The Global dynamics of a SIR model considering competitions among multiple strains in patchy environments.

Pandemics, such as Covid-19 and AIDS, tend to be highly contagious and have the characteristics of global spread and existence of multiple virus strains. To analyze the competition among different strains, a high dimensional SIR model studying multiple strains' competition in patchy environments is introduced in this work. By introducing the basic reproductive number of different strains, we found global stability conditions of disease-free equilibrium and persistence conditions of the model. The competition exclusion conditions of that model are also given. This work gives some insights into the properties of the multiple strain patchy model and all of the analysis methods used in this work could be used in other related high dimension systems.

Exploiting an early warning nomogram for predicting the risk of ICU admission in COVID-19 patients: A multi-center study in China (preprint)

Background: Novel coronavirus disease 2019 (COVID-19) is a global public health emergency. Here, we developed and validated a practical model based on the data from a multi-center cohort in China for early identification and prediction of which patients will be admitted to the intensive care unit (ICU). Methods: Data of 1087 patients with laboratory-confirmed COVID-19 were collected from 49 sites between January 2 and February 28, 2020, in Sichuan and Wuhan. Patients were randomly categorized into the training and validation cohorts (7:3). The least absolute shrinkage and selection operator and logistic regression analyzes were used to develop the nomogram. The performance of the nomogram was evaluated for the C-index, calibration, discrimination, and clinical usefulness. Further, the nomogram was externally validated in a different cohort. Results: The individualized prediction nomogram included 6 predictors: age, respiratory rate, systolic blood pressure, smoking status, fever, and chronic kidney disease. The model demonstrated a high discriminative ability in the training cohort (C-index = 0.829), which was confirmed in the external validation cohort (C-index = 0.776). In addition, the calibration plots confirmed good concordance for predicting the risk of ICU admission. Decision curve analysis revealed that the prediction nomogram was clinically useful. Conclusion: We established an early prediction model incorporating clinical characteristics that could be quickly obtained on hospital admission, even in community health centers. This model can be conveniently used to predict the individual risk for ICU admission of patients with COVID-19 and optimize the use of limited resources.

Can corticosteroids improve the outcomes of patients with Covid-19? A retrospective cohort study of patients within and outside the epicentre (preprint)

Background The novel coronavirus disease 2019 (Covid-19) has been a worldwide pandemic with more than 300,000 deaths. Corticosteroids have been used in some patients with severe Covid-19 in order to control the systemic inflammation or cytokine storm, however, their effects and safety have not yet been elucidated.Methods Patients with confirmed Covid-19 were retrospectively included from both the epicentre and out of the epicentre. Patients were classified into two groups according to the use of systemic corticosteroids, and the mortality and the rate of virus clearance were compared between the two groups. In addition, independent factors associated with death after corticosteroids treatment were also identified.Results A total of 775 patients were included in our final analysis, of which 238 (30.7%) patients received systemic corticosteroids treatment. Compared with patients without corticosteroids treatment, patients with corticosteroids treatment had significantly higher mortality (19.3% vs. 3.7%, P < 0.001) and lower rate of virus clearance (43.2% vs. 66.7%, P < 0.001) although along with increase of SpO2/FiO2 and blood lymphocytes in patients with severe Covid-19. Corticosteroids treatment was associated with longer hospital length of stays and delayed virus clearance time. In patients with corticosteroids treatment, blood lymphocytes (odds ratio (OR) 0.792, 95% confidence interval (CI) 0.672–0.932, P = 0.005) and creatine kinase (CK) (OR 1.006, 95%CI 1.000-1.012, P = 0.038) were independent risk factors associated with death, with a sensitivity of 90.91% and 44.44% and a specificity of 70.75% and 94.05%, respectively.Conclusions In patients with Covid-19, corticosteroids treatment is associated with increased mortality and reduced rate of virus clearance.