriented to the sighted. Our prototype allows users to create e-learning content for visualization, interaction, collaboration, and inclusive learning. Future work will investigate our tool's impact on skills development and content creation. © 2022 IEEE.
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Superspreading has become a key mechanism of COVID-19 transmission which creates chaos. The classical approach of compartmental models may not sufficiently reflect the epidemiological situation amid superspreading events (SSEs). We perform a data-driven approach and recognise the deterministic chaos of confirmed cases. The first derivative (≈difference of total confirmed cases) and the second derivative (≈difference of the first derivative) are used upon SSEs to showcase the chaos. Varying solution trajectories, sensitivity and numerical unpredictability are the chaotic characteristics discussed here. © 2023 Indian Institute of Health Management Research.
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Offsite construction (OSC) in Southeast Asia is facing many challenges due to the COVID-19 pandemic. Despite its importance, there is scant research on this topic in the region. This study aims to review OSC and identify determinants of success and their correlation with success after the pandemic in the Southeast Asian region. This paper follows a sequential exploratory mixed methodology: (1) reviewing statistical data relevant to construction in four countries, (2) carrying out a configurative literature review and documentary research, (3) eliciting responses from in-depth interviews consisting of two phases, (4) collecting written consultancies, and (5) capturing experts' opinions in four countries through a questionnaire survey. The study uses three statistical techniques to determine the correlations between factors: ordinal logistic regression with factors input, a generalized linear model with ordinal logistic response and covariates, and Spearman's correlations. Based on 55 variables and 459 responses from 4 countries, the statistical calculations identify the 42 most significant pairs of relationships between determinants and success, from which the follow-up priorities involve economic differentiation, productivity in the factory setting, reducing uncertainty over the program, and minimizing disturbances in the vicinity of the site under post-COVID-19 controls. This empirical research generates three outcomes: (1) filling the gap of OSC review in the region, (2) offering topical solutions for the construction industry after COVID-19, and (3) helping four countries derive economic benefits from OSC.
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Introduction: SARS-CoV-2 infection can cause manifestations in multiple systems in the body.1 Haematological system is one such system which can be affected by both SARS-CoV-2 infection and critical illness. There are some studies that have shown that thrombocytopaenia can be associated in patients with SARS-CoV-2 infection.2 There is minimal data on its prevalence and outcome on critically ill patients. One study has shown that low platelet is associated with worsening of respiratory parameters and long term adverse outcome in critically ill.3 Identifying the presence of thrombocytopaenia and its consequences is important to manage critically ill COVID-19 patients. Objectives: To identify the prevalence and consequences of thrombocytopaenia and the presence of other causes of thrombocytopaenia in critically ill COVID-19 patients. Methods: A retrospective observational study was done in COVID designated Intensive care unit and High dependency unit in Base Hospital-Teldeniya, Sri Lanka. All patients with positive SARS-CoV-2 testing who later develops pneumonia with oxygen dependency and requiring ICU or HDU care were included in the study. Data was collected from patient records for the duration from 1st of January 2021 to 30th of June 2021. Their demographic data, data related to platelet counts and other causes leading to thrombocytopaenia were collected. Thrombocytopaenia was categorised as mild (platelet count 100-150× 109/L), Moderate (50-100×109/L) and severe (less than 50×109/L) Results: Total of 189 patients were admitted to either ICU or HDU requiring oxygen therapy due to COVID pneumonia during the study period. The mean age was 60.59 years with SD of 14.9. Age range was 16 to 94 years. 63 patients (33.33%) had thrombocytopenia with 8 (12.69%), 19 (30.15%) and 36 (57.14%) had severe, moderate, and mild thrombocytopenia respectively. 28 (44.4%) of the patients had low platelets at the time of admission. 28.5% of the patients had another cause for thrombocytopenia. Figure 1 shows other causes contributed to thrombocytopenia. Out of the patents who had thrombocytopenia 17 (26.9%) patients died. This was equal to 48.7% of all critical care deaths. The proportion of death among patients with thrombocytopenia was fund to be significant compared to that of patients with a normal platelet count. (p=0.045) Conclusions: Thrombocytopenia may arise due to multiple aetiologies in critically ill patients. Our study shows that at least 1/3 of the critically ill patients with COVID-19 infection develop thrombocytopenia at some point of their course of illness. It is important to have an insight on the progression of the illness and the outcome in order to plan discharge and follow up for these patients.
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Introduction The COVID-19 pandemic has shifted services offered in clinic online, with some local authorities starting to offer Emergency Hormonal Contraception (EHC) through a sexual health e-service. After completing an online, asynchronous consultation, EHC could be delivered or collected in pharmacy. Method Retrospective analysis of 12,016 EHC consultations completed between 1st January 2021 and 31st December 2021, from 11 local authority areas in London. Age, ethnicity, and deprivation deciles were used to identify demographic trends. Results The majority of EHC consultations were completed by 25-34 year olds (41.0%, n=4922). 53.4% (n=6422) of consultations were completed by users from the bottom 3 deciles of deprivation. 74.6% (n=8967) of consultation were from racially minoritised groups. Amongst 16-17 year olds and 35-44 year olds, the majority of consultations were completed by users in the bottom 3 deciles of deprivation (58.5% (n=197) and 58.6% (n=495) respectively). 66.2% (n=1289) of Black African women were from the bottom 3 deciles of deprivation, as were 58.6% (n=1498) of Black Caribbean women and 46.4% (n=1416) of White British women. 53.1% (n=1033) of Black African women, 54.3% (n1388) of Black Caribbean women, and 47.1% (n=1435) of White British women, were 16-24 years old. Discussion The majority of individuals accessing online EHC are young and racially minoritised, experiencing significant levels of deprivation. This approach to helping users access online EHC could help reduce unintended pregnancies and mitigate health inequalities. Local authorities could consider whether an online EHC service would help in their local area.
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Introduction The COVID-19 pandemic has moved some health services, such as contraception, online. Eleven local authorities in London enrolled for a routine contraception (RC) e-service. After completing an online, asynchronous consultation, RC could be delivered or collected in pharmacy. Method Retrospective analysis of 10,525 RC consultations completed between 1st January 2021 and 31st December 2021, from 11 local authorities in London. Age, ethnicity and deprivation decile were used to identify demographic trends. Results The majority of RC consultations were completed by 25-34 year olds (47.9%, n=5042). The majority (61.2%, n=6442) of consultations were completed by users in the lower deciles 2-4. 66.7% (n=7021) of consultations were completed by users from ethnic minority groups. In 16-17 year olds and 35-44 year olds, the majority of RC consultations were completed by users in the bottom 3 deciles of deprivation (55.5% (n=146) and 55.8% (n=383) respectively). 68% (n=607) of Black African women were from the bottom 3 deciles of deprivation, as were 59.5% (n=606) of Black Caribbean women as compared to 40.6% (n1432) of White British women. 46.1% (n=412) of Black African women, 51.9% (n=576) of Black Caribbean women, and 45.5% (n=1595) of White British women, were 16-24 years old. Discussion The majority of individuals accessing RC online are young and racially minoritised, experiencing significant levels of deprivation. An online RC service can reach populations with the poorest sexual health outcomes, reduce unintended pregnancies and help reduce health inequalities. Local authorities could consider whether an online RC service would help in their area.
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Introduction: COVID-19 resulted in many office workers switching to remote work. Emerging studies report working from home has negatively affected sleep health (SH) and psychological well-being. Our aim was to evaluate the relationship between SH and healthand work-related quality of life and explore whether these associations differed pre- and post-COVID-19 emergence. Methods: Baseline data from 125 adults enrolled pre- (n=59) and post-COVID-19 emergence (n=66) in a clinical trial with desk jobs were included in this analysis (86.4% White;49.6% female;43.9±10.7 y). Health-related quality of life (HRQoL) was assessed using the SF-36 questionnaire, which addresses eight health concepts (physical, social, and role functioning;mental health;health perceptions;energy or fatigue;pain;general health) and yields 2 summary scales (mental component summary, physical component summary). Workplace productivity and worker health was measured using the Health and Work Questionnaire (HWQ). Six SH dimensions were assessed using questionnaires (satisfaction, alertness) and 7 nights of actigraphy (regularity, timing, efficiency, duration). Each dimension was categorized as good or poor;a composite score was created based on the sum of good SH dimensions. Multiple linear regression models were adjusted for gender and age and stratified by enrollment pre- or post-COVID-19 emergence. Data are presented as standardized coefficients (β) and p-values (p). Results: Compared to participants enrolled prior to COVID-19, those enrolled post-COVID-19 had worse SF-36 emotional, social, and general health and greater HWQ-assessed impatience (all p<0.05);however, SH did not differ between those enrolled pre- and post-COVID. Prior to COVID-19, greater SH was associated with higher SF-36 physical component scores (β=.389, p=.003);however, no association was observed post-COVID (β=.137, p=.271). In contrast, no association was observed pre-COVID between SH and SF-36 mental component scores (β=.181, p=.160), but greater SH was associated with greater mental component scores post- COVID (β =.308, p=.004). Furthermore, better SH was associated with lower stress post-COVID (β =-.423, p<.001). Conclusion: SH was associated with HRQoL and workplace and worker health, though these associations sometimes differed between pre- and post-COVID emergence. Research should explore whether promoting SH in employees impacts their personal and workplace-related quality of life.
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The tremendous growth of social media empowers the rapid dissemination and amplification of information by becoming the most influenceable communication tool with a significant extent of human lives. People extensively use those platforms during the outbreak of the COVID-19 to cope with social distancing and isolation by expressing their opinions, views, and issues they are facing. The study used Twitter as the social media platform to discover the emotional perspective of Sri Lankans during the outbreak. The emotion analysis was conducted based on 8-scale emotions (anger, anticipation, disgust, fear, joy, sadness, surprise, and trust) provided by the NRC Emotion Lexicon. The extraction of noteworthy topics propagated in different domains of COVID-19 helps to identify the perception of society during the pandemic. Latent Dirichlet Allocation was applied to explore relevant and accurate topics. Furthermore, the wordcloud was generated by representing the most insightful data points which help to keep track of social concerns or viewpoints. Our experiment shows that people discuss under three major themes of "COVID-19 Immunization", "Government rules and regulations to prevent the spread of COVID-19"and "COVID-19 related health statistics". Furthermore, the "fear"is dominated among eight emotions followed by "anticipation"and "sadness"when Sri Lankans express their thoughts and feelings related to COVID-19. There is a noticeable decrease in "joy"which is the least expressed emotion followed by "trust". Hence, clearly defined, these findings support in predicting the COVID-19 related outcomes and addressing the concerns, emotions, and reactions of citizens by the government and the policymakers. © 2022 IEEE.
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Social media platforms are open for society to distribute numerous vectors of information upon personal interest. A substantive component of social media platforms is mining long-term trends. COVID-19 is an indispensable keyword captivating from last year in multiplex social media platforms providing opportunities for societal to express opinions in multiple languages. Existing literature is scarce in multiple aspects, such as the lack of resources that provide high accuracy long-term trend detection in social media platforms and missing the substantive factor for long-term trend detection, and time series forecasting over Natural Language Processing. This research presents a novel methodology to extract insights into low resource language and the responses are validated using the built crowdsourcing platform. Additionally, research employees with several classical time series analysis techniques and different LSTM RNN to identify the best-performing long-term trend analysis method. Novel concept deployed by extracting public data available in YouTube and Twitter which has at least one year of life expressing content related to COVID-19. Preponderance textual engagements were felt under the low resource language category. Crowdsourcing supports mitigating the risk of the intimidating issue. Finally, the data set is employed with time series forecasting. Mean Square Error and Huber loss were employed for testing. Authors argue there is no long-term trend in Twitter posts and YouTube videos with comments more petite than a hundred. YouTube videos with more than a hundred comments were further analyzed and the authors revealed that Stacked LSTM outperforms the LSTM used for trend analysis for more than one year by achieving 84.96% accuracy. © 2022 IEEE.
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Due to the 2020 COVID-19 pandemic, online virtual gathering platforms have risen at all levels as alternatives to traditional videoconferencing. The implementation of physical distancing and limited capacity implemented to reduce the spread of COVID-19 changed how academic activities are conducted. For example, student outreach presents students with experiential learning opportunities and teamwork on campus. While video conferencing tools have thrived over the pandemic, these lack immersion and presence, lead to fatigue, and lack engagement. In this paper, we present the development and hosting of a Design Challenge employing the open-source virtual reality (VR) platform, Hubs by Mozilla. Usability perceptions from five out of ten participants were gathered and analyzed employing a simplified version of the System Usability Scale questionnaire. The process of developing the Mozilla Hubs environment allowed us to identify technical issues associated with performance and audio quality. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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The rapid and seemingly endless expansion of COVID-19 can be traced back to the inefficiency and shortage of testing kits that offer accurate results in a timely manner. An emerging popular technique, which adopts improvements made in mobile ultrasound technology, allows for healthcare professionals to conduct rapid screenings on a large scale. We present an image-based solution that aims at automating the testing process which allows for rapid mass testing to be conducted with or without a trained medical professional that can be applied to rural environment and third world countries. Our contributions towards rapid large-scale testing includes a novel deep learning architecture capable of analyzing ultrasound data that can run in real time and significantly improve the current state-of-the-art detection accuracies using image based COVID-19 detection. © 2021 IEEE.
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This paper presents a novel ultrasound imaging point-of-care (PoC) COVID-19 diagnostic system. The adaptive visual diagnostics utilize few-shot learning (FSL) to generate encoded disease state models that are stored and classified using a dictionary of knowns. The novel vocabulary based feature processing of the pipeline adapts the knowledge of a pretrained deep neural network to compress the ultrasound images into discrimative descriptions. The computational efficiency of the FSL approach enables high diagnostic deep learning performance in PoC settings, where training data is limited and the annotation process is not strictly controlled. The algorithm performance is evaluated on the open source COVID-19 POCUS Dataset to validate the system's ability to distinguish COVID-19, pneumonia, and healthy disease states. The results of the empirical analyses demonstrate the appropriate efficiency and accuracy for scalable PoC use. The code for this work will be made publicly available on GitHub upon acceptance. © 2021 IEEE.
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Objectives: Discussion based on three clinical cases of the applicability of videoconsultation in the Primary Care practice. Description: Videoconsults are a modality of telemedicine that allows for an improved doctor-patient interaction and resembles in-person consultations. Despite overwhelming evidence at an international level, it is not until the arrival of the COVID-19 pandemic that it has found a place in our country. In the evaluation and follow-up of patients with mental health problems, it grants a climate of trust that enables the demonstration of emotions with more facial contact than in on-site consults and translates into a method that complements and expands the necessary clinical information. In the monitoring of respiratory diseases, videoconsults permit an improved education in management of inhaled therapy and the recording of the inhaling technique in a safe environment. In the management of COVID-19 it may be the consult of choice to discriminate patients with moderate affectation and indication of complementary tests. Conclusion: The main advantages to videoconsults are the greater facial interaction and the avoidance of unnecessary displacements. A need exists to resolve certain technical aspects, encourage training and creating a culture that facilitates its progressive implementation.