ABSTRACT
As the COVID-19 pandemic continued unabatedly, many global supply chains involved in manufacturing and distributing personal protective equipment often failed to meet surge demand due to production capacity limits. Before the COVID-19 pandemic, the existing medical mask supply chain in Taiwan was decentralized, but immediately following the outbreak in 2020, the government of Taiwan established a centralized virtual company that integrated production, distribution, and sales. We use an exploratory empirical case study to gain insights into Taiwan's innovative public-private collaboration and the relationship between collaborative activities and supply chain resilience. This paper examines how a ten-fold growth, from 1.88 million to 20 million, in the daily production of medical masks, and their equitable distribution was achieved within four months of the onset of the COVID-19 pandemic. The results indicate that the public-private collaboration through a government-led centralized supply chain mitigated the impacts of unpredictable disruptions, built supply chain resilience, and ensured mask availability to the public. © 2023 Informa UK Limited, trading as Taylor & Francis Group.
ABSTRACT
Objectives: Association of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and kidney injury has been noted in previous studies. However, the mechanisms remain unknown. The present study aimed to explore the potential mechanisms of kidney injury in COVID-19. Methods: Demographic characteristics, underlying diseases, signs, symptoms, and laboratory data of 100 COVID-19 patients were collected and analyzed in this retrospective study. Patients were divided into three groups: mild, moderate, and severe to critical group. Kidney injury was evaluated by markers including estimated glomerular filtration rate (eGFR), serum creatinine, blood urea nitrogen, and cystatin C. Results: A total of 100 patients with 12 mild, 63 moderate, and 25 severe to critical COVID-19 were included in this study. The kidney injury markers including eGFR, serum creatinine, blood urea nitrogen, and cystatin C all worsened significantly with an increase in disease severity. The correlation test showed that cytokines IL-2R, IL-6, IL-8, and tumor necrosis factor (TNF)-alpha were statistically correlated with eGFR and cystatin C. In multivariate analysis, log IL-6 (beta = 0.331, p = .001 for eGFR and beta = 0.405, p < .001 for cystatin C) and log TNF-alpha (beta = -0.316, p = .001 for eGFR and beta = 0.534, p < .001 for cystatin C) were found to be the major independent predictors of kidney injury. Conclusion: Serum IL-6 and TNF-alpha levels were the major independent predictors of kidney injury in COVID-19.
ABSTRACT
In this study, a survival analysis of the time to death caused by coronavirus disease 2019 is presented. The analysis of a dataset from the East Asian region with a focus on data from the Philippines revealed that the hazard of time to death was associated with the symptoms and background variables of patients. Machine learning algorithms, i.e., dimensionality reduction and boosting, were used along with conventional Cox regression. Machine learning algorithms solved the diverging problem observed when using traditional Cox regression and improved performance by maximizing the concordance index (C-index). Logistic principal component analysis for dimensionality reduction was significantly efficient in addressing the collinearity problem. In addition, to address the nonlinear pattern, a higher C-index was achieved using extreme gradient boosting (XGBoost). The results of the analysis showed that the symptoms were statistically significant for the hazard rate. Among the symptoms, respiratory and pneumonia symptoms resulted in the highest hazard level, which can help in the preliminary identification of high-risk patients. Among various background variables, the influence of age, chronic disease, and their interaction were identified as significant. The use of XGBoost revealed that the hazards were minimized during middle age and increased for younger and older people without any chronic diseases, with only the elderly having a higher risk of chronic disease. These results imply that patients with respiratory and pneumonia symptoms or older patients should be given medical attention.
ABSTRACT
Social bots, or algorithmic agents that amplify certain viewpoints and interact with selected actors on social media, may influence online discussion, news attention, or even public opinion through coordinated action. Previous research has documented the presence of bot activities and developed detection algorithms. Yet, how social bots influence attention dynamics of the hybrid media system remains understudied. Leveraging a large collection of both tweets (N = 1,657,551) and news stories (N = 50,356) about the early COVID-19 pandemic, we employed bot detection techniques, structural topic modeling, and time series analysis to characterize the temporal associations between the topics Twitter bots tend to amplify and subsequent news coverage across the partisan spectrum. We found that bots represented 8.98% of total accounts, selectively promoted certain topics and predicted coverage aligned with partisan narratives. Our macro-level longitudinal description highlights the role of bots as algorithmic communicators and invites future research to explain micro-level causal mechanisms.