Applying a Hybrid MCDM Model to Evaluate Green Supply Chain Management Practices

The COVID-19 pandemic has prompted global supply chain managers to reassess their operations. Developing a green supply chain requires successfully integrating environmental responsibility principles and benchmarks into supply chain management practices. In the past, there have been few studies on the most effective strategies for reducing the environmental impact of supply chains and improving their sustainability. This study used the decision-making trial and evaluation laboratory (DEMATEL) method to construct a structural model evaluation system of the green supply chain management (GSCM) to evaluate the interdependent relationships among dimensions and criteria. A GSCM evaluation system was created after using the DEMATEL-based ANP (DANP) to convert the GSCM evaluation indicators and impact factors into degrees of importance. This study explores the obstacles and challenges that organizations face when implementing GSCM practices and how these challenges can be overcome. The results found that organizational changes had the most significant impact, given that they would also improve the other three dimensions. Among the 16 evaluation criteria, resource allocation and market expansion optimization were the most important. Based on these findings, the study proposed specific improvement strategies that corporations and other stakeholders could use to adopt GSCM practices. © 2023 by the authors.

[Inference of start time of resurgent COVID-19 epidemic in Beijing with SEIR dynamics model and evaluation of control measure effect].

Objective: To infer the start time of the resurgent COVID-19 epidemic in Xinfadi wholesale market in Beijing in June 2020 and evaluate the effect of comprehensive prevention and control measures in this epidemic. Methods: SEIR dynamics model was used to fit daily onset infections to search the start date of this resurgent COVID-19 epidemic in Beijing. The number of cumulative infections from June 12 to July 1 in Beijing were fitted considering different levels of control strength. Results: The current reemerged COVID-19 epidemic in Beijing probably started between May 22 and May 28 (cumulative probability: 95%), with the highest probability on May 25 (23%). The R(0) of the current reemerged COVID-19 epidemic was 4.22 (95%CI: 2.88-7.02). Dynamic model fitting suggested that by June 11, the cumulative number of COVID-19 cases would reached 99 (95%CI: 77-121), which was in line with the actual situation, and without control, by July 1, the cumulative number of COVID-19 cases would reach 65 090 (95%CI: 39 068-105 037). Since June 12, comprehensive prevention and control measures have been implemented in Beijing, as of July 1, compared with uncontrolled situation, the number of infections had been reduced by 99%, similar to the fitting result of a 95% reduction of the transmission rate. The sensitivity analysis showed consistent results. Conclusions: For the emergent outbreak of COVID-19, the dynamics model can be used to infer the start time of the transmission and help tracing the source of epidemic. The comprehensive prevention and control measures taken in Beijing have quickly blocked over 95% of the transmission routes and reduced 99% of the infections, containing the sudden epidemic timely and effectively, which have value in guiding the prevention and control of the epidemic in the future.

[Using Markov Chain Monte Carlo methods to estimate the age-specific case fatality rate of COVID-19].

Objectives: The COVID-19 epidemic has swept all over the world. Estimates of its case fatality rate were influenced by the existing confirmed cases and the time distribution of onset to death, and the conclusions were still unclear. This study was aimed to estimate the age-specific case fatality rate of COVID-19. Methods: Data on COVID-19 epidemic were collected from the National Health Commission and China CDC. The Gamma distribution was used to fit the time from onset to death. The Markov Chain Monte Carlo simulation was used to estimate age-specific case fatality rate. Results: The median time from onset to death of COVID-19 was M=13.77 (P(25)-P(75): 9.03-21.02) d. The overall case fatality rate of COVID-19 was 4.1% (95%CI: 3.7%-4.4%) and the age-specific case fatality rate were 0.1%, 0.4%, 0.4%, 0.4%,0.8%, 2.3%, 6.4%, 14.0 and 25.8% for 0-, 10-, 20-, 30-, 40-, 50-, 60-, 70- and ≥80 years group, respectively. Conclusions: The Markov Chain Monte Carlo simulation method adjusting censored is suitable for case fatality rate estimation during the epidemic of a new infectious disease. Early identification of the COVID-19 case fatality rate is helpful to the prevention and control of the epidemic.

[Risk assessment of global COVID-19 imported cases into China].

Objective: To assess the risk of COVID-19 foreign imports cases to China. Methods: We collected epidemic data (cumulative daily confirmed cases in each country, cumulative confirmed imported cases), demographic data (population density, population) and information on potential source groups of tourists (the daily estimated number of overseas Chinese, overseas Chinese students, overseas workers, foreign students coming to China and flight passengers) and the global health security index (GHS) to assess and predict risk of imported cases for recent (February 1(st) to April 25(th)) and future (after April 26(th)). Results: Strong positive correlation was found among variables including the number of imported cases, cumulative confirmed cases, attack rate, number of overseas Chinese, number of overseas Chinese students, number of foreign students coming to China, number of flight passengers and GHS. In the recent risk analysis, imported cases of Russian were the highest, followed by United Kingdom, United States, France and Spain. In the future risk prediction, 44 countries including United States and Singapore are evaluated as potential high-risk countries in the future through the attack rate index of each country and the estimated average number of daily passengers. Conclusion: The risk assessment of COVID-19 imported cases can be used to identify high-risk areas in recent and future, and might be helpful to strengthen the prevention and control of the epidemic and ultimately overcome the epidemic.