Data Analytics for COVID-19 Pandemic

Since the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, the pandemic situation has begun to undergo positive changes with the joint efforts of various countries and world organizations. However, pressures such as the COVID-19 mutations and the sharp rise in confirmed cases have brought uncertainties to the prevention and control of the pandemic. The overall situation is still severe and complex. Based on the multi-dimensional spatial-temporal COVID-19 data collected by the open-source NetEase News (NEN) website and a real-time dynamic website, it is to explore the characteristics of the pandemic data, visualize the development trend, and analyze the spread of the pandemic in this paper. Moreover, it is to provide a rule basis for the prevention and control of the COVID-19 pandemic by constructing the decision tree model. From the results, some suggestions are provided for decision-makers.

Benchmarking the failure assessment of perishable product supply chain using fault tree approach: insights from apple case study of India

PurposeThe purpose of this paper is to facilitate perishable product supply chain (PPSC) managers and practitioners to assess PPSC failure events. The paper proposed fault tree methodology for assessing failures associated with PPSC for evaluating the performance in terms of effective PPSC management adoption.Design/methodology/approachInitially, different failure events were identified from literature and semi-structured interviews from experts. Fault tree model was developed from the identified failure events. Probability of failure events was calculated using Poisson distribution based on the annual reports and interviews conducted from experts. Further, qualitative analysis – minimum cut sets (MCSs), structural importance coefficient (SIC) – and quantitative analysis – Birnbaum importance measure (BIM), criticality importance factor (CIF) and diagnosis importance factor (DIF) – were performed for ranking of failure events. In this study, fault tree development and analysis were conducted on apple supply chain to present the authenticity of this method for failure analysis.FindingsThe findings indicate that the failure events, given as failure at production and procurement (A2), that is, involvement of middleman (BE3), handling and packaging failure (BE4) and transportation failure (A3), hold the highest-ranking scores in analysis of PPSC using fault tree approach.Originality/valueThis research uses the modularization approach for evaluation of failure events of PPSC. This paper explores failures related to PPSC for efficient management initiatives in apple supply chain context. The paper also provides suggestion from managerial perspective with respect to each failure event.

Clinical Data Analysis for Prediction of Cardiovascular Disease Using Machine Learning Techniques

Cardiovascular disease is difficult to detect due to several risk factors, including high blood pressure, cholesterol, and an abnormal pulse rate. Accurate decision-making and optimal treatment are required to address cardiac risk. As machine learning technology advances, the healthcare industry’s clinical practice is likely to change. As a result, researchers and clinicians must recognize the importance of machine learning techniques. The main objective of this research is to recommend a machine learning-based cardiovascular disease prediction system that is highly accurate. In contrast, modern machine learning algorithms such as REP Tree, M5P Tree, Random Tree, Linear Regression, Naive Bayes, J48, and JRIP are used to classify popular cardiovascular datasets. The proposed CDPS’s performance was evaluated using a variety of metrics to identify the best suitable machine learning model. When it came to predicting cardiovascular disease patients, the Random Tree model performed admirably, with the highest accuracy of 100%, the lowest MAE of 0.0011, the lowest RMSE of 0.0231, and the fastest prediction time of 0.01 seconds.

Investigating Orthorexia Nervosa With The ORTO-R in a Sample of University Students With Or Without Subthreshold Autism Spectrum: Focus On Dietary Habits and Sex Differences

Purpose: The aim of the present study was to investigate the presence of ON symptoms, measured by means of the ORTO-R, in a sample of University students with or without AT, specifically focusing on evaluating the role of sex and of dietary habits in the association between ON and autism spectrum. Methods: Subjects were requested to anonymously fulfil the ORTO-R and the Adult Autism Subthreshold Autism Spectrum (AdAS Spectrum) through an online form. Results: 2140 students participated in the study. Subjects with significant AT reported significantly higher ORTO-R scores than subjects without AT. Females and subjects following a vegetarian/vegan diet reported significantly higher ORTO-R scores than males and than subjects following an omnivorous diet, respectively. Significant positive correlations were found between ORTO-R and AdAS Spectrum scores. A decision tree model, with sex, type of diet and presence of AT as independent variables and ORTO-R score as dependent variable, showed in the first step the presence of significantly higher ORTO-R scores among females than among males, and in the second step showed in both sexes the presence of higher ORTO-R scores among subjects with AT than in those without. A significant interaction of sex and presence/absence of AT was reported on ORTO-R score, with a higher increasing trend of ORTO-R score with the increase of AdAS Spectrum score among females than among males. Conclusions: Our results further highlighted the association between AT and ON, in particular among females.

KL-6 in ANCA-Associated Vasculitis Patients with and without ILD: A Machine Learning Approach

Background: ANCA-associated vasculitis (AAV) are small vessel vasculitis distinguished between microscopic polyangiitis (MPA) and granulomatosis with polyangiitis (GPA). The former may have interstitial lung disease (ILD) associated with high morbidity and mortality. Here, Krebs von den Lungen-6 (KL-6), a marker of fibrotic ILD, was assessed for distinguishing AAV patients with ILD from those without ILD, and whether its changes over time are correlated with disease activity. Materials and Methods: Thirteen AAV patients (eight females, mean age 61 ± 14.8 years) were enrolled: six MPA and six GPA. Serum samples were assayed for KL-6 concentrations (Fujirebio Europe, Belgium). To investigate potential binary classifiers for diagnosis of AAV-ILD, we constructed a regression decision tree model. Results: Higher serum KL-6 were in AAV-ILD compared with those without ILD (972.8 ± 398.5 vs 305.4 ± 93.9, p = 0.0040). Area under the receiver operating characteristics curve showed 100% of the diagnostic performance of KL-6 for identifying the ILD involvement (accuracy 91.7%) and the best cutoff value of 368 U/mL (sensitivity 100% and specificity 87.5%). The decision tree model showed a 33% improvement in class purity using a cut-off value of 513 U/mL to distinguish AAV patients with and without ILD. Stratifying AAV patients as MPA and GPA with and without ILD considering T0 and T1 KL-6, the model obtained an improvement of 40% for classifying GPA non-ILD with a T0 serum KL-6 cut-off value of 513 U/mL and a T1 KL-6 cut-off of 301 U/mL. A direct correlation was found between serum T0 KL-6 and T0 BVAS (r = 0.578, p = 0.044). Conclusion: Our multicenter study demonstrated KL-6 as a reliable, non-invasive, and easy-to-perform marker of ILD in AAV patients and its helpfulness for disease activity assessment. Changes in serum concentrations of KL-6 over time could be useful for monitoring AAV patients. Further study of KL-6 as a marker of response to therapy during long-term follow-up would also be worthwhile.

Polyketide Derivatives in the Resistance of Gerbera hybrida to Powdery Mildew

Powdery mildew is a common disease affecting the commercial production of gerbera flowers (Gerbera hybrida, Asteraceae). Some varieties show a certain degree of resistance to it. Our objective was to identify biomarkers of resistance to powdery mildew using an 1H nuclear magnetic resonance spectroscopy and chemometrics approach in a complex, fully factorial experiment to suggest a target for selection and breeding. Resistant varieties were found to differ from those that were susceptible in the metabolites of the polyketide pathway, such as gerberin, parasorboside, and gerberinside. A new compound probably involved in resistance, 5-hydroxyhexanoic acid 3-O-β-D-glucoside, was described for the first time. A decision tree model was built to distinguish resistant varieties, with an accuracy of 57.7%, sensitivity of 72%, and specificity of 44.44% in an independent test. Our results suggest the mechanism of resistance to powdery mildew in gerbera and provide a potential tool for resistance screening in breeding programs.

Should rapid antigen tests be government funded in Australia? An economic evaluation

Objective: Easy and equitable access to testing is a cornerstone of the public health response to COVID-19. Currently in Australia, testing using Polymerase Chain Reaction (PCR) tests for COVID-19 is free-to-the-user, but the public purchase their own Rapid Antigen Tests (RATs). We conduct an economic analysis of government-funded RATs in Australia. Design: An interactive decision tree model was developed to compare one policy in which government-funded RATs are free-to-the-user, and one in which individuals purchase their own RATs. The decision tree represents RAT and PCR testing pathways for a cohort of individuals without COVID-19-like symptoms, to estimate the likelihood of COVID-19 positive individuals isolating prior to developing symptoms and the associated costs of testing, from a government perspective. Data sources: Test costs and detection rates were informed by published studies, other input parameter values are unobservable and uncertain, for which a range of scenario analyses are presented. Data synthesis: Assuming 10% prevalence of COVID-19 in a cohort of 10,000 individuals who would use government-funded RATs, the model estimates an additional 464 individuals would isolate early at a cost to the government of around $52,000. Scenario analyses indicate that the incremental cost per additional COVID-19 positive individual isolating with no symptoms remains at a few hundred dollars at 5% prevalence, rising to $2,052 at 1% prevalence. Conclusions: Based on the presented decision tree model, even only minor reductions in COVID-19 transmission rates due to early isolation would justify the additional costs associated with a policy of government-funded RATs.

The Method of Real Options and the Business Model «Lean Canvas» in the Practice of Performance Evaluation of it Projects

The relevance of the study is due to the active implementation of IT technologies in various aspects of companies, which gives special importance to the development of a methodology for assessing the effectiveness of projects in a highly uncertain environment. The paper presents the methodology and assesses the effectiveness of IT projects using binomial «decision tree» model and iterative risk assessment metamodel «Lean Canvas». The comparative assessment of IT project efficiency using discounted cash flow method, binomial «decision tree» model and Black–Scholes model was carried out. The results have shown the advantage of option-based approach to the evaluation of IT project efficiency in comparison with the traditional DCF method, which allows to build flexibility in the planning and management of the project, assess its potential and consider the uncertainties as additional opportunities for profit.