Optimization of urban emergency support material distribution under major public health emergencies based on improved sparrow search algorithm.

The outbreak of major public health emergencies such as the coronavirus epidemic has put forward new requirements for urban emergency management procedures. Accuracy and effective distribution model of emergency support materials, as an effective tool to inhibit the deterioration of the public health sector, have gradually become a research hotspot. The distribution of urban emergency support devices, under the secondary supply chain structure of "material transfer center-demand point," which may involve confusing demands, is studied to determine the actual situation of fuzzy requests under the impact of an epidemic outbreak. An optimization model of urban emergency support material distribution, based on Credibility theory, is first constructed. Then an improved sparrow search algorithm, ISSA, was designed by introducing Sobol sequence, Cauchy variation and bird swarm algorithm into the structure of the classical SSA. In addition, numerical validation and standard test set validation were carried out and the experimental results showed that the introduced improved strategy effectively improved the global search capability of the algorithm. Furthermore, simulation experiments are conducted, based on Shanghai, and the comparison with existing cutting-edge algorithms shows that the designed algorithm has stronger superiority and robustness. And the simulation results show that the designed algorithm can reduce vehicle cost by 4.83%, time cost by 13.80%, etc. compared to other algorithms. Finally, the impact of preference value on the distribution of emergency support materials is analyzed to help decision-makers to develop reasonable and effective distribution strategies according to the impact of major public health emergencies. The results of the study provide a practical reference for the solution of urban emergency support materials distribution problems.

Study on optimization of multi-UAV nucleic acid sample delivery paths in large cities under the influence of epidemic environment.

In the context of global novel coronavirus infection, we studied the distribution problem of nucleic acid samples, which are medical supplies with high urgency. A multi-UAV delivery model of nucleic acid samples with time windows and a UAV (Unmanned Aerial Vehicle) dynamics model for multiple distribution centers is established by considering UAVs' impact cost and trajectory cost. The Golden Eagle optimization algorithm (SGDCV-GEO) based on gradient optimization and Corsi variation is proposed to solve the model by introducing gradient optimization and Corsi variation strategy in the Golden Eagle optimization algorithm. Performance evaluation by optimizing test functions, Friedman and Nemenyi test compared with Golden Jackal Optimization (GJO), Hunter-Prey Optimization (HPO), Pelican Optimization Algorithm (POA), Reptile Search Algorithm (RSA) and Golden Eagle Optimization (GEO), the convergence performance of SGDCV-GEO algorithm was demonstrated. Further, the improved RRT (Rapidly-exploring Random Trees) algorithm is used in the UAV path planning, and the pruning process and logistic chaotic mapping strategy are introduced in the path generation method. Finally, simulation experiments are conducted based on 8 hospitals and 50 randomly selected communities in the Pudong district of Shanghai, southern China. The experimental results show that the developed algorithm can effectively reduce the delivery cost and total delivery time compared with simulated annealing algorithm (SA), crow search algorithm (CSA), particle swarm algorithm (PSO), and taboo search algorithm (TS), and the developed algorithm has good uniformity, robustness, and high convergence accuracy, which can be effectively applied to the multi-UAV nucleic acid sample delivery path optimization in large cities under the influence of an epidemic environment.

Cardiac Biomarker Levels and Their Prognostic Values in COVID-19 Patients With or Without Concomitant Cardiac Disease.

The coronavirus disease 2019 (COVID-19) pandemic has become a global threat. Increases in cardiac biomarkers are common and are associated with adverse outcomes in patients with COVID-19. Although these increases are more likely to occur in cases with concomitant cardiac disease, the differences in cardiac biomarker levels between patients with and without cardiac disease and their associations with in-hospital mortality are largely unknown. A consecutive serial of laboratory-confirmed COVID-19 cases was retrospectively enrolled. Clinical characteristics, laboratory results, and outcome data were collected. The levels of cardiac biomarkers were evaluated and compared by stratifying patients according to concomitant cardiac conditions and clinical classifications. The prognostic efficacy of cardiac biomarker levels on admission was also assessed. Among the overall study population and survived patients, the cardiac biomarker levels at both the early and late stages in cardiac patients were significantly higher than those in non-cardiac patients. However, their concentrations in cardiac patients were comparable to non-cardiac ones among non-survivors. The cardiac biomarker levels at the late stage of the disease were significantly decreased compared to those at the early stage among patients who were alive. Whereas, the late-stage biomarker levels were significantly increased in patients who ultimately died. Subgroup analysis illustrated that increases in cardiac biomarkers were closely related to the severity of the disease, and were prognostic for high risks of in-hospital mortality in non-cardiac, rather than in cardiac patients. Myo and NT-proBNP, rather than Hs-TnI and CK-MB, were independently associated with in-hospital mortality in the overall population and non-cardiac patients. However, these associations were not significant among cardiac patients. In conclusion, our results helped better understand the release pattern and prognostic performance of cardiac biomarkers in patients with COVID-19. Increased levels of Myo and NT-proBNP on admission could be useful markers for early identifying high-risk patients. However, special attention must be paid when implementing the prognostic function for cardiac patients.