Collaborative Determination Method of Metro Train Plan Adjustment and Passenger Flow Control under the Impact of COVID-19

Aiming at the problem of metro operation and passenger transport organization under the impact of the novel coronavirus (COVID-19), a collaborative determination method of train planning and passenger flow control is proposed to reduce the train load rate in each section and decrease the risk of spreading COVID-19. The Fisher optimal division method is used to determine reasonable passenger flow control periods, and based on this, different flow control rates are adopted for each control period to reduce the difficulty of implementing flow control at stations. According to the actual operation and passenger flow changes, a mathematical optimization model is established. Epidemic prevention risk values (EPRVs) are defined based on the standing density criteria for trains to measure travel safety. The optimization objectives of the model are to minimize the EPRV of trains in each interval, the passenger waiting time and the operating cost of the corporation. The decision variables are the number of running trains during the study period and the flow control rate at each station. The original model is transformed into a single-objective model by the linear weighting of the target, and the model is solved by designing a particle swarm optimization and genetic algorithm (PSO-GA). The validity of the method and the model is verified by actual metro line data. The results of the case study show that when a line is in the moderate-risk area of COVID-19, two more trains should be added to the full-length and short-turn routes after optimization. Combined with the flow control measures for large passenger flow stations, the maximum train load rate is reduced by 35.18%, and the load rate of each section of trains is less than 70%, which meets the requirements of COVID-19 prevention and control. The method can provide a theoretical basis for related research on ensuring the safety of metro operation during COVID-19.

A multi-parameter-level model for simulating future mortality scenarios with COVID-alike effects

There has been a growing interest among pension plan sponsors in envisioning how the mortality experience of their active and deferred members may turn out to be if a pandemic similar to the COVID-19 occurs in the future. To address their needs, we propose in this paper a stochastic model for simulating future mortality scenarios with COVID-alike effects. The proposed model encompasses three parameter levels. The first level includes parameters that capture the long-term pattern of mortality, whereas the second level contains parameters that gauge the excess age-specific mortality due to COVID-19. Parameters in the first and second levels are estimated using penalised quasi-likelihood maximisation method which was proposed for generalised linear mixed models. Finally, the third level includes parameters that draw on expert opinions concerning, for example, how likely a COVID alike pandemic will occur in the future. We illustrate our proposed model with data from the United States and a range of expert opinions.

Environmental geotechnics: Challenges and opportunities in the post-Covid-19 world

The outbreak of the coronavirus disease 2019 (Covid-19) pandemic not only has created a health crisis across the world but is also expected to impact negatively the global economy and societies at a scale that is maybe larger than that of the 2008 financial crisis. Simultaneously, it has inevitably exerted many negative consequences on the geoenvironment on which human beings depend. The current paper articulates the role of environmental geotechnics in elucidating and mitigating the effects of the current pandemic. It is the belief of all authors that the Covid-19 pandemic presents not only significant challenges but also opportunities for the development of the environmental geotechnics field. This discipline should make full use of geoenvironmental researchers' and engineers' professional skills and expertise to look for development opportunities from this crisis, to highlight the irreplaceable position of the discipline in the global fight against pandemics and to contribute to the health and prosperity of communities, to serve humankind better. In order to reach this goal while taking into account the specificity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the uncertainty of its environmental effects, it is believed that more emphasis should be placed on the following research directions: pathogen-soil interactions;isolation and remediation technologies for pathogen-contaminated sites;new materials for pathogen-contaminated soil;recycling and safe disposal of medical wastes;quantification of uncertainty in geoenvironmental and epidemiological problems;emerging technologies and adaptation strategies in civil, geotechnical and geoenvironmental infrastructures;pandemic-induced environmental risk management;and modelling of pathogen transport and fate in geoenvironment, among others. Moreover, Covid-19 has made it clear to the environmental geotechnics community the importance of urgent international co-operation and of multidisciplinary research actions that must extend to a broad range of scientific fields, including medical and public health disciplines, in order to meet the complexities posed by the Covid-19 pandemic. © 2021 ICE Publishing: All rights reserved.

Laboratory findings and a combined multifactorial approach to predict death in critically ill patients with COVID-19: a retrospective study.

To describe the laboratory findings of cases of death with coronavirus disease 2019 (COVID-19) and to establish a scoring system for predicting death, we conducted this single-centre, retrospective, observational study including 336 adult patients (≥18 years old) with severe or critically ill COVID-19 admitted in two wards of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology in Wuhan, who had definite outcomes (death or discharge) between 1 February 2020 and 13 March 2020. Single variable and multivariable logistic regression analyses were performed to identify mortality-related factors. We combined multiple factors to predict mortality, which was validated by receiver operating characteristic curves. As a result, in a total of 336 patients, 34 (10.1%) patients died during hospitalisation. Through multivariable logistic regression, we found that decreased lymphocyte ratio (Lymr, %) (odds ratio, OR 0.574, P < 0.001), elevated blood urea nitrogen (BUN) (OR 1.513, P = 0.009), and raised D-dimer (DD) (OR 1.334, P = 0.002) at admission were closely related to death. The combined prediction model was developed by these factors with a sensitivity of 100.0% and specificity of 97.2%. In conclusion, decreased Lymr, elevated BUN, and raised DD were found to be in association with death outcomes in critically ill patients with COVID-19. A scoring system was developed to predict the clinical outcome of these patients.