An optimal control theory approach for freight structure path evolution post-COVID-19 pandemic.

After the outbreak of COVID-19, the freight demand fell briefly, and as production resumed, the trucking share rate increased again, further increasing energy consumption and environmental pollution. To optimize the sudden changing freight structure, the study aims on developing an evolution model based on Markov's theory to estimate the freight structure post-COVID-19. The current study applies economic cybernetics to establish a freight structural adjustment path optimization model and solve the problem of how much freight transportation should increase each year under the premise that the total turnover of the freight industry continues to grow, and how many years it will take at least to reach a reasonable freight structure. The freight transport structure of China is used to examine the feasibility of the proposed model. The finding indicates that the development of China's freight transport structure is at an adjustment period and should enter a stable period by 2035 and the COVID-19 makes it harder to adjust the freight structure. Increasing the growth rate of the freight volume of railway and waterway transportation is the key to realizing the optimization of the freight structure, and the freight structure path optimization method can realize the rationalization of the freight structure in advance.

Speculation and clarification announcements on stock price fluctuations: Why are rumours plausible and hard to clarify?

During the COVID-19 pandemic, stock markets were fragile and sensitive to downside news regardless of whether the news was true. In China, stock rumours are increasingly rampant, affecting the sound development of the capital market. By manually gathering a sample of rumours about Chinese A-share firms, this paper studies the effects of stock market rumours and the corresponding rumour clarifications on stock returns. The study suggests that rumours rely on the information environment to persuade the market through the media effect. In terms of information disclosure, for firms that previously disclosed "negative news”, stock prices would experience abnormal drops when negative rumours appear. In terms of the media effect, rumours released by leading media cause even more significant abnormal fluctuations in stock prices. Further study shows that positive rumours significantly cause an abnormal rise in state-owned enterprises' stock prices, while negative rumours significantly cause an abnormal decline in small and medium enterprise board (SME) and growth enterprise market board (GEM) stock prices. From the perspective of the effect of clarification announcements in restraining stock price fluctuations, clear and timely clarifications are recommended.

Patient allocation method in major epidemics under the situation of hierarchical diagnosis and treatment.

OBJECTIVES: Patients are classified according to the severity of their condition and graded according to the diagnosis and treatment capacity of medical institutions. This study aims to correctly assign patients to medical institutions for treatment and develop patient allocation and medical resource expansion schemes among hospitals in the medical network. METHODS: Illness severity, hospital level, allocation matching benefit, distance traveled, and emergency medical resource fairness were considered. A multi-objective planning method was used to construct a patient allocation model during major epidemics. A simulation study was carried out in two scenarios to test the proposed method. RESULTS: (1) The single-objective model obtains an unbalanced solution in contrast to the multi-objective model. The proposed model considers multi-objective problems and balances the degree of patient allocation matching, distance traveled, and fairness. (2) The non-hierarchical model has crowded resources, and the hierarchical model assigns patients to matched medical institutions. (3) In the "demand exceeds supply" situation, the patient allocation model identified additional resources needed by each hospital. CONCLUSION: Results verify the maneuverability and effectiveness of the proposed model. It can generate schemes for specific patient allocation and medical resource amplification and can serve as a quantitative decision-making tool in the context of major epidemics.

The Allocation Method for Personal Protective Equipment in the Emerging Infectious Disease Environment.

The COVID-19 pandemic gives humankind a lesson that the outbreak of an emerging infectious disease (EID) is sudden and uncertain. Accurately mastering its dynamics and putting forward an efficient and fair humanitarian logistics plan for personal protective equipment (PPE) remains difficult. This study examines the decision making for humanitarian logistics to answer the question that how to coordinate fairness and efficiency when facing supply-demand imbalance during humanitarian logistics planning in an EID environment. The main contributions include two aspects: (1) The victims' losses in terms of fairness and efficiency in receiving PPE are jointly explored by evaluating their bearing capacity evolution, and then a novel loss function is built to search for a reasonable compromise between fairness and efficiency. (2) A multi-objective optimization model is built, which is solved using the combined use of goal programming approach and improved branch and bound method. Finally, the practicability of the proposed model is tested by an EID case study. The potential advantages of the proposed model and improved approach are discussed.

Forecasting China's crude oil futures volatility: New evidence from the MIDAS-RV model and COVID-19 pandemic.

In this study, we focus on the role of jumps and leverage in predicting the realized volatility (RV) of China's crude oil futures. We employ a standard mixed data sampling (MIDAS) modeling framework. First, the in-sample results indicate that the jump and leverage effects are useful in predicting the RV of Chinese crude oil futures. Second, the out-of-sample results suggest that jump has very significant predictive power at the one-day-ahead horizon while the leverage effect contains more useful information for long-term predictions. Moreover, our results are supported by a number of robustness checks. Finally, we find new evidence that the prediction model that considers the leverage effect has the best predictive power during the COVID-19 pandemic.

The host immune response of a discharged COVID-19 patient with twice reemergence of SARS-CoV-2: a case report.

BACKGROUND: The coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global pandemic. There have been reports that long-term SARS-CoV-2 RNA shedding and re-infection of COVID-19 patients existed. However, the specific mechanism, diagnosis, and treatment of COVID-19 are still unclarified. CASE PRESENTATION: In this case, we reported a 64-year-old patient who had a long-term course of COVID-19 for 174 days with two retests of SARS-CoV-2 RNA positive after discharging from the hospital. The patient's serum immunoglobulin G (IgG) of SARS-CoV-2 tested positive after the initial infection. And during treatment, the CD4 + T cell count and ratio to peripheral blood mononuclear cell (PBMC) were in dynamic change. CONCLUSIONS: Our results suggested that the host immune system responded with IgG production after SARS-CoV-2 infection, but was not protective enough for the patient. The reemergence of SARS-CoV-2 could be related to the cell count and proportion of CD4 + T cells in PBMC. And the increase of CD4 + T cells after treatment may help to clear the virus.

The Public Servants' Response When Facing Pandemic: The Role of Public Service Motivation, Accountability Pressure, and Emergency Response Capacity.

(1) Background: Public servants are regarded as guardians of the public interest, and their pandemic response played a vital role in controlling the spread of the epidemic. However, there is limited knowledge of the factors that influence public servants' response (PSR) when facing pandemic prevention and control tasks. (2) Methods: Based on the theory of planned behavior (TPB), models were constructed and a regression method was employed with Chinese civil servant data to investigate how PSR is influenced by public service motivation (PSM), accountability pressure (AP), and emergency response capacity (ERC). (3) Results and discussion: PSM, AP, and ERC all have a positive effect on PSR, with AP having the greatest influence, followed by PSM and ERC. The effects of PSM, AP, and ERC on PSR have group heterogeneity, which had little effect on civil servants with very low levels of PSR and the greatest impact on civil servants with medium-level PSR. Job categories of civil servants also are a factor related to PSR; PSM and AP have the strongest effects on civil servants in professional technology, and ERC has the greatest influence on administrative law enforcement. Moreover, gender, administrative level, and leadership positions also have an impact on PSR. (4) Conclusions: Based on the factors of PSR, we found at least three important aspects that governments need to consider in encouraging PSR when facing a pandemic.

Development and validation of a simple-to-use nomogram to predict the deterioration and survival of patients with COVID-19.

BACKGROUND: COVID-19 pandemic has forced physicians to quickly determine the patient's condition and choose treatment strategies. This study aimed to build and validate a simple tool that can quickly predict the deterioration and survival of COVID-19 patients. METHODS: A total of 351 COVID-19 patients admitted to the Third People's Hospital of Yichang between 9 January to 25 March 2020 were retrospectively analyzed. Patients were randomly grouped into training (n = 246) or a validation (n = 105) dataset. Risk factors associated with deterioration were identified using univariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression. The factors were then incorporated into the nomogram. Kaplan-Meier analysis was used to compare the survival of patients between the low- and high-risk groups divided by the cut-off point. RESULTS: The least absolute shrinkage and selection operator (LASSO) regression was used to construct the nomogram via four parameters (white blood cells, C-reactive protein, lymphocyte≥0.8 × 109/L, and lactate dehydrogenase ≥400 U/L). The nomogram showed good discriminative performance with the area under the receiver operating characteristic (AUROC) of 0.945 (95% confidence interval: 0.91-0.98), and good calibration (P = 0.539). Besides, the nomogram showed good discrimination performance and good calibration in the validation and total cohorts (AUROC = 0.979 and AUROC = 0.954, respectively). Decision curve analysis demonstrated that the model had clinical application value. Kaplan-Meier analysis illustrated that low-risk patients had a significantly higher 8-week survival rate than those in the high-risk group (100% vs 71.41% and P < 0.0001). CONCLUSION: A simple-to-use nomogram with excellent performance in predicting deterioration risk and survival of COVID-19 patients was developed and validated. However, it is necessary to verify this nomogram using a large-scale multicenter study.

A new scheduling method based on sequential time windows developed to distribute first-aid medicine for emergency logistics following an earthquake.

After an earthquake, affected areas have insufficient medicinal supplies, thereby necessitating substantial distribution of first-aid medicine from other supply centers. To make a proper distribution schedule, we considered the timing of supply and demand. In the present study, a "sequential time window" is used to describe the time to generate of supply and demand and the time of supply delivery. Then, considering the sequential time window, we proposed two multiobjective scheduling models with the consideration of demand uncertainty; two multiobjective stochastic programming models were also proposed to solve the scheduling models. Moreover, this paper describes a simulation that was performed based on a first-aid medicine distribution problem during a Wenchuan earthquake response. The simulation results show that the methodologies proposed in this paper provide effective schedules for the distribution of first-aid medicine. The developed distribution schedule enables some supplies in the former time windows to be used in latter time windows. This schedule increases the utility of limited stocks and avoids the risk that all the supplies are used in the short-term, leaving no supplies for long-term use.

Role of Traditional Chinese Medicine in the Management of Viral Pneumonia.

Viral pneumonia is one kind of acute respiratory tract infection caused by the virus. There have been many outbreaks of viral pneumonia with high contagiousness and mortality both in China and abroad, such as the great influenza in 1918, the severe acute respiratory syndrome (SARS) coronavirus in 2003, the Influenza A (H1N1) virus in 2009, and the Middle East Respiratory Syndrome coronavirus (MERS-CoV) in 2012 and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2019. These outbreaks and/or pandemic have significant impact on human life, social behaviors, and economic development. Moreover, no specific drug has been developed for these viruses. Traditional Chinese medicine (TCM) plays an important role in the treatment of viral pneumonia during these outbreaks especially in SARS and SARS-CoV-2 because studies suggest that TCM formulations may target several aspects of the disease and may have lesser side effects than manufactured pharmaceuticals. In recent years, a lot of clinicians and researchers have made a series of in-depth explorations and investigations on the treatment of viral pneumonia with TCM, which have understood TCM therapeutic mechanisms more specifically and clearly. But critical analysis of this research in addition to further studies are needed to assess the potential of TCM in the treatment of viral pneumonia.

Guideline-Based Chinese Herbal Medicine Treatment Plus Standard Care for Severe Coronavirus Disease 2019 (G-CHAMPS): Evidence From China.

Background: In January, national guidelines were developed and recommended for use throughout China to fight coronavirus disease 2019 (COVID-19). Chinese herbal medicine (CHM) was also included as part of the treatment plans at various stages of COVID-19. Methods: We conducted a pilot randomized, controlled trial in patients with severe COVID-19 in Wuhan, China. Eligible adult patients were randomly assigned in a 2:1 ratio to receive either CHM plus standard care or standard care alone for 7 days. The primary outcome was the change in the disease severity category of COVID-19 after treatment. Results: Between Jan 31, 2020, and Feb 19, 2020, 42 out of 100 screened patients were included in the trial: 28 in the CHM plus standard care group and 14 in the standard care alone group. Among 42 participants who were randomized (mean [SD] age 60.43 years [12.69 years]), 21 (21/42, 50%) were aged ≥65 years, 35 (35/42, 83%) were women, and 42 (42/42, 100%) had data available for the primary outcome. For the primary outcome, one patient from each group died during treatment; the odds of a shift toward death was lower in the CHM plus group than in the standard care alone group (common OR 0.59, 95% CI 0.148-2.352, P = 0.454). Three (two from the CHM plus group and one from the standard care alone group) patients progressed from severe to critical illness. After treatment, mild, moderate, and severe COVID-19 disease accounted for 17.86% (5/28) vs. 14.29% (2/28), 71.43% (20/28) vs. 64.29% (9/28), and 0% (0) vs. 7.14% (1/28) of the patients treated with CHM plus standard care vs. standard care alone. Conclusions: For the first time, the G-CHAMPS trial provided valuable information for the national guideline-based CHM treatment of hospitalized patients with severe COVID-19. The effects of CHM in COVID-19 may be clinically important and warrant further consideration and studies. Clinical Trial Registration: http://www.chictr.org.cn/index.aspx. Uniqueidentifier: ChiCTR2000029418.