Epidemiology and infection
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Observational Study
Development and validation of prognosis model of mortality risk in patients with COVID-19.
This study aimed to identify clinical features for prognosing mortality risk using machine-learning methods in patients with coronavirus disease 2019 (COVID-19). A retrospective study of the inpatients with COVID-19 admitted from 15 January to 15 March 2020 in Wuhan is reported. The data of symptoms, comorbidity, demographic, vital sign, CT scans results and laboratory test results on admission were collected. ⋯ A multivariate logistic regression model was formed to predict mortality 292 in-sample patients with area under the receiver operating characteristics (AUROC) of 0.9521, which was better than CURB-65 (AUROC of 0.8501) and the machine-learning-based model (AUROC of 0.4530). An out-sample data set of 13 patients was further tested to show our model (AUROC of 0.6061) was also better than CURB-65 (AUROC of 0.4608) and the machine-learning-based model (AUROC of 0.2292). LDH, CRP and age can be used to identify severe patients with COVID-19 on hospital admission.
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Increased population movements and increased mobility made it possible for severe acute respiratory syndrome coronavirus 2, which is mainly spread by respiratory droplets, to spread faster and more easily. This study tracked and analysed the development of the coronavirus 2019 (COVID-19) outbreak in the top 100 cities that were destinations for people who left Wuhan before the city entered lockdown. Data were collected from the top 100 destination cities for people who travelled from Wuhan before the lockdown, the proportion of people travelling into each city, the intensity of intracity travel and the daily reports of COVID-19. ⋯ The average intensity of intracity travel on the nth day in these cities during the development of the outbreak was positively related to the growth rate of the number of confirmed COVID-19 cases on the n + 5th day in these cities and had a significant linear relationship (P < 0.01). Higher intensities of population movement were associated with a higher incidence of COVID-19 during the pandemic. Restrictions on population movement can effectively curb the development of an outbreak.
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There is limited information concerning the viral load of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in aerosols deposited on environmental surfaces and the effectiveness of infection prevention and control procedures on eliminating SARS-CoV-2 contamination in hospital settings. We examined the concentration of SARS-CoV-2 in aerosol samples and on environmental surfaces in a hospital designated for treating severe COVID-19 patients. Aerosol samples were collected by a microbial air sampler, and environmental surfaces were sampled using sterile premoistened swabs at multiple sites. ⋯ Only two swabs, sampled from the inside of a patient's mask, were positive for SARS-CoV-2 RNA. All other swabs and aerosol samples were negative for the virus. Our study indicated that strict implementation of infection prevention and control procedures was highly effective in eliminating aerosol and environmental borne SARS-CoV-2 RNA thereby reducing the risk of cross-infection in hospitals.
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Multicenter Study
Application of ordinal logistic regression analysis to identify the determinants of illness severity of COVID-19 in China.
Corona Virus Disease 2019 (COVID-19) has presented an unprecedented challenge to the health-care system across the world. The current study aims to identify the determinants of illness severity of COVID-19 based on ordinal responses. A retrospective cohort of COVID-19 patients from four hospitals in three provinces in China was established, and 598 patients were included from 1 January to 8 March 2020, and divided into moderate, severe and critical illness group. ⋯ The interval between illness onset and diagnosis (OR = 1.056, 95% CI: 1.012-1.101) and interval between illness onset and admission (OR = 1.048, 95% CI: 1.009-1.087) were independent significant predictors of illness severity. Patients of critical illness suffered from inferior survival, as compared with patients in the severe group (HR = 14.309, 95% CI: 5.585-36.659) and in the moderate group (HR = 41.021, 95% CI: 17.588-95.678). Our findings highlight that the identified determinants may help to predict the risk of developing more severe illness among COVID-19 patients and contribute to optimising arrangement of health resources.
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We report a family cluster of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection involving five patients in a family cluster in Dazhou, China, including the epidemiological, clinical, laboratory and radiological findings. Three-generation transmission was observed. ⋯ This cluster also demonstrated that COVID-19 is transmissible during the incubation period of an asymptomatic person. Early isolation and treatment, stressing prevention of cluster outbreaks, could help prevent further spread of the epidemic.