摘要： The study in this paper analyzes the model of the spread of COVID-19 in Papua Province. The population is divided into five subpopulations, namely Suspected (S), Infected (I), Independent Isolation (Q1), Isolation provided by the Regional Government of Papua Province (Q2), and Recovered (R). Parameter values were obtained from COVID-19 data for June-August 2021 in Papua Province. The method for estimating the parameters in the model study uses the least square fitting technique and some of the parameters are taken from other journals, with data sources in Indonesia. Model analysis determines non-endemic stability points, and endemic models determine reproduction numbers. The model analyzed local and global asymptotic stability. The model solution is solved by using the fourth-order Runge-Kutta method.

关键词： Event study   Machine learning   COVID-19   Pharmaceutical sector   Neural network   India   STOCK-MARKET RESPONSE   NEURAL-NETWORKS   PRODUCT INTRODUCTIONS   PUBLIC ANNOUNCEMENTS   EMPIRICAL-ANALYSIS   IMPACT   RETURNS   INNOVATION   PRICE   UNCERTAINTY  

摘要： PurposeThis paper aims to examine the impact of announcements related to 77 interventions by 46 listed Indian pharmaceutical firms during COVID-19 on the abnormal returns of the firms. The study also finds the variables which explain cumulative abnormal returns (CARs).Design/methodology/approachThis study uses standard event methodology to compute the abnormal returns of firms announcing pharmaceutical interventions in 2020 and 2021. Besides this, the multilayer perceptron technique is applied to identify the variables that influence the CARs of the sample *** results show the presence of abnormal returns of 0.64% one day before the announcement, indicating information leakage. The multilayer perceptron approach identifies five variables that explain the CARs of the sample companies, which are licensing_age, licensing_size, size, commercialization_age and approval_***/valueThe study contributes to the efficient market literature by revealing how firm-specific nonfinancial disclosures affect stock prices, especially in times of crisis like pandemics. Prior research focused on determining the effect of COVID-19 variables on abnormal returns. This is the first research to use artificial neural networks to determine which firm-specific variables and pharmaceutical interventions can influence CARs.

关键词： Case report   Chronic Inflammatory Demyelinating   COVID-19 vaccine   Polyradiculoneuropathy  

摘要： BackgroundSevere post-vaccination neurological complications are rare. Chronic Inflammatory Demyelinating Polyneuropathy (CIDP) is an immune-mediated polyneuropathy affecting the peripheral nerve roots, which is not well described as a post-vaccination side effect. Here, we present a rare complication of vaccination against SARS-CoV-2, reaching a diagnosis of *** presentationA 67-year-old diabetic male presented with lower extremity paresthesia and weakness following the third dose of the Sinopharm (BBIBP-CorV) vaccine. Despite initial dismissal as a diabetic complication, symptoms escalated, affecting all extremities. Electromyography study revealed abnormal spontaneous activity with chronic reinnervation changes, which was more significant in the lower extremities. Based on the clinical course, radiographic imaging, and laboratory data, a diagnosis of CIDP with severe axonal demyelinating features was established. Treatment with intravenous immunoglobulin (IVIg), prednisolone, and azathioprine resulted in marked improvement of the upper extremities but limited recovery in distal lower extremity *** CIDP is a rare complication following COVID-19 vaccination, it should be considered in the differential diagnosis. Timely diagnosis of vaccine-induced CIDP is challenging, and any delay can adversely affect treatment response in affected patients.

关键词： ENGAGEMENT   ADVICE   PEOPLE   POLICY  

摘要： In early 2021, the United Kingdom (UK) had the highest per capita death rate from Covid-19 of any large country. Yet it had previously been ranked as one of the best prepared countries for a future pandemic. This gap between preparedness and performance has been the subject of intense debate, including as part of the UK Covid-19 Inquiry. In this paper, we contribute to this ongoing process of reflection by identifying the imagined public(s) within the UK's scientific advice system. Drawing on scholarship in Science and Technology Studies (STS) that critiques framings of a singular or homogeneous 'public', we review meeting minutes and media briefings to reveal two imagined publics, co-constructed by the UK's science advisors and policymakers in early 2020: first, a 'freedom-loving' public resistant to stringent policy interventions; and second, a public that was-in an echo of wartime rhetoric- 'all in it together'. These imagined publics reflect a series of framing assumptions that help to make sense of the UK's pandemic response. We focus particularly on the tensions between the homogeneous and multi-faceted imagined public, and the compound health and social inequalities that predated the pandemic but became starker and more visible as it unfolded. Our paper charts these tensions and demonstrates how these imagined publics went through stages of cohesion and fracture in the fraught early months of the pandemic. We conclude by considering the implications of this analysis for understanding the UK's response to Covid-19, and for the future of scientific advice and emergency preparedness. Why does this matter? Studies of scientific advice reveal that how scientists and decision makers imagine the public and their concerns affect the communication of scientific advice, and the construction and value placed on relevant knowledge. Advisory scientists frame their models and their advice in terms of what they regard as politically possible.

关键词： Tuberculosis   COVID-19   Treatment   Outcomes  

摘要： BackgroundThe COVID-19 pandemic disrupted tuberculosis (TB) health services, including treatment support and access to drugs, as patients were not able to access health facilities. While the effect of this disruption on treatment outcomes has been studied in isolated treatment centres, cities and provinces, the impact of the pandemic on TB treatment outcomes at a country and regional level has not been *** used treatment outcomes for new and relapse TB cases reported to the World Health Organization (WHO) from 49 high TB, TB/HIV and drug-resistant TB burden countries from 2012 to 2019. We developed multinomial logistic regression models for trends in TB treatment success, failure, death and loss to follow up. We predicted TB treatment outcomes for 2020 and 2021, comparing these to observations, by computing ratios between observed and predicted probabilities. We aggregated these risk ratios (RR) for six WHO-defined regions using random-effects *** 49 countries and four TB treatment outcomes, 17 (out of 196) country-outcome pairs in 2020 and 21 in 2021 had evidence of systematic differences between observed and predicted TB treatment outcome probabilities. Regionally, only four (out of 24) region-outcome pairs had evidence of systematic differences in 2020 and four in 2021, where the European region accounted for four of these in total. Globally, there was evidence of systematic differences in treatment failure in both 2020 (RR: 1.14, 95%CI: 1.01-1.28, p = 0.0381) and 2021 (RR: 1.36, 95%CI: 1.03-1.78, p = 0.0277), deaths in 2020 (RR: 1.08, 95%CI: 1.03-1.13, p = 0.0010) and losses to follow up in 2020 (RR: 0.91, 95%CI: 0.86-0.97, p = 0.0059).ConclusionsWhile for some countries and regions there were significant differences between observed and predicted treatment outcomes probabilities, there was insufficient evidence globally to identify systematic differences between observed and expected TB treatment outcome probabilities bec

关键词： COVID-19 infection experiences   Depressive mood   Learning modality preferences   PREVALENCE  

摘要： The COVID-19 pandemic has significantly impacted the mental health of children and adolescents worldwide. While various factors contributing to this phenomenon have been explored, the role of learning style preferences remains underexamined. This cross-sectional study, conducted between October and November 2020, involved over 20,000 participants, including students from junior high school, senior high school, and undergraduate levels. Significant differences in depressive mood levels were observed among individuals with different learning modality preferences upon the return to traditional, in-person learning. Students with in-person learning preferences exhibited lower depression levels, while students with no preference for either learning style outperformed devotees of remote learning. The highest depression occurred among those endorsing neither learning mode. Importantly, the impact of these preferences on depressive moods was found to be moderated by COVID-19 infection experiences. The findings highlight the nuanced relationship between learning style preferences, COVID-19 infection experiences, and mental health outcomes among students. Acknowledging the moderating effect of infection experiences is crucial for developing targeted interventions and adapting pedagogical approaches post-pandemic. This study contributes valuable insights into the complex relationships shaping the mental well-being of students amidst educational disruptions caused by the pandemic.

摘要： The coronavirus disease 2019 (COVID-19) outbreak from Wuhan, China has spread very quickly in the world and caused many deaths. In Indonesia, the first case of COVID-19 was reported by the government in March 2020 then it became a pandemic disease. Estimating case increase rate (CIR) and case fatality rates (CFR) are important parameters for studying the basics of pandemic disease. In this paper, we analyse the case increase and case fatality rates of COVID-19 in Indonesia based on longitudinal data and average temperature by using biresponse local linear estimator. The characteristics of case increase and case fatality rates of COVID-19 in each province are different, so locally modeling is more appropriate. The result shows, on average, the highest rate of case increase was in July 2020 and the highest rate of case fatality in August 2020. This happened because of government policies that loosen social distancing programs and increased human mobility due to the holiday in the previous month. Based on average temperature, in November 2020, there were the lowest decreassing CIR and the highest increassing CFR because in November, it entered the rainy season which makes the average temperature lower. It means that the number of COVID-19 cases will be higher if the average temperature is lower.

关键词： Attitude   Actual usage   Credibility   Effort expectancy   Performance expectancy   Risk   Self-efficacy   Telemedicine   Usage intention   USER ACCEPTANCE   INFORMATION-TECHNOLOGY   COMPUTER-TECHNOLOGY   UNIFIED THEORY   HEALTH-CARE   SYSTEM   SATISFACTION   INTENTIONS   DISTANCE   SUPPORT  

摘要： PurposeThis study aims to investigate the consumer behavior toward telemedicine services in India during the COVID-19 pandemic onset. With lockdown restrictions and safety concerns in visiting brick-and-mortar clinics or hospitals during the pandemic, Telemedicine had emerged as a potent alternative for seeking redressal to health issues. Based on theory and focus interviews with the telemedicine users, the researchers proposed a model to understand the intent and actual usage of telemedicine in ***/methodology/approachThe cross-sectional study undertaken used a questionnaire designed on a seven-point Likert scale and administered to respondents with the objective of identifying the determinants of intent and actual usage of telemedicine services. Simple random sampling was used to collect primary data. The data was cleaned and finally a sample of 405 responses complete in all respects was considered for analysis. The questionnaire comprised of 34 items and following the recommendation of Hair et al. (2016), which says the minimum sample size in structural equation modeling should be ten times the number of indicator variables, a sample size of 405 was deemed *** research paper finds that performance expectancy, attitude, credibility and self-efficacy positively impact the intention of consumers to use telemedicine services. As the effort expectancy or risk perception toward telemedicine increases the intent and actual usage of telemedicine decreases. The intention to use telemedicine emerged as a strong predictor of the actual usage of telemedicine. Intent to use telemedicine was explained 81.4% by its predictors of performance expectancy, effort expectancy, attitude, risk, credibility and self-efficacy, and actual usage was explained 79.9% by its predictors. This study also reports that telemedicine was found to be popular among chronic as well as episodic patients though the preference was skewed in favor of the episodic patients. One of

摘要： Capsule Conventional Networks are one of most used AI methods in the area of medical image analysis and categorization. Various modules utilize deep capsule convolutional networks to analyze selected chest X-ray images related to COVID-19. Due to the lack of sufficient availability of medical image datasets, applying dense and effective Capsule CNN is an arduous task. In order to overcome challenges such as limited image size and inadequate datasets, the dataset implements diverse preprocessing techniques across multiple stages. These methods are employed to ensure successful training of the dataset using capsule Convolutional Neural Networks and achieve optimal and accurate results. Additionally, transfer learning is incorporated to enhance the overall training process. The dataset undergoes various preprocessing stages, including dataset balancing, image analysis by medical advisors, and augmentation techniques for the images. This proposed module was evaluated in 2 varied situations. In the case, fine clinical images and scans were preprocessed and assessed and it achieved 100% accuracy was achieved. In the second case, it was an exclusive dataset and the module proficiency was 99%. The performance of the proposed model demonstrates strong efficacy when applied to a specific dataset for testing and evaluation.

关键词： Chronic kidney diseases   Hemodialysis   COVID-19   Family caregivers   Caregiver burden   MORTALITY   CRITERIA   DISEASE   BURDEN  

摘要： BackgroundThe Coronavirus disease 2019 (COVID-19) pandemic has significantly affected various aspects of public health. The virus poses a particular threat to individuals with kidney failure undergoing hemodialysis and their caregivers. The present study investigated the experience of family caregivers of individuals undergoing hemodialysis about caring during the COVID-19 pandemic in *** qualitative study was conducted with 17 family caregivers of individuals undergoing hemodialysis in Bojnurd, Iran using inductive qualitative content analysis. The participants were selected using convenience and purposive sampling method with maximum variation. Semi-structured interviews were used in data collection based on the interview guide. The data were analyzed with *** results culminated in the identification of a main category of the COVID-19 care burden on caregivers and two generic categories including the COVID-19 Overt (financial/constraining) care burden (subcategories: Non-adherence to Health Protocols, COVID-19 Financial Costs, COVID-19 Restrictions and Hemodialysis Appointments, and Decreased Caregiver Support during the COVID-19 Era), and the COVID-19 Covert (emotional/psychological) Care Burden (subcategories: Caregiver's Loneliness in the Care, Stress of Contracting COVID-19, Psychological Consequences of individuals undergoing hemodialysis Staying at Home, The burden of other Individuals' Expectations of the Caregiver, and Physical and emotional pressure on the Caregiver).ConclusionCaregivers during the COVID-19 period have experienced both overt and covert care burden. The results of this study can contribute to understanding the experiences of caregivers of individuals with chronic diseases such as kidney failure, in critical conditions like the COVID-19 pandemic, by healthcare teams and devising strategies and programs to support them.