Complexity of Government response to Covid-19 pandemic: A perspective of coupled dynamics on information heterogeneity and epidemic outbreak (preprint)

This study aims at modeling the universal failure in preventing the outbreak of COVID-19 via real-world data from the perspective of complexity and network science. Through formalizing information heterogeneity and government intervention in the coupled dynamics of epidemic and infodemic spreading; first, we find that information heterogeneity and its induced variation in human responses significantly increase the complexity of the government intervention decision. The complexity results in a dilemma between the socially optimal intervention that is risky for the government and the privately optimal intervention that is safer for the government but harmful to the social welfare. Second, via counterfactual analysis against the COVID-19 crisis in Wuhan, 2020, we find that the intervention dilemma becomes even worse if the initial decision time and the decision horizon vary. In the short horizon, both socially and privately optimal interventions agree with each other and require blocking the spread of all COVID-19-related information, leading to a negligible infection ratio 30 days after the initial reporting time. However, if the time horizon is prolonged to 180 days, only the privately optimal intervention requires information blocking, which would induce a catastrophically higher infection ratio than that in the counter-factual world where the socially optimal intervention encourages early-stage information spread. These findings contribute to the literature by revealing the complexity incurred by the coupled infodemic-epidemic dynamics and information heterogeneity to the governmental intervention decision, which also sheds insight into the design of an effective early warning system against the epidemic crisis in the future.

Exploring the relationship between novel Coronavirus pneumonia and Parkinson's disease.

The hypothesis is that there is 0a relationship between Parkinson's disease and coronavirus disease 2019 (COVID-19). By summarizing the pathogenesis of Parkinson's disease and COVID-19 and the impact of COVID-19 on the central nervous system, the relationship between Parkinson's disease and COVID-19 was analyzed, including whether Parkinson's disease is a predisposition factor for COVID-19 and whether COVID-19 causes the occurrence of Parkinson's disease. Discuss the impact of COVID-19 on patients with Parkinson's disease, including symptoms and life impact. To summarize the principles, goals and methods of home rehabilitation for Parkinson's disease patients during COVID-19. Through the analysis of this paper, it is believed that COVID-19 may cause Parkinson's disease. Parkinson's disease has the condition of susceptibility to COVID-19, but this conclusion is still controversial.

Molecular mechanism of the TGF­ß/Smad7 signaling pathway in ulcerative colitis.

Aberrant TGF­ß/Smad7 signaling has been reported to be an important mechanism underlying the pathogenesis of ulcerative colitis. Therefore, the present study aimed to investigate the effects of a number of potential anti­colitis agents on intestinal epithelial permeability and the TGF­ß/Smad7 signaling pathway in an experimental model of colitis. A mouse model of colitis was first established before anti­TNF­α and 5­aminosalicyclic acid (5­ASA) were administered intraperitoneally and orally, respectively. Myeloperoxidase (MPO) activity, histological index (HI) of the colon and the disease activity index (DAI) scores were then detected in each mouse. Transmission electron microscopy (TEM), immunohistochemical and functional tests, including Evans blue (EB) and FITC­dextran (FD­4) staining, were used to evaluate intestinal mucosal permeability. The expression of epithelial phenotype markers E­cadherin, occludin, zona occludens (ZO­1), TGF­ß and Smad7 were measured. In addition, epithelial myosin light chain kinase (MLCK) expression and activity were measured. Anti­TNF­α and 5­ASA treatments was both found to effectively reduce the DAI score and HI, whilst decreasing colonic MPO activity, plasma levels of FD­4 and EB permeation of the intestine. Furthermore, anti­TNF­α and 5­ASA treatments decreased MLCK expression and activity, reduced the expression of Smad7 in the small intestine epithelium, but increased the expression of TGF­ß. In mice with colitis, TEM revealed partial epithelial injury in the ileum, where the number of intercellular tight junctions and the expression levels of E­cadherin, ZO­1 and occludin were decreased, all of which were alleviated by anti­TNF­α and 5­ASA treatment. In conclusion, anti­TNF­α and 5­ASA both exerted protective effects on intestinal epithelial permeability in an experimental mouse model of colitis. The underlying mechanism may be mediated at least in part by the increase in TGF­ß expression and/or the reduction in Smad7 expression, which can inhibit epithelial MLCK activity and in turn reduce mucosal permeability during the pathogenesis of ulcerative colitis.

Exploring the Clinical Characteristics of COVID-19 Clusters Identified Using Factor Analysis of Mixed Data-Based Cluster Analysis.

The COVID-19 outbreak has brought great challenges to healthcare resources around the world. Patients with COVID-19 exhibit a broad spectrum of clinical characteristics. In this study, the Factor Analysis of Mixed Data (FAMD)-based cluster analysis was applied to demographic information, laboratory indicators at the time of admission, and symptoms presented before admission. Three COVID-19 clusters with distinct clinical features were identified by FAMD-based cluster analysis. The FAMD-based cluster analysis results indicated that the symptoms of COVID-19 were roughly consistent with the laboratory findings of COVID-19 patients. Furthermore, symptoms for mild patients were atypical. Different hospital stay durations and survival differences among the three clusters were also found, and the more severe the clinical characteristics were, the worse the prognosis. Our aims were to describe COVID-19 clusters with different clinical characteristics, and a classifier model according to the results of FAMD-based cluster analysis was constructed to help provide better individualized treatments for numerous COVID-19 patients in the future.

Clinical characteristics of 17 patients with COVID-19 and systemic autoimmune diseases: a retrospective study.

OBJECTIVES: Increasing data about COVID-19 have been acquired from the general population. We aim to further evaluate the clinical characteristics of COVID-19 in patients with systemic autoimmune diseases (AIDs). METHODS: We included all confirmed inpatients with COVID-19 and systemic AIDs in Wuhan Tongji Hospital from 29 January to 8 March 2020. We retrospectively collected and analysed information on epidemiology of 1255 inpatients and additional clinical characteristics of patients with systemic AIDs. Outcomes were followed up until 16 April 2020. RESULTS: Of the 1255 patients with COVID-19, the median age was 64.0 years and 53.1% were male. More than half (63.0%) had chronic comorbidities. The proportions of elderly, male and patients with comorbidities were significantly higher in intensive care unit (ICU) than in the general ward (p<0.001). 17 (0.61%) patients with systemic AIDs were further screened and analysed from 2804 inpatients. The median age was 64.0 years and 82.4% were female. All patients were living in Wuhan and two family clusters were found. 1 (5.9%) patient was admitted to ICU and one died. 10 (62.5%) of 16 patients changed or stopped their anti-AIDs treatments during hospitalisation, and 5 of them felt that the disease had worsened after the quarantine. CONCLUSIONS: Older males with chronic comorbidities are more vulnerable to severe COVID-19. The lower proportion of COVID-19 in patients with systemic AIDs needs more high-quality human clinical trials and in-depth mechanism researches. Of note, the withdrawal of anti-AIDs treatments during hospitalisation can lead to flares of diseases.