Risk Factors for Severity and Mortality in Adult Patients Confirmed with COVID-19 in Sierra Leone: A Retrospective Study

Background:The coronavirus disease 2019 (COVID-19) is a highly infectious respiratory disease. There is no recommended antiviral treatment approved for COVID-19 in Sierra Leone, and supportive care and protection of vital organ function are performed for the patients. This study summarized the clinical characteristics, drug treatments, and risk factors for the severity and prognosis of COVID-19 in Sierra Leone to provide evidence for the treatment of COVID-19.Methods:Data of 180 adult COVID-19 patients from the 34th Military Hospital in Freetown Sierra Leone between March 31, 2020 and August 11, 2020 were retrospectively collected. Patients with severe and critically ill are classified in the severe group, while patients that presented asymptomatic, mild, and moderate disease were grouped in the non-severe group. The clinical and laboratory information was retrospectively collected to assess the risk factors and treatment strategies for severe COVID-19. Demographic information, travel history, clinical symptoms and signs, laboratory detection results, chest examination findings, therapeutics, and clinical outcomes were collected from each case file. Multivariate logistic analysis was adopted to identify the risk factors for deaths. Additionally, the clinical efficacy of dexamethasone treatment was investigated.Results:Seventy-six (42.22%) cases were confirmed with severe COVID-19, while 104 patients (57.78%) were divided into the non-severe group. Fever (56.67%, 102/180) and cough (50.00%, 90/180) were the common symptoms of COVID-19. The death rate was 18.89% (34/180), and severe pneumonia (44.12%, 15/34) and septic shock (23.53%, 8/34) represented the leading reasons for deaths. The older age population, a combination of hypertension and diabetes, the presence of pneumonia, and high levels of inflammatory markers were significantly associated with severity of COVID-19 development (P < 0.05 for all). Altered level of consciousness [odds ratio (OR) = 56.574, 95% confidence interval (CI) 5.645-566.940, P = 0.001], high levels of neutrophils (OR = 1.341, 95%CI 1.109-1.621, P = 0.002) and C-reactive protein (CRP) (OR = 1.014, 95%CI 1.003-1.025, P = 0.016) might be indicators for COVID-19 deaths. Dexamethasone treatment could reduce mortality [30.36% (17/56) vs. 50.00% (10/20)] among severe COVID-19 cases, but the results were not statistically significant (P > 0.05).Conclusions:The development and prognosis of COVID-19 may be significantly correlated with consciousness status, and the levels of neutrophils and CRP. © 2022 Journal of Bone and Joint Surgery Inc.. All rights reserved.

A Reinforcement Learning Variable Neighborhood Search for the Robust Dynamic Bayesian Network Optimization Problem under the Supply Chain Ripple Effect

Due to the impact of the global COVID-19, numerous industries have suffered from the disruption propagating along the supply chain, i.e. the ripple effect. To reduce adverse impact of the ripple effect, supply chain (SC) risk management under it is becoming an increasingly hot topic in both practice and research. In our former research, a robust dynamic bayesian network (DBN) approach has been developed for disruption risk assessment, whereas the solution methods adopted before (commercial solvers and simulated annealling algorithm) are not efficient enough, especially for large-size instances. For this reason, a new reinforcement learning variable neighborhood search (QVNS) is developed for solving the robust DBN optimization model, where the Q-learning algorithm is implemented to select the most efficient neighborhood structure in different stages of the search process. We conduct computational experiments on randomly generated instances, which indicates that Q-learning algorithm can improve significantly the performance of the VNS on large-size instances of the robust DBN optimization problem. Copyright (C) 2022 The Authors.

Characteristics of the atmospheric pollutants at traffic monitoring sites in Beijing during 2018~2020

To explore the impacts on both air pollution and to monitor traffic pollution in Beijing, 18 air quality monitoring stations were selected from the 34 existing stations, including 5 traffic stations, 11 urban stations evaluated and 2 background stations (Miyun and Dingling). We compared the concentrations with the 5 major pollutants, NO2, CO, O3, PM2.5 and PM10, at the selected stations during 2018~2020, we found that these five pollutants decreased during these three years, and the decreases were generally 3%~50% higher in the traffic stations evaluated than in the urban stations evaluated. The decreases of NO2, CO, O3, and PM2.5 were more important at the traffic stations evaluated than at the urban stations evaluated. The seasonal variation at each monitoring station was also different. O3 was higher in summer and lower in winter, and the highest value in June 2018. The comparison to the concentrations of the other four pollutants NO2, CO, PM2.5 and PM10 were also higher in winter and lower in summer. In March 2018, the concentration of pollutants was extremely higher due to dust and adverse meteorological conditions. To investigate the influence of COVID-19, the concentrations for these five pollutants were compared in 2019 and 2020. The results showed that NO2 decreased most in comparison to the same period in 2019. At traffic stations evaluated, the average decreases among NO2, CO and PM2.5 were 4.81%, 10.21% and 4.38% respectively higher than those at the urban stations evaluated. © 2021, Editorial Board of China Environmental Science. All right reserved.

Stochastic Integrated Supplier Selection and Disruption Risk Assessment Under Ripple Effect

The impact of the COVID-19 pandemic in the supply chain (SC) evokes the need for valid measures to cope with the SC disruption risk. Supplier selection and disruption risk assessment, as valid measures, have received increasing attentions from academia. However, most of existing works focus on supplier selection and disruption risk assessment separately. This work investigates an integrated supplier selection and disruption risk assessment problem under ripple effect. The objective is to minimize the weighted sum of the disrupted probability and the total cost for the manufacturer. For the problem, a new stochastic programming model combined with Bayesian network (BN) is formulated. Then, an illustrative example is conducted to demonstrate the proposed method. © 2021, IFIP International Federation for Information Processing.

A Tabu Search Heuristic for the Robust Dynamic Bayesian Network Optimisation Problem Under the Supply Chain Ripple Effect

Due to the impact of the global COVID-19, supply chain (SC) risk management under the ripple effect is becoming an increasingly hot topic in both practice and research. In our former research, a robust dynamic bayesian network (DBN) approach has been developed for disruption risk assessment, whereas there still exists a gap between the proposed simulated annealing (SA) algorithm and commercial solver in terms of solution quality. To improve the computational efficiency for solving the robust DBN optimisation model, a tabu search heuristic is proposed for the first time in this paper. We design a novel problem-specific neighborhood move to keep the search in feasible solution space. The computational experiments, conducted on randomly generated instances, indicate that the average gap between our approach and commercial solver is within 0.07 %, which validates the performance of the proposed method. © 2021, IFIP International Federation for Information Processing.