Risk assessment of port congestion risk during the COVID-19 pandemic

With the COVID-19 outbreak hitting the world, the frequency and severity of port congestion caused by various factors are increasing, challenging the stability of international supply chains. Thus, it is necessary to conduct an in-depth study on congestion risks to reduce their adverse impacts on congestion. Although traditional criticality analysis techniques may be capable of ranking port congestion risk in common scenarios, new risk analysis methods are urgently required to tackle uncertainty along with the COVID-19 pandemic. This paper develops a methodology designed for the identification and prioritization of port congestion risk during the pandemic. First, a novel congestion risk assessment model is established by extending the risk prioritization index (RPI) suggested by failure mode and effects analysis (FMEA). Next, the combination of fuzzy Bayesian reasoning, AHP and the variation coefficient method is incorporated into the model in a complementary way to facilitate the treatment of uncertainty and quantitative analysis of the congestion under the different influence of risk factors in ports. Finally, the mode introduces a set of risk utility values for calculating the RPI for prioritization. A real case study and a sensitivity analysis were carried out to illustrate and validate the proposed model. The results proved that the applied method is feasible and functional. In the illustrative example, the top three risk factors are "Interruption of railways/barges services", "Skilled labor shortage" and "Shortage of truck-drivers/drayage truck". The findings obtained from this paper could provide useful insights for risk prevention and mitigation.

Infection with wild-type SARS-CoV-2 elicits broadly neutralizing and protective antibodies against omicron subvariants.

The omicron variants of SARS-CoV-2 have substantial ability to escape infection- and vaccine-elicited antibody immunity. Here, we investigated the extent of such escape in nine convalescent patients infected with the wild-type SARS-CoV-2 during the first wave of the pandemic. Among the total of 476 monoclonal antibodies (mAbs) isolated from peripheral memory B cells, we identified seven mAbs with broad neutralizing activity to all variants tested, including various omicron subvariants. Biochemical and structural analysis indicated the majority of these mAbs bound to the receptor-binding domain, mimicked the receptor ACE2 and were able to accommodate or inadvertently improve recognition of omicron substitutions. Passive delivery of representative antibodies protected K18-hACE2 mice from infection with omicron and beta SARS-CoV-2. A deeper understanding of how the memory B cells that produce these antibodies could be selectively boosted or recalled can augment antibody immunity against SARS-CoV-2 variants.

Omicron variants escape the persistent SARS-CoV-2-specific antibody response in 2-year COVID-19 convalescents regardless of vaccination.

With the ongoing COVID-19 pandemic and the emergence of various SARS-CoV-2 variants, a comprehensive evaluation of long-term efficacy of antibody response in convalescent individuals is urgently needed. Several longitudinal studies had reported the antibody dynamics after SARS-CoV-2 acute infection, but the follow-up was mostly limited to 1 year or 18 months at the maximum. In this study, we investigated the durability, potency, and susceptibility to immune evasion of SARS-CoV-2-specific antibody in COVID-19 convalescents for 2 years after discharge. These results showed the persistent antibody-dependent immunity could protect against the WT and Delta variant to some extent. However, the Omicron variants (BA.1, BA.2, and BA.4/5) largely escaped this preexisting immunity in recovered individuals. Furthermore, we revealed that inactivated vaccines (BBIBP-CorV, CoronaVac, or KCONVAC) could improve the plasma neutralization and help to maintain the broadly neutralizing antibodies at a certain level. Notably, with the time-dependent decline of antibody, 1-dose or 2-dose vaccination strategy seemed not to be enough to provide immune protection against the emerging variants. Overall, these results facilitated our understanding of SARS-CoV-2-induced antibody memory, contributing to the development of immunization strategy against SARS-CoV-2 variants for such a large number of COVID-19 survivors.

Antigenic sin of wild-type SARS-CoV-2 vaccine shapes poor cross-neutralization of BA.4/5/2.75 subvariants in BA.2 breakthrough infections.

With declining SARS-CoV-2-specific antibody titers and increasing numbers of spike mutations, the ongoing emergence of Omicron subvariants causes serious challenges to current vaccination strategies. BA.2 breakthrough infections have occurred in people who have received the wild-type vaccines, including mRNA, inactivated, or recombinant protein vaccines. Here, we evaluate the antibody evasion of recently emerged subvariants BA.4/5 and BA.2.75 in two inactivated vaccine-immunized cohorts with BA.2 breakthrough infections. Compared with the neutralizing antibody titers against BA.2, marked reductions are observed against BA.2.75 in both 2-dose and 3-dose vaccine groups. In addition, although BA.2 breakthrough infections induce a certain cross-neutralization capacity against later Omicron subvariants, the original antigenic sin phenomenon largely limits the improvement of variant-specific antibody response. These findings suggest that BA.2 breakthrough infections seem unable to provide sufficient antibody protection against later subvariants such as BA.2.75 in the current immunization background with wild-type vaccines.

Reactive-diffusion epidemic model on human mobility networks: Analysis and applications to COVID-19 in China.

The complex dynamics of human mobility, combined with sporadic cases of local outbreaks, make assessing the impact of large-scale social distancing on COVID-19 propagation in China a challenge. In this paper, with the travel big dataset supported by Baidu migration platform, we develop a reactive-diffusion epidemic model on human mobility networks to characterize the spatio-temporal propagation of COVID-19, and a novel time-dependent function is incorporated into the model to describe the effects of human intervention. By applying the system control theory, we discuss both constant and time-varying threshold behavior of proposed model. In the context of population mobility-mediated epidemics in China, we explore the transmission patterns of COVID-19 in city clusters. The results suggest that human intervention significantly inhibits the high correlation between population mobility and infection cases. Furthermore, by simulating different population flow scenarios, we reveal spatial diffusion phenomenon of cases from cities with high infection density to cities with low infection density. Finally, our model exhibits acceptable prediction performance using actual case data. The localized analytical results verify the ability of the PDE model to correctly describe the epidemic propagation and provide new insights for controlling the spread of COVID-19.

Optimization, validation and analysis of a 2019-nCoV nucleic acid detection kit. (Special Issue: Spatio-temporal dynamics analysis, risk assessment and emergency management for COVID-19 epidemic.) [Chinese]

Objective: To optimize the sensitivity and specificity of a 2019-nCoV nucleic acid detection kit, so as to improve the positive detection rate and provide guidance for clinical use by comparison with different kits.

Accounting Transparency, Fear Sentiment and the COVID-19 Epidemic: For Public Health Security and the Construction of an Early Warning System

A central issue of public health security and the construction of an early warning system is to establish a set of responsibility-oriented incentives and restraint mechanisms. This is closely related to the accounting transparency of the institutional environment and the fear sentiment of the individual's predicament. This study analyses the relationship between accounting transparency, fear sentiment, and COVID-19 through a VAR model analysis. The results show a significant and negative relationship between accounting transparency and daily new COVID-19 patients. In particular, accounting transparency has a negative impact on the increase in the number of people infected with a two-period lag, while the three-period lag in the number of new epidemics has a negative impact on accounting information. Second, accounting transparency has a positive impact on the increase in the search volume on COVID-19 within a three-period lag. After the three-period lag, the number of new epidemics has a positive impact on accounting information. Third, an increase in fear sentiment can be driven by the fear of COVID-19. Fourth, in the public health early warning system, according to the abovementioned time characteristics, the system arranges the emotional counseling, early warning incentives, and institutional constraints to be dealt with in the first 4 days. In addition, in the early warning target-oriented system setting, the parallel system helps to improve the early warning efficiency.

Identification and application of a pair of noncompeting monoclonal antibodies broadly binding to the nucleocapsid proteins of SARS-CoV-2 variants including Omicron.

The SARS-CoV-2 nucleocapsid protein (NP) is an important indicator for the virus infection, highlighting the crucial role of NP-specific monoclonal antibodies (mAbs) used in multiple biochemical assays and clinical diagnosis for detecting the NP antigen. Here, we reported a pair of noncompeting human NP-specific mAbs, named P301-F7 and P301-H5, targeting two distinct linear epitopes on SARS-CoV-2 or SARS-CoV. We evaluated the application of P301-F7 in the analysis of enzyme linked immunosorbent assay, western blot, flow cytometry, immunofluorescence, and focus reduction neutralization test. We for the first time report a broad mAb effectively recognizing various live viruses of SARS-CoV-2 variants including Alpha, Beta, Delta, and Omicron, indicating a wide range of application prospects.

Increased resistance of SARS-CoV-2 Lambda variant to antibody neutralization.

A recently identified SARS-CoV-2 variant, Lambda, has spread to many countries around the world. Here, we measured and evaluated the reduced sensitivity of Lambda variant to the neutralization by plasma polyclonal antibodies elicited by the natural SARS-CoV-2 infection and inactivated vaccine. The combination of two substitutions appearing in the RBD of spike protein (L452Q and F490S) resulted in noticeably reduced neutralization against Lambda variant. F490S contributed more than L452Q in affecting the neutralization. In addition, the neutralization test with 12 published nAbs binding to RBD of SARS-CoV-2 with defined structures suggested that Lambda variant resisted the neutralization by some antibodies from Class 2 and Class 3. Overall, these results suggest that pre-existing antibody neutralization established by natural infection from non-Lambda variants or immunization could be significantly decreased, re-emphasizing the importance of ongoing viral mutation monitoring.

Psychological Behavior, Work Stress, and Social Support of Frontline Nurses During the COVID-19 Pandemic.

Using a cluster sampling method, 248 nurses from frontline departments of three large general hospitals in Sichuan Province, China, were selected as participants in the current study. Risk perception and coping methods during the coronavirus disease 2019 (COVID-19) pandemic, negative emotions and psychological workload, and awareness of social support among nurses were studied. Negative emotions among nurses increased significantly, with fear being the most common (73.4%, n = 182). Psychological workload of nurses in close contact with patients with COVID-19 was higher than that of nurses in other positions (p < 0.05). Family was the major source of social support. Social support was negatively associated with depression (-0.206, p < 0.05), obsessive-compulsiveness/anxiety (-0.185, p < 0.05), and hypochondriasis (-0.234, p < 0.05). Psychological workload of nurses was positively correlated with depression (0.251, p < 0.05), neurasthenia (0.242, p < 0.05), and obsessive-compulsiveness/anxiety (0.231, p < 0.05). Nursing staff in frontline departments encountered psychological workload burdens to varying degrees during the COVID-19 pandemic. Thus, it is important to implement and strengthen psychological counseling for nurses in close contact positions, and to seek family and social support for nurses. [Journal of Psychosocial Nursing and Mental Health Services, 60(10), 21-27.].

Diagnostic Policies Optimization for Chronic Diseases Based on POMDP Model.

During the process of disease diagnosis, overdiagnosis can lead to potential health loss and unnecessary anxiety for patients as well as increased medical costs, while underdiagnosis can result in patients not being treated on time. To deal with these problems, we construct a partially observable Markov decision process (POMDP) model of chronic diseases to study optimal diagnostic policies, which takes into account individual characteristics of patients. The objective of our model is to maximize a patient's total expected quality-adjusted life years (QALYs). We also derive some structural properties, including the existence of the diagnostic threshold and the optimal diagnosis age for chronic diseases. The resulting optimization is applied to the management of coronary heart disease (CHD). Based on clinical data, we validate our model, demonstrate how the quantitative tool can provide actionable insights for physicians and decision makers in health-related fields, and compare optimal policies with actual clinical decisions. The results indicate that the diagnostic threshold first decreases and then increases as the patient's age increases, which contradicts the intuitive non-decreasing thresholds. Moreover, diagnostic thresholds were higher for women than for men, especially at younger ages.

The Reduction of Credit Risk in the Health Care Industry in China: Are We Returning to the Pre-COVID-19 Era?

This study aims to evaluate the changes in the credit risk of the health care industry in China due to the COVID-19 epidemic by the modified KMV (named by Kealhofer, Mcquown, and Vasicek) model to calculate the default distances. We observe that the overall default distance mainly first decreased and then increased before and after the COVID-19 epidemic control in China; after the epidemic was controlled, the overall credit risk was reduced by 22.8%. Specifically, as shown in subdivided industries, health care equipment and health care facilities have larger credit risk fluctuations, while health care suppliers, health care distributors, and health care services have smaller fluctuations. These results can contribute to our understanding of why the COVID-19 epidemic in China could be controlled earlier, and software facilities are more important than hardware facilities in public health safety. Our methodological innovation is to use the GARCH (generalized autoregressive conditional heteroskedasticity) model and threshold regression model to modify the important parameters of the KMV model. This method has good accuracy in the Chinese environment.

The Transient IFN Response and the Delay of Adaptive Immunity Feature the Severity of COVID-19.

COVID-19 patients show heterogeneous and dynamic immune features which determine the clinical outcome. Here, we built a single-cell RNA sequencing (scRNA-seq) dataset for dissecting these complicated immune responses through a longitudinal survey of COVID-19 patients with various categories of outcomes. The data reveals a highly fluctuating peripheral immune landscape in severe COVID-19, whereas the one in asymptomatic/mild COVID-19 is relatively steady. Then, the perturbed immune landscape in peripheral blood returned to normal state in those recovered from severe COVID-19. Importantly, the imbalance of the excessively strong innate immune response and delayed adaptive immunity in the early stage of viral infection accelerates the progression of the disease, indicated by a transient strong IFN response and weak T/B-cell specific response. The proportion of abnormal monocytes appeared early and rose further throughout the severe disease. Our data indicate that a dynamic immune landscape is associated with the progression and recovery of severe COVID-19, and have provided multiple immune biomarkers for early warning of severe COVID-19.

Dysregulated hematopoiesis in bone marrow marks severe COVID-19.

Severe coronavirus disease 2019 (COVID-19) is often indicated by lymphopenia and increased myelopoiesis; however, the underlying mechanism is still unclear, especially the alteration of hematopoiesis. It is important to explore to what extent and how hematopoietic stem cells contribute to the impairment of peripheral lymphoid and myeloid compartments in COVID-19 patients. In this study, we used single-cell RNA sequencing to assess bone marrow mononuclear cells from COVID-19 patients with peripheral blood mononuclear cells as control. The results showed that the hematopoietic stem cells in these patients were mainly in the G1 phase and prone to apoptosis, with immune activation and anti-viral responses. Importantly, a significant accumulation of immature myeloid progenitors and a dramatic reduction of lymphoid progenitors in severe cases were identified, along with the up-regulation of transcription factors (such as SPI1, LMO4, ETS2, FLI1, and GATA2) that are important for the hematopoietic stem cell or multipotent progenitor to differentiate into downstream progenitors. Our results indicate a dysregulated hematopoiesis in patients with severe COVID-19.

Using Fuzzy AHP-PROMETHEE for Market Risk Assessment of New-Build River Cruises on the Yangtze River

Cruise tourism on the Yangtze River Basin has developed rapidly in recent years. However, it is still facing such challenges as homogenization of itinerary and shore attraction arrangement, as well as aging cruise ships and simplification of service facilities, while it is also difficult to satisfy hierarchical and personalized tourist needs. To change such circumstances, new-build river cruise ships are inevitable. Complexity of market supply and demand environment, together with variability of tourist preferences, have increased market uncertainties of new-build cruise products. This study aims to assess market risks of new-build river cruise ships first by identifying risk factors, from the perspective of supply and demand under the actual conditions of the Yangtze River cruise market, then by evaluating potential impacts, caused by risk factors based on multi-criteria decision-making considerations. Fuzzy AHP-PROMETHEE was employed to prioritize the risk factors. The results reveal that among the most significant market risk factors are the following, in sequence: backwardness of support facilities;sudden security crisis;homogenization of cruise products;simplification of tourism route design;and inadequate management of the tourism market. Such findings will provide beneficial insights for strategic and sustainable development of river cruises on the Yangtze River.

The Influence of Commodity Presentation Mode on Online Shopping Decision Preference Induced by the Serial Position Effect

The information cluster that supports the final decision in a decision task is usually presented as a series of information. According to the serial position effect, the decision result is easily affected by the presentation order of the information. In this study, we seek to investigate how the presentation mode of commodities and the informativeness on a shopping website will influence online shopping decisions. To this end, we constructed two experiments via a virtual online shopping environment. The first experiment suggests that the serial position effect can induce human computer interaction decision-making bias, and user decision-making results in separate evaluation mode are more prone to the recency effect, whereas user decision-making results in joint evaluation mode are more prone to the primacy effect. The second experiment confirms the influence of explicit and implicit details of information on the decision bias of the human computer interaction caused by the serial position effect. The results of the research will be better applied to the design and development of shopping websites or further applied to the interactive design of complex information systems to alleviate user decision-making biases and induce users to make more rational decisions.