Cascading failures and resilience optimization of hospital infrastructure systems against the COVID-19

The outbreak of the Coronavirus Disease 2019 (COVID-19) has put the resilience of a country's healthcare infrastructure to the most severe test. The challenge of taking emergency measures to optimize the supply of medical resources and effectively meet the medical needs of residents is an important issue that needs to be resolved urgently in the prevention and control of public health emergencies. This paper analyzes cascading failures and optimization of the resilience of the hospital infrastructure system (HIS) with the presence of the COVID-19. It proposes a propagation model to describe the COVID-19 infectious process and establishes a cascading failure model of a HIS to analyze its failure mechanism. It also proposes a method for optimizing the resilience of HIS. Then the supplies and demands in maintaining the operations of HIS are studied, and a restoration strategy is obtained. Finally, simulation analysis of the spread of the COVID-19 is carried out to illustrate the applicability of the proposed method.

Cascading failures and resilience optimization of hospital infrastructure systems against the COVID-19.

The outbreak of the Coronavirus Disease 2019 (COVID-19) has put the resilience of a country's healthcare infrastructure to the most severe test. The challenge of taking emergency measures to optimize the supply of medical resources and effectively meet the medical needs of residents is an important issue that needs to be resolved urgently in the prevention and control of public health emergencies. This paper analyzes cascading failures and optimization of the resilience of the hospital infrastructure system (HIS) with the presence of the COVID-19. It proposes a propagation model to describe the COVID-19 infectious process and establishes a cascading failure model of a HIS to analyze its failure mechanism. It also proposes a method for optimizing the resilience of HIS. Then the supplies and demands in maintaining the operations of HIS are studied, and a restoration strategy is obtained. Finally, simulation analysis of the spread of the COVID-19 is carried out to illustrate the applicability of the proposed method.

Mental Health Disorders and Associated Risk Factors in Quarantined Adults During the COVID-19 Outbreak in China: Cross-Sectional Study.

BACKGROUND: People undergoing mass home- and community-based quarantine are vulnerable to mental health disorders during outbreaks of coronavirus disease (COVID-19), but few studies have evaluated the associated psychosocial factors. OBJECTIVE: This study aimed to estimate the prevalence of anxiety and depressive symptoms and identify associated demographic and psychosocial factors in the general Chinese population during the COVID-19 pandemic quarantine period. METHODS: Participants aged 18 years or above were recruited in a cross-sectional online survey using snowball sampling from February 26-29, 2020. The survey included questions on demographics, family relationships, chronic diseases, quarantine conditions, lifestyle, COVID-19 infection, and anxiety and depressive symptoms. Logistic regression analyses were conducted to identify factors associated with elevated anxiety or depressive symptoms. RESULTS: Out of 2331 participants, 762 (32.7%) experienced elevated anxiety or depressive symptoms. Nine risk factors associated with anxiety or depressive symptoms included younger age, reduced income, having cancer or other chronic diseases, having family members living with cancer, concerns related to COVID-19 infection for themselves or family members, living alone, having family conflicts, having <3 or >8 hours of sedentary time per day, and worsened sleep quality. CONCLUSIONS: The findings highlight an urgent need for psychological support for populations at high risk for elevated anxiety or depressive symptoms during the COVID-19 pandemic.

Optimal Product Substitution and Dual Sourcing Strategy considering Reliability of Production Lines.

Most of the supply chain literature assumes that product substitution is an effective method to mitigate supply chain disruptions and that all production lines either survive or are disrupted together. Such assumptions, however, may not hold in the real world: (1) when there is a shortfall of all products, product substitution may be inadequate unless it is paired with other strategies such as dual sourcing; and (2) production lines do not survive forever and may fail. To relax such assumptions, this paper therefore investigates the situations that the manufacturer may optimize substitution policy and dual sourcing policy to cope with supply chain disruptions. The paper obtains and compares the optimal policies for both deterministic and stochastic demands. A real-world case is also studied to verify the effectiveness of the proposed model.