Monitoring and Early Warning of SMEs' Shutdown Risk under the Impact of Global Pandemic Shock

The COVID-19 outbreak devastated business operations and the world economy, especially for small and medium-sized enterprises (SMEs). With limited capital, poorer risk tolerance, and difficulty in withstanding prolonged crises, SMEs are more vulnerable to pandemics and face a higher risk of shutdown. This research sought to establish a model response to shutdown risk by investigating two questions: How do you measure SMEs' shutdown risk due to pandemics? How do SMEs reduce shutdown risk? To the best of our knowledge, existing studies only analyzed the impact of the pandemic on SMEs through statistical surveys and trivial recommendations. Particularly, there is no case study focusing on an elaboration of SMEs' shutdown risk. We developed a model to reduce cognitive uncertainty and differences in opinion among experts on COVID-19. The model was built by integrating the improved Dempster's rule of combination and a Bayesian network, where the former is based on the method of weight assignment and matrix analysis. The model was first applied to a representative SME with basic characteristics for survival analysis during the pandemic. The results show that this SME has a probability of 79% on a lower risk of shutdown, 15% on a medium risk of shutdown, and 6% of high risk of shutdown. SMEs solving the capital chain problem and changing external conditions such as market demand are more difficult during a pandemic. Based on the counterfactual elaboration of the inferred results, the probability of occurrence of each risk factor was obtained by simulating the interventions. The most likely causal chain analysis based on counterfactual elaboration revealed that it is simpler to solve employee health problems. For the SMEs in the study, this approach can reduce the probability of being at high risk of shutdown by 16%. The results of the model are consistent with those identified by the SME respondents, which validates the model.

Delayed Care Seeking and Outcomes of Testicular Torsion among Children during the COVID-19 Pandemic: A Systematic Review and Meta-Analysis

Introduction Since the onset of coronavirus disease 2019 (COVID-19), stay-at-home orders and fear caused by the pandemic have had a significant effect on the timing and outcomes of testicular torsion. However, the evidence was limited since the study results were inconsistent. This study aims to examine the hospitalization rates, timing, and outcomes of testicular torsion in children before and during the pandemic. Materials and Methods Using PubMed, Embase, and Google Scholar databases, we conducted a systematic search and meta-analysis of studies reporting the timing and outcomes of children admitted with testicular torsion before and during the COVID-19 pandemic. Subgroup analyses were conducted to explore possible sources of heterogeneity. Result The outcomes of 899 testicular torsion patients from eight studies were evaluated. Our study found an increased hospitalization rate for patients with testicular torsion (incidence rate ratio = 1.60, 95% confidence interval [CI]: 1.27-2.03;p = 0.001). Despite a significant increase in the duration of symptoms during the COVID-19 pandemic (weighted mean difference = 11.04, 95% CI: 2.75-19.33;p = 0.009), orchiectomy rates did not increase (odds ratio = 1.33, 95% CI: 0.85-2.10;p = 0.147). Conclusion During the COVID-19 pandemic, hospitalization rates for testicular torsion and the duration of symptoms among children increased significantly. Moreover, the rate of orchiectomy did not increase during the pandemic, indicating that pediatric emergency services have remained efficient and have prevented an increase in the number of orchiectomies performed despite pandemic-related closures and delays in transporting patients to medical care.

Model selection with decision support model for US natural gas consumption forecasting

Reliable prediction of natural gas consumption helps make the right decisions ensuring sustainable economic growth. This problem is addressed here by introducing a hybrid mathematical model defined as the Choquet integral-based model. Model selection is based on decision support model to consider the model performance more comprehensively. Different from the previous literature, we focus on the interaction between models when combine models. This paper adds grey accumulation generating operator to Holt-Winters model to capture more information in time series, and the grey wolf optimizer obtains the associated parameters. The proposed model can deal with seasonal (short-term) variability using season auto-regression moving average computation. Besides, it uses the long short term memory neural network to deal with long-term variability. The effectiveness of the developed model is validated on natural gas consumption due to the COVID-19 pandemic in the USA. For this, the model is customized using the publicly available datasets relevant to the USA energy sector. The model shows better robustness and outperforms other similar models since it consider the interaction between models. This means that it ensures reliable perdition, taking the highly uncertain factor (e.g., the COVID-19) into account. © 2023 Elsevier Ltd

Effectiveness of Remdesivir as Treatment for COVID-19 Positive US Veterans

Background. The COVID 19 disease has claimed over 6.3 million lives, globally. Despite such high casualties, the treatment options are limited. Although the FDA issued emergency use authorizations for oral antivirals to treat mild-to-moderate COVID 19 disease, intravenous Remdesivir treatment remains the only fully FDA-approved antiviral. However, many early studies questioned its efficacy. Accordingly, the WHO initially recommended against its use in COVID 19 positive patients. Based on the newly emerging data, as of 22 April 2022, WHO suggests that Remdesivir can be effectively used in mild or moderate COVID 19 cases. This retrospective cohort data analysis was undertaken to evaluate and clarify the effectiveness of Remdesivir use in older US veterans. Methods. The deidentified veterans' data were accessed from the VA COVID 19 Shared Data Resources with local ethical approvals. Propensity matched cohorts with and without Remdesivir treatment were analyzed using Cox regression models, constructed in a way to avoid immortal time and calendar time biases. Limited to hospitalized veterans, patients were followed for 60 days to the outcomes of mechanical ventilation (MV) and death in separate models. The cohort was also limited to those who received low flow without high flow oxygen and a combination of low and high flow oxygen in another set of models. Results. A total of 3,372 veterans were included in this study who were hospitalized between 01 January to 31 December 2021 for COVID 19 disease. Of those, 1,686 received Remdesivir treatment, while their matches never received it. After propensity score matching that included demography, vaccination status, comorbidities, medication use, lab tests, Remdesivir recipients and controls were similar in age (66.8+/-14.1 vs. 67.0+/-13.8 years). Relative risk reductions (1-HR), 53% for MV, and 42% for death (Fig. 1) were observed with low flow oxygen and Remdesivir therapy. In veterans who received high and low flow oxygen, although there was a significant 18% reduction in risk for death, progression to MV was not significant (P=0.22). (Figure Presented) Conclusion. The data showed significant risk reductions of disease progression to MV/death when Remdesivir was used in COVID 19 positive patients with low supplementary oxygen flow, supporting the current NIH recommendation.

Factors affecting consumer behaviour during the COVID-19 pandemic in Malaysia

Consumer behavior has changed during the Covid-19 pandemic in all spheres of life. In Malaysia, there was a surge in e-commerce, a preference to buy essential goods from trusted brands while being cautious with spending. During the pandemic, Malaysian consumers have been more careful about spending their money and where they spend their money. Based on the review of past literature, the study's goal was to examine the relationships of variables such as perceived severity, cyberchondria, self-efficacy, and self-isolation on consumer behavior during the Covid-19 pandemic in Malaysia. The aim of the study was also to highlight the implications of the study that will be beneficial to the Malaysian government, the consumer association, and retailers. The quantitative research method was used to conduct this study via online questionnaires. The target respondents were consumers from Selangor between the ages of 20 to 60, mainly those with jobs and who earned a monthly income. A total of 196 respondents answered the questionnaire. The reliability, linearity, normality, correlation, and multiple regression tests were conducted using SPSS. The study results revealed that only perceived severity and self-isolation had significant relationships with consumer behavior. The scientific novelty of the study was that both cyberchondria and self-efficacy were insignificant. These findings imply that both cyberchondria and self-efficacy do not affect the consumer behaviour of Malaysian during the pandemic. The implications of the research findings were discussed.

Mathematical artificial intelligence design of mutation-proof COVID-19 monoclonal antibodies

Emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants have compromised existing vaccines and posed a grand challenge to coronavirus disease 2019 (COVID-19) prevention, control, and global economic recovery. For COVID-19 patients, one of the most effective COVID-19 medications is monoclonal antibody (mAb) therapies. The United States Food and Drug Administration (U.S. FDA) has given the emergency use authorization (EUA) to a few mAbs, including those from Regeneron, Eli Elly, etc. However, they are also undermined by SARS-CoV-2 mutations. It is imperative to develop effective mutation-proof mAbs for treating COVID-19 patients infected by all emerging variants and/or the original SARS-CoV-2. We carry out a deep mutational scanning to present the blueprint of such mAbs using algebraic topology and artificial intelligence (AI). To reduce the risk of clinical trial-related failure, we select five mAbs either with FDA EUA or in clinical trials as our starting point. We demonstrate that topological AI-designed mAbs are effective for variants of concerns and variants of interest designated by the World Health Organization (WHO), as well as the original SARS-CoV-2. Our topological AI methodologies have been validated by tens of thousands of deep mutational data and their predictions have been confirmed by results from tens of experimental laboratories and population-level statistics of genome isolates from hundreds of thousands of patients.

A Fuzzy Bayesian Network Based on Fault Tree for Vaccine Safety Risks Analysis

Against the Covid-19 background, vaccine safety has aroused the wild attention of all social areas. However, the factors that cause vaccine safety risks are complicated and meanwhile, data is difficult to obtain, making it a challenge for analyzing vaccine safety risks quantitatively. This paper concretises the issue of vaccine system safety by creatively proposing an analytical framework for the problem of uncertainty. First, the paper focuses on the whole process of vaccine safety, analyses risk factors affecting vaccine safety in development, approval, production, transportation, and supervision of vaccines in order to build a vaccine risk assessment system. The proposed framework is then used to construct a Bayesian network early warning system for vaccine risk. To address the difficulty of obtaining data, the probability of safety risks occurring throughout the process is calculated by combining expert knowledge and fuzzy set theory to obtain uncertainty data. In response to structural complexity, a comprehensive framework is constructed using fault trees and Bayesian networks to capture the correlation between risk factors. This analytical framework can provide guidance to governments and vaccine-related companies in their decision-making to prevent vaccine safety issues. Finally, sensitivity analysis revealed a high probability of vaccine risk in the transport process. © 2021 IEEE.

[Epidemiology, clinical characteristics and treatment in 278 patients with coronavirus disease 2019 in Guangzhou].

Objective: To retrospective analyze the epidemiology, clinical characteristics, treatment and prognosis in patients with coronavirus disease 2019 (COVID-19). Methods: A total of 278 patients with COVID-19 admitted to Guangzhou Eighth People's Hospital from January 20 to February 10, 2020 were selected. The general demographic data, epidemiological data, clinical symptoms, laboratory examinations, lung CT imaging, treatment and prognosis were analyzed. Results: There were 130 male patients (46.8%) and 148 females (53.2%) with age (48.1±17.0) years and 88.8% patients between 20-69 years. Two hundred and thirty-six (84.9%) patients had comorbidities. Two hundred and eleven cases (75.9%) were common type. The in-hospital mortality was 0.4% (1/278). The majority (201, 72.3%) were imported cases mainly from Wuhan (89, 44.3%). The most common clinical manifestations were fever (70.9%) and dry cough (61.5%). In some patients, hemoglobin (10.4%), platelets (12.6%) and albumin (55.4%) were lower than the normal range. Other biochemical tests according to liver and function were normal, while lactic dehydrogenase (LDH) was elevated in 61 patients (21.9%), creatine kinase increased in 26 patients (9.4%). Prolonged activated partial thromboplastin time (APTT) was seen in 52 patients (18.7%), D-dimer higher than normal in 140 patients (50.4%), while 117 patients (42.1%) had elevated high-sensitivity C-reactive protein. Typical CT manifestations included single or multiple ground glass shadows especially in lung periphery in early disease which infiltrated and enlarged during progressive stage. Diffuse consolidation with multiple patchy density in severe/critical cases and even "white lung" presented in a few patients. Two hundred and forty-two patients (87.1%) received one or more antiviral agents, 242 (87.1%) combined with antibacterials, 191 (68.7%) with oxygen therapy. There were 198 patients (71.2%) treated with traditional Chinese medicine. Conclusions: COVID-19 could attack patients in all ages with majority of common type and low mortality rate. Clinical manifestations involve multiple organs or systems. Progression of the disease results in critical status which should be paid much attention.

Impact of the COVID-19 pandemic on neuropsychiatric symptoms of patients with dementia and the mental health of their caregivers

Objective This study investigated the impact of the COVID-19 pandemic and the public-health measures aiming to reduce the spread of COVID-19 (i.e restrictions, social distancing, self-isolation) on neuropsychiatric symptoms of patients with different forms of dementia and the mental health of their caregivers in Germany. Method An online-survey was conducted for caregivers of people with dementia to assess the impact of COVID-19 and COVID-19 related measures on both the caregivers and dementia patients via self-reports by the caregiver. Results 78 caregivers participated in the study. Following the beginning of the COVID-19 pandemic, worsened neuropsychiatric symptoms such as apathy, depression, anxiety, and agitation were regularly reported. Not living with the caregiver was associated with a worsening of depressive symptoms in patients. A better understanding of the situation by the patient was instead related to low risk for depressive aggravation. Many of the caregivers (51.3 %) themselves also experienced worsened mental health. Perceived loneliness and worries regarding the future associated with the pandemic were related to deterioration in mental health. Conclusions Future health-strategies should be informed by the needs of both patients with dementia and their caregivers to prevent the worsening of neuropsychiatric symptoms in patients and in mental health of their caregivers. One-to-one support, as a way of example, is considered beneficial by the majority of caregivers and could be a useful tool to attenuate these harmful effects. © 2021 Georg Thieme Verlag. All rights reserved.

African americans have lower COVID-19 mortality risk than caucasians in CKD

Background: In the general population, African Americans have increased mortality risk with COVID-19. However, this has not been well-studied in CKD population. Methods: We analyzed a national Veteran cohort using data from the VA COVID-19 Shared Data Resource for COVID-positive patients (N=196,269) from 3/1/2020 -3/9/2021. Diagnosis of COVID-19 was defined as a confirmed positive laboratory test result. Index date was defined as the date of first positive COVID-19 test or the first negative test for patients who never tested positive for COVID-19. Baseline eGFR was defined as at least one outpatient serum creatinine measurement obtained within two years before the index date or the average of the two closest serum creatinine measurements obtained within two years before the index date. We identified 58,743 patients with valid eGFR measurements. Of this cohort, 51,002 were African American or Caucasian. Mortality data were available for 50,830 patients. We used Cox regression models to compare COVID-19 mortality in African Americans versus Caucasians based on pre-COVID eGFR stratification. Results: Of the COVID-positive patients with available eGFR and mortality data, baseline mean age was 60 ± 17 years, 24% African American, 76% Caucasian, and 21% with eGFR <60. There were 627 deaths among African Americans and 2,480 deaths among Caucasians. Average follow-up duration was 0.5 ± 0.3 years in African Americans and 0.4 ± 0.2 years in Caucasians. While there was no difference in mortality risk between African American and Caucasian Veterans without CKD, African Americans had lower mortality risk when compared to Caucasians in the CKD subgroup (Table 1). Conclusions: In the CKD subgroup, African Americans have lower COVID-19 mortality than Caucasians. The reasons for this observation are unclear.

273 Reluctance towards digital image sharing and challenges for teledermatology

Background: Patient reluctance to engage in telemedicine remains a key challenge to digital expansion in the era of COVID-19. Teledermatology, in particular, is heavily impacted by this, given its’ foundation in visual assessments. An understanding of patient attitudes towards digital image sharing and determinants of these attitudes is necessary to address patient-centered barriers to teledermatology adoption. Objective: To evaluate digital image sharing preferences and predictors of patient preferences. Methods: We conducted a secondary analysis of pooled data from the Health Information National Trends Survey 4, Cycle 3 and 4, a cross-sectional survey of 6,437 US adults. Differences in willingness to electronically exchange digital images/videos (e.g., skin lesions) with providers were compared by patient characteristics and beliefs. Results: Overall, 53.5% of US adults reported disinclination towards digitally exchanging images and videos with their providers. Disinterest was greater in adults aged 75 or above (70.9%), retired (67.3%), with less than a high school education (65.1%), with less than $20,000 annual income (60.9%), and limited English proficiency (63.3%). Further, aversion was also higher among adults who distrust health information from doctors (75.4%), lack mobile device ownership (77.1%), and have fair or poor health (60.4%). Conclusion: Disinclination towards digital image sharing may pose challenges for teledermatology adoption among certain groups during this period of telehealth growth. Improved efforts targeting barriers to adoption, including older age, lower socioeconomic status, language barriers, worse health, and poorer physician-patient relationship dynamics, are needed to ensure vulnerable groups are not left without needed dermatologic care.

SARS-CoV-2 becoming more infectious as revealed by algebraic topology and deep learning

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused by coronavirus disease 2019 (COVID-19) has led to a tremendous human fatality and economic loss. SARS-CoV-2 infectivity is a key reason for the widespread viral transmission, but its rigorous experimental measurement is essentially impossible due to the ongoing genome evolution around the world. We show that artificial intelligence (AI) and algebraic topology (AT) offer an accurate and efficient alternative to the experimental determination of viral infectivity. AI and AT analysis indicates that the on-going mutations make SARS-CoV-2 more infectious.