Two-Stage Multi-Objective Stochastic Model on Patient Transfer and Relief Distribution in Lockdown Area of COVID-19.

The outbreak of an epidemic disease may cause a large number of infections and a slightly higher death rate. In response to epidemic disease, both patient transfer and relief distribution are significant to reduce corresponding damage. This study proposes a two-stage multi-objective stochastic model (TMS-PTRD) considering pre-pandemic preparedness measures and post-pandemic relief operations. The proposed model considers the following four objectives: the total number of untreated infected patients, the total transfer time, the overall cost, and the equity distribution of relief supplies. Before an outbreak, the locations of temporary relief distribution centers (TRDCs) and the inventory levels of established TRDCs should be determined. After an outbreak, the locations of temporary hospitals (THs), the locations of designated hospitals (DHs), the transfer plans for patients, and the relief distribution should be determined. To solve the TMS-PTRD model, we address an improved preference-inspired co-evolutionary algorithm named the PICEA-g-AKNN algorithm, which is embedded with a novel similarity distance and three different tailored evolutionary strategies. A real-world case study of Hunan of China and 18 test instances are randomly generated to evaluate the TMS-PTRD model. The finding shows that the PICEA-g-AKNN algorithm is better than some most widely used multi-objective algorithms.

Suicidal ideation in the general population in China after the COVID-19 pandemic was initially controlled.

BACKGROUND: The COVID-19 pandemic increases the risk of psychological problems including suicidal ideation (SI) in the general population. In this study, we investigated the risk factors of SI after the COVID-19 pandemic was initially controlled in China. METHODS: We conducted an online questionnaire via JD Health APP in China in June 2020. Demographic data, feelings and experiences related to the COVID-19 pandemic and psychological problems were collected. The participants (n = 14,690) were divided into the non-SI and SI groups. A binary logistic regression analysis was used to examine the correlates of SI. RESULTS: Nine percent of the participants (1328/14690) reported SI. The regression analysis showed that SI was positively associated with ethnic minority (OR = 1.42 [1.08-1.85]), age (e.g. 18-30 years: OR = 2.31 [1.67-3.20]), having history of mental disorders (OR = 2.75 [2.27-3.35]), daily life disturbance due to health problems (OR = 1.67 [1.38-2.01]), being around someone with the COVID-19 (OR = 1.58 [1.30-1.91]), being uncertain about effective disease control (OR = 1.23 [1.03-1.46]), and having depressive symptoms (OR = 4.40 [3.59-5.39]), insomnia symptoms (OR = 2.49 [2.13-2.90]) or psychological distress (OR = 1.87 [1.59-2.18]). LIMITATIONS: The main limitation is that the cross-sectional design of this study could not allow us to further explore the causality of SI. CONCLUSIONS: The prevalence of SI was relatively high in general population after the COVID-19 pandemic was initially controlled in China. SI should be monitored continually after the COVID-19 pandemic.

Government Supports, Digital Capability, and Organizational Resilience Capacity during COVID-19: The Moderation Role of Organizational Unlearning

This paper provides an investigation into how different types of government supports can be used to enhance organizational resilience capacity during the COVID-19 pandemic. Based on resource orchestration theory, this study examines the effects of direct government support and indirect government support on organizational resilience capacity, the mediation role of digital capability, and the moderation effects of organizational unlearning. The empirical results from 205 Chinese firms show that direct government support and indirect government support have positive effects on organizational resilience capacity, which were mediated by digital capability. In addition, organizational unlearning positively and negatively moderates the positive relationship between direct government support, indirect government support and digital capability. Our theoretical discussion and empirical results contribute to the literature related to organizational resilience, digital capability, government support, and organizational unlearning.

Cellular metabolic basis of altered immunity in the lungs of patients with COVID-19.

Metabolic pathways drive cellular behavior. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection causes lung tissue damage directly by targeting cells or indirectly by producing inflammatory cytokines. However, whether functional alterations are related to metabolic changes in lung cells after SARS-CoV-2 infection remains unknown. Here, we analyzed the lung single-nucleus RNA-sequencing (snRNA-seq) data of several deceased COVID-19 patients and focused on changes in transcripts associated with cellular metabolism. We observed upregulated glycolysis and oxidative phosphorylation in alveolar type 2 progenitor cells, which may block alveolar epithelial differentiation and surfactant secretion. Elevated inositol phosphate metabolism in airway progenitor cells may promote neutrophil infiltration and damage the lung barrier. Further, multiple metabolic alterations in the airway goblet cells are associated with impaired muco-ciliary clearance. Increased glycolysis, oxidative phosphorylation, and inositol phosphate metabolism not only enhance macrophage activation but also contribute to SARS-CoV-2 induced lung injury. The cytotoxicity of natural killer cells and CD8+ T cells may be enhanced by glycerolipid and inositol phosphate metabolism. Glycolytic activation in fibroblasts is related to myofibroblast differentiation and fibrogenesis. Glycolysis, oxidative phosphorylation, and glutathione metabolism may also boost the aging, apoptosis and proliferation of vascular smooth muscle cells, resulting in pulmonary arterial hypertension. In conclusion, this preliminary study revealed a possible cellular metabolic basis for the altered innate immunity, adaptive immunity, and niche cell function in the lung after SARS-CoV-2 infection. Therefore, patients with COVID-19 may benefit from therapeutic strategies targeting cellular metabolism in future.

Relationships between the changes in sleep patterns and sleep quality among Chinese people during the 2019 coronavirus disease outbreak.

OBJECTIVE: Rapidly increasing numbers of confirmed cases and deaths during the 2019 coronavirus disease outbreak (COVID-19) resulted in widespread psychological problems in the Chinese population. The purpose of this study was to investigate the sleep quality and changes in sleep patterns before and during the outbreak in the general population in China and to determine factors related to sleep quality. METHODS: This cross-sectional study was conducted using an online questionnaire from 20 February to 29 February 2020 in China. Socio-demographic data, self-designed COVID-19-related characteristics, sleep patterns, and Pittsburgh Sleep Quality Index (PSQI) scores were obtained. Single factor analysis and multivariate binary logistic regression analysis were used. RESULTS: A total of 1897 individuals were included in our study, and 30.0% of participants reported suffering poor sleep quality (PSQI≥8). Logistic regression analysis found that the factors related to sleep quality included poor physical health (OR = 3.382, p < 0.001), respiratory disease (OR = 1.629, p = 0.008), other diseases (OR = 2.504, p = 0.012), suspected case of COVID-19 in the same community (OR = 1.928, p = 0.002), confirmed case of COVID-19 in the same community (OR = 2.183, p = 0.007), worry about being infected (OR = 2.336, p < 0.001), ≥1 h/day spent hearing COVID-19 information (OR = 1.960, p < 0.001), time difference in midpoint time in bed (OR = 1.230, p < 0.001), and time difference in time in bed (OR = 0.711, p < 0.001). CONCLUSIONS: Our study revealed that more than one-fourth of the participants suffered poor sleep quality during the COVID-19 outbreak. In addition to the poor health status and COVID-19-related anxiety, delayed sleep phase and reduced time in bed impacted sleep quality in the general population in China.