Modeling of the resilient supply chain system from a perspective of production design changes

Building an effective resilient supply chain system (RSCS) is critical and necessary to reduce the risk of supply chain disruptions in unexpected scenarios such as COVID-19 pandemic and trade wars. To overcome the impact of insufficient raw material supply on the supply chain in mass disruption scenarios, this study proposes a novel RSCS considering product design changes (PDC). An RSCS domain model is first developed from the perspective of PDC based on a general conceptual framework, i.e., function-context-behavior-principle-state-structure (FCBPSS), which can portray complex systems under unpredictable situations. Specifically, the interaction among the structure, state and behavior of the infrastructure system and substance system is captured, and then a quantitative analysis of the change impact process is presented to evaluate the resilience of both the product and supply chain. Next, a case study is conducted to demonstrate the PDC strategy and to validate the feasibility and effectiveness of the RSCS domain model. The results show that the restructured RSCS based on the proposed strategy and model can remedy the huge losses caused by the unavailability of raw materials.

An Explainable Machine Learning Framework for Forecasting Crude Oil Price during the COVID-19 Pandemic

Financial institutions, investors, central banks and relevant corporations need an efficient and reliable forecasting approach for determining the future of crude oil price in an effort to reach optimal decisions under market volatility. This paper presents an innovative research framework for precisely predicting crude oil price movements and interpreting the predictions. First, it compares six advanced machine learning (ML) models, including two state-of-the-art methods: extreme gradient boosting (XGB) and the light gradient boosting machine (LGBM). Second, it selects novel data, including user search big data, digital currencies and data on the COVID-19 epidemic. The empirical results suggest that LGBM outperforms other alternative ML models. Finally, it proposes an interpretable framework for facilitating decision making to interpret the prediction results of complex ML models and for verifying the importance of various features affecting crude oil price. The results of this paper provide practical guidance for participants in the crude oil market.

The differential diagnosis of pneumonia in two patients infected by atypical pathogens.

The current study presents two patients who lived in a rural family with close contact and suffered from rapidly progressive pneumonia. Chest computed tomography images and lymphocytopenia indicated the possibility of COVID-19 infection, but antibody and nucleic acid tests excluded this possibility. Negative results were obtained from corresponding tests for pneumococcal, adenovirus, fungal and legionella infection. Metagenomics analysis and subsequent antibody tests confirmed mycoplasma pneumonia. After treating with moxifloxacin, both patients recovered well and left the hospital. In terms of complicated infectious disease, consideration of atypical pathogens and medical and epidemiological history were important for differential diagnosis of COVID-19; metagenomics analysis was useful to provide direct references for diagnosis.

Etiology and antimicrobial resistance of secondary bacterial infections in patients hospitalized with COVID-19 in Wuhan, China: a retrospective analysis.

BACKGROUND: A considerable proportion of patients hospitalized with coronavirus disease 2019 (COVID-19) acquired secondary bacterial infections (SBIs). The etiology and antimicrobial resistance of bacteria were reported and used to provide a theoretical basis for appropriate infection therapy. METHODS: This retrospective study reviewed electronic medical records of all the patients hospitalized with COVID-19 in the Wuhan Union Hospital between January 27 and March 17, 2020. According to the inclusion and exclusion criteria, patients who acquired SBIs were enrolled. Demographic, clinical course, etiology, and antimicrobial resistance data of the SBIs were collected. Outcomes were also compared between patients who were classified as severe and critical on admission. RESULTS: Among 1495 patients hospitalized with COVID-19, 102 (6.8%) patients had acquired SBIs, and almost half of them (49.0%, 50/102) died during hospitalization. Compared with severe patients, critical patients had a higher chance of SBIs. Among the 159 strains of bacteria isolated from the SBIs, 136 strains (85.5%) were Gram-negative bacteria. The top three bacteria of SBIs were A. baumannii (35.8%, 57/159), K. pneumoniae (30.8%, 49/159), and S. maltophilia (6.3%, 10/159). The isolation rates of carbapenem-resistant A. baumannii and K. pneumoniae were 91.2 and 75.5%, respectively. Meticillin resistance was present in 100% of Staphylococcus aureus and Coagulase negative staphylococci, and vancomycin resistance was not found. CONCLUSIONS: SBIs may occur in patients hospitalized with COVID-19 and lead to high mortality. The incidence of SBIs was associated with the severity of illness on admission. Gram-negative bacteria, especially A. baumannii and K. pneumoniae, were the main bacteria, and the resistance rates of the major isolated bacteria were generally high. This was a single-center study; thus, our results should be externally examined when applied in other institutions.

Ratios of neutrophil-to-lymphocyte and platelet-to-lymphocyte predict all-cause mortality in inpatients with coronavirus disease 2019 (COVID-19): a retrospective cohort study in a single medical centre.

The Coronavirus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a public health emergency of international concern. The current study aims to explore whether the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) are associated with the development of death in patients with COVID-19. A total of 131 patients diagnosed with COVID-19 from 13 February 2020 to 14 March 2020 in a hospital in Wuhan designated for treating COVID-19 were enrolled in the current study. These 131 patients had a median age of 64 years old (interquartile range: 56-71 years old). Furthermore, among these patients, 111 (91.8%) patients were discharged and 12 (9.2%) patients died in the hospital. The pooled analysis revealed that the NLR at admission was significantly elevated for non-survivors, when compared to survivors (P < 0.001). The NLR of 3.338 was associated with all-cause mortality, with a sensitivity of 100.0% and a specificity of 84.0% (area under the curve (AUC): 0.963, 95% confidence interval (CI) 0.911-1.000; P < 0.001). In view of the small number of deaths (n = 12) in the current study, NLR of 2.306 might have potential value for helping clinicians to identify patients with severe COVID-19, with a sensitivity of 100.0% and a specificity of 56.7% (AUC: 0.729, 95% CI 0.563-0.892; P = 0.063). The NLR was significantly associated with the development of death in patients with COVID-19. Hence, NLR is a useful biomarker to predict the all-cause mortality of COVID-19.