In this paper, to study rumor spreading, we propose a novel susceptible-infected-removed (SIR) model by introducing the trust mechanism. We derive mean-field equations that describe the dynamics of the SIR model on homogeneous networks and inhomogeneous networks. Then a steady-state analysis is conducted to investigate the critical threshold and the final size of the rumor spreading. We show that the introduction of trust mechanism reduces the final rumor size and the velocity of rumor spreading, but increases the critical thresholds on both networks. Moreover, the trust mechanism not only greatly reduces the maximum rumor influence, but also postpones the rumor terminal time, which provides us with more time to take measures to control the rumor spreading. The theoretical results are confirmed by sufficient numerical simulations.
the outbreak of the novel coronavirus SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) that originated in Wuhan, Hubei province has spread rapidly across China. Many hospitals in Wuhan are the epicenter of the outbreak. The first 14 staff members who were infected with SARS-Cov-2 were from our department. All of them recovered. In such an urgent and unexpected situation, our department began immediate and effective prevention and control strategies to stop the spread of the epidemic in the department. We believe that clinical departments, especially those related to noninfectious diseases in geographic areas of high risk for virus transmission, should take appropriate management and control measures to improve safety during this epidemic. For this purpose, we summarize and share our experiences which should help medical staff prepare in advance for a similar situation. These include the characteristics of SARS-Cov-2 infection, principles of prevention and control of infection, management of infected patients, and epidemic prevention in the outpatient department, ward, operating room, and medical staff.At the end of December 2019, several cases of pneumonia in Wuhan, China caused by an unknown virus were reported to the World Health Organization (WHO). The novel virus was initially identified as a coronavirus on January 7, 2020, and named "severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)" by WHO on February 11, 2020. The disease caused by SARS-CoV-2 is now known as coronavirus disease 2019 (COVID-19). According to the latest retrospective analysis, the median (range) incubation period of COVID-19 is 3 d, but incubation can be as long as 24 d. 1 The median age of patients is 47 yr, and adults are more susceptible than children and adolescents.There is no specific identifying symptom during the early stage of COVID-19 infection. Infected individuals may display weakness, cough, fever, or even no symptom, and yet they are already infectious. The accuracy of early diagnosis based on combined chest computed tomography and nucleic acid test can reach 97%. Expiratory dyspnea occurs in the late stages of infection, and respiratory distress syndrome, acute circulatory failure, or renal failure in severe cases. No specific effective therapeutic protocol in known. The current treatment methods are empiric based on symptoms, and consist of respiratory support (eg, oxygen, mechanical ventilation, or artificial lung technology), antibiotics, and antiviral drugs supplemented by immunoglobulin infusion. 1,2 The outbreak of COVID-19 in Wuhan occurred on the eve of the Chinese New Year (January 12, 2020). However, due to
To combat COVID-19, both clinicians and scientists need to digest the vast amount of relevant biomedical knowledge in literature to understand the disease mechanism and the related biological functions. We have developed a novel and comprehensive knowledge discovery framework, COVID-KG to extract finegrained multimedia knowledge elements (entities, relations and events) from scientific literature. We then exploit the constructed multimedia knowledge graphs (KGs) for question answering and report generation, using drug repurposing as a case study. Our framework also provides detailed contextual sentences, subfigures and knowledge subgraphs as evidence. All of the data, KGs, reports 1 , resources and shared services are publicly available 2 .
Background Many countries have experienced 2 predominant waves of COVID-19–related hospitalizations. Comparing the clinical trajectories of patients hospitalized in separate waves of the pandemic enables further understanding of the evolving epidemiology, pathophysiology, and health care dynamics of the COVID-19 pandemic. Objective In this retrospective cohort study, we analyzed electronic health record (EHR) data from patients with SARS-CoV-2 infections hospitalized in participating health care systems representing 315 hospitals across 6 countries. We compared hospitalization rates, severe COVID-19 risk, and mean laboratory values between patients hospitalized during the first and second waves of the pandemic. Methods Using a federated approach, each participating health care system extracted patient-level clinical data on their first and second wave cohorts and submitted aggregated data to the central site. Data quality control steps were adopted at the central site to correct for implausible values and harmonize units. Statistical analyses were performed by computing individual health care system effect sizes and synthesizing these using random effect meta-analyses to account for heterogeneity. We focused the laboratory analysis on C-reactive protein (CRP), ferritin, fibrinogen, procalcitonin, D-dimer, and creatinine based on their reported associations with severe COVID-19. Results Data were available for 79,613 patients, of which 32,467 were hospitalized in the first wave and 47,146 in the second wave. The prevalence of male patients and patients aged 50 to 69 years decreased significantly between the first and second waves. Patients hospitalized in the second wave had a 9.9% reduction in the risk of severe COVID-19 compared to patients hospitalized in the first wave (95% CI 8.5%-11.3%). Demographic subgroup analyses indicated that patients aged 26 to 49 years and 50 to 69 years; male and female patients; and black patients had significantly lower risk for severe disease in the second wave than in the first wave. At admission, the mean values of CRP were significantly lower in the second wave than in the first wave. On the seventh hospital day, the mean values of CRP, ferritin, fibrinogen, and procalcitonin were significantly lower in the second wave than in the first wave. In general, countries exhibited variable changes in laboratory testing rates from the first to the second wave. At admission, there was a significantly higher testing rate for D-dimer in France, Germany, and Spain. Conclusions Patients hospitalized in the second wave were at significantly lower risk for severe COVID-19. This corresponded to mean laboratory values in the second wave that were more likely to be in typical physiological ranges on the seventh hospital day compared to the first wave. Our federated approach demonstrated the feasibility and power of harmonizing heterogeneous EHR data from multiple international health care systems to rapidly conduct large-scale studies to characterize how COVID-19 clinical trajectories evolve.
A sizeable proportion of enterprises, especially SMEs, in receipt of financial assistance from the government, will fail to repay. In this paper we asked whether, and to what extent, it may be beneficial to apply a screening mechanism to deter those mostly likely to fail to repay from seeking such financial assistance in the first place. The answer largely turns on the relative weights attached for the objectives of stabilisation as compared with allocative efficiency. For this purpose, we develop a two-sector infinite horizon model featuring oligopolistic small businesses and a screening contract in the presence of a pandemic shock with asymmetric information. The adversely affected sector with private information can apply for government loans to reopen businesses once the pandemic has passed. First, we show that a pro-allocation government sets a harsh default sanction to deter entrepreneurs with bad projects from reentering and improves aggregate productivity in the long run, but the economy suffers persistent unemployment in the near term. However, a pro-stabilisation government sets a lenient default sanction or provides full guarantees to reach full employment in the short term, but the economy will be shifted to a lower equilibrium in the long run. The optimal default sanction balances the trade-off between allocation and stabilisation. Then, we derive an analytic measure of "Stabilisation Proclivity" and characterise the parameter space and the macro-financial frictions that render the government either more pro-allocation or more pro-stabilisation. Finally, we solve for the optimal default sanction numerically and conducts comparative statics for various policy analyses.
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