ABSTRACT
Respiratory infectious diseases have seriously harmed people's daily lives. The analysis of urban functional spatial structure from the perspective of prevention and control of respiratory infectious diseases has practical significance for optimizing the urban functional spatial structure and slowing down the spread of infectious diseases. In this study, kernel density, random forest, geographic weighted spatial analysis, and other methods were used to investigate the spatial relationship between the urban functional spatial structure constructed from multi-source data and confirmed tuberculosis cases. A summary of the main driving factors and the heterogeneous influence of patient gender was provided. Results showed that: (1) the infection risk of confirmed cases in the urban central area was highest and relatively concentrated; (2) the main functional area characteristics that led to the cluster of patients were commercial services (0.4217, 0.2693, 0.3647), and shopping services were the first driving factor of infection risk; (3) In the stratified analysis, the spatial distribution of male patients was greater than that of female patients. Compared to the overall analysis of patients, the spatial correlation between male patients and park green space was the most significant over three years.There is an increasing spatial correlation between public management and the influence location of each functional area. Because of the similarities in spatial transmission and pathology between tuberculosis and COVID-19, this study provides a scientific basis and methodological reference for the scientific prevention and control and precise policy implementation of COVID-19.
Subject(s)
COVID-19 , Tuberculosis , Communicable DiseasesABSTRACT
Background: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has progressed to a pandemic associated with substantial morbidity and mortality. The WHO and the United States Center for Disease Control and Prevention (CDC) have issued interim clinical guidance for management of patients with confirmed coronavirus disease (COVID-19), but there is limited data on the virologic and clinical characteristics for prognosis of severe COVID-19. Methods: A total of 50 patients with severe COVID-19 were divided into good and poor recovery groups. The dynamic viral shedding and serological characteristics of SARS-CoV-2 were explored. The risk factors associated with poor recovery and lung lesion resolutions were identified. In addition, the potential relationships among the viral shedding, the pro-inflammatory response, and lung lesion evolutions were characterized. Results: A total of 58% of the patients had poor recovery and were more likely to have a prolonged interval of viral shedding. The longest viral shedding was 57 days after symptom onset. Older age, hyperlipemia, hypoproteinemia, corticosteroid therapy, consolidation on chest computed-tomography (CT), and prolonged SARS-CoV-2 IgM positive were all associated with poor recovery. Additionally, the odds of impaired lung lesion resolutions were higher in patients with hypoproteinemia, hyperlipemia, and elevated levels of IL-4 and ferritin. Finally, viral shedding and proinflammatory responses were closely correlated with lung lesion evolutions on chest CT. Conclusions Patients with severe COVID-19 have prolonged SARS-CoV-2 infection and delayed intermittent viral shedding. Older age, hyperlipemia, hypoproteinemia, corticosteroid usage, and prolonged SARS-CoV-2 IgM positive might be utilized as predicative factors for the patients with poor recovery.