A new approach to COVID-19 data mining: A deep spatial-temporal prediction model based on tree structure for traffic revitalization index.

The outbreak of the COVID-19 epidemic has had a huge impact on a global scale and its impact has covered almost all human industries. The Chinese government enacted a series of policies to restrict the transportation industry in order to slow the spread of the COVID-19 virus in early 2020. With the gradual control of the COVID-19 epidemic and the reduction of confirmed cases, the Chinese transportation industry has gradually recovered. The traffic revitalization index is the main indicator for evaluating the degree of recovery of the urban transportation industry after being affected by the COVID-19 epidemic. The prediction research of traffic revitalization index can help the relevant government departments to know the state of urban traffic from the macro level and formulate relevant policies. Therefore, this study proposes a deep spatial-temporal prediction model based on tree structure for the traffic revitalization index. The model mainly includes spatial convolution module, temporal convolution module and matrix data fusion module. The spatial convolution module builds a tree convolution process based on the tree structure that can contain directional features and hierarchical features of urban nodes. The temporal convolution module constructs a deep network for capturing temporal dependent features of the data in the multi-layer residual structure. The matrix data fusion module can perform multi-scale fusion of COVID-19 epidemic data and traffic revitalization index data to further improve the prediction effect of the model. In this study, experimental comparisons between our model and multiple baseline models are conducted on real datasets. The experimental results show that our model has an average improvement of 21%, 18%, and 23% in MAE, RMSE and MAPE indicators, respectively.

Deep learning in the COVID-19 epidemic: A deep model for urban traffic revitalization index.

The research of traffic revitalization index can provide support for the formulation and adjustment of policies related to urban management, epidemic prevention and resumption of work and production. This paper proposes a deep model for the prediction of urban Traffic Revitalization Index (DeepTRI). The DeepTRI builds model for the data of COVID-19 epidemic and traffic revitalization index for major cities in China. The location information of 29 cities forms the topological structure of graph. The Spatial Convolution Layer proposed in this paper captures the spatial correlation features of the graph structure. The special Graph Data Fusion module distributes and fuses the two kinds of data according to different proportions to increase the trend of spatial correlation of the data. In order to reduce the complexity of the computational process, the Temporal Convolution Layer replaces the gated recursive mechanism of the traditional recurrent neural network with a multi-level residual structure. It uses the dilated convolution whose dilation factor changes according to convex function to control the dynamic change of the receptive field and uses causal convolution to fully mine the historical information of the data to optimize the ability of long-term prediction. The comparative experiments among DeepTRI and three baselines (traditional recurrent neural network, ordinary spatial-temporal model and graph spatial-temporal model) show the advantages of DeepTRI in the evaluation index and resolving two under-fitting problems (under-fitting of edge values and under-fitting of local peaks).

Analysis of the intestinal microbiota in COVID-19 patients and its correlation with the inflammatory factor IL-18.

The ongoing global pandemic of COVID-19 disease, which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), mainly infect lung epithelial cells, and spread mainly through respiratory droplets. However, recent studies showed potential intestinal infection of SARS-CoV-2, implicated the possibility that the intestinal infection of SARS-CoV-2 may correlate with the dysbiosis of gut microbiota, as well as the severity of COVID-19 symptoms. Here, we investigated the alteration of the gut microbiota in COVID-19 patients, as well as analyzed the correlation between the altered microbes and the levels of intestinal inflammatory cytokine IL-18, which was reported to be elevated in the serum of in COVID-19 patients. Comparing with healthy controls or seasonal flu patients, the gut microbiota showed significantly reduced diversity, with increased opportunistic pathogens in COVID-19 patients. Also, IL-18 level was higher in the fecal samples of COVID-19 patients than in those of either healthy controls or seasonal flu patients. Moreover, the IL-18 levels were even higher in the fecal supernatants obtained from COVID-19 patients that tested positive for SARS-CoV-2 RNA than those that tested negative in fecal samples. These results indicate that changes in gut microbiota composition might contribute to SARS-CoV-2-induced production of inflammatory cytokines in the intestine and potentially also to the onset of a cytokine storm.

A graph spatial-temporal model for predicting population density of key areas.

Predicting the population density of key areas of the city is crucial. It helps reduce the spread risk of Covid-19 and predict individuals' travel needs. Although current researches focus on using the method of clustering to predict the population density, there is almost no discussion about using spatial-temporal models to predict the population density of key areas in a city without using actual regional images. We abstract 997 key areas and their regional connections into a graph structure and propose a model called Word Embedded Spatial-temporal Graph Convolutional Network (WE-STGCN). WE-STGCN is mainly composed of the Spatial Convolution Layer, the Temporal Convolution Layer, and the Feature Component. Based on the data set provided by the DataFountain platform, we evaluate the model and compare it with some typical models. Experimental results show that WE-STGCN has 53.97% improved to baselines on average and can commendably predicting the population density of key areas.

Predictive criteria of severe cases in COVID-19 patients of early stage: A retrospective observational study.

BACKGROUND: Patients with coronavirus disease 2019 (COVID-19) often suffer sudden deterioration of disease around 1-2 weeks after onset. Once the disease progressed to severe phase, clinical prognosis of patients will significantly deteriorate. METHODS: This was a multicenter retrospective study on patients of all adult inpatients (≥18 years old) from Tianyou Hospital (Wuhan, China) and the Fourth Affiliated Hospital, Zhejiang University School of Medicine. All 139 patients had laboratory-confirmed COVID-19 in their early stage, which is defined as within 7 days of clinical symptoms. Univariate and multivariate logistic regression models were used to determine the predictive factors in the early detection of patients who may subsequently develop into severe cases. RESULTS: Multivariable logistic regression analysis showed that the higher level of hypersensitivity C-reactive protein (OR = 4.77, 95% CI:1.92-11.87, P = .001), elevated alanine aminotransferase (OR = 6.87, 95%CI:1.56-30.21, P = .011), and chronic comorbidities (OR = 11.48, 95% CI:4.44-29.66, P < .001) are the determining risk factors for the progression into severe pneumonia in COVID-19 patients. CONCLUSION: Early COVID-19 patients with chronic comorbidities, elevated hs-CRP or elevated ALT are significantly more likely to develop severe pneumonia as the disease progresses. These risk factors may facilitate the early diagnosis of critical patients in clinical practice.

A child with household transmitted COVID-19.

BACKGROUND: Although people of all ages are susceptible to the novel coronavirus infection, which is presently named "Coronavirus Disease 2019" (COVID-19), there has been relatively few cases reported among children. Therefore, it is necessary to understand the clinical characteristics of COVID-19 in children and the differences from adults. CASE PRESENTATION: We report one pediatric case of COVID-19. A 14-month-old boy was admitted to the hospital with a symptom of fever, and was diagnosed with a mild form of COVID-19. The child's mother and grandmother also tested positive for SARS-CoV-2 RNA. However, the lymphocyte counts were normal. The chest computed tomography (CT) revealed scattered ground glass opacities in the right lower lobe close to the pleura and resorption after the treatment. The patient continued to test positive for SARS-CoV-2 RNA in the nasopharyngeal swabs and stool at 17 days after the disappearance of symptoms. CONCLUSION: The present pediatric case of COVID-19 was acquired through household transmission, and the symptoms were mild. Lymphocyte counts did not significantly decrease. The RNA of SARS-CoV-2 in stool and nasopharyngeal swabs remained positive for an extended period of time after the disappearance of symptoms. This suggests that attention should be given to the potential contagiousness of pediatric COVID-19 cases after clinical recovery.