Social and spatial heterogeneities in COVID-19 impacts on individual's metro use: A big-data driven causality inference.

While mobility intervention policies implemented during the early stages of the COVID-19 outbreak had a significant impact on public transit use, few studies have investigated the individual-level responses in metro transit riding behaviors. Using long time-series cellphone big data from frequent metro users in Shenzhen, China, we developed a quasi-experimental interrupted time series (ITS) design to estimate the treatment effects of mobility intervention policies on people's daily shares of metro transit use (SMU). The results indicate that the first-level emergency response (FLR) and the public transit restriction (PTR) policy yielded abrupt drops in SMU of 8.0% and 17.6%, respectively, whereas the return-to-work (RTW) order had an immediate recovery effect of 14.5%. The effect of the FLR is time-decreasing while those effects of the PTR and the RTW are time-increasing. Females and elderly people living in neighborhoods near the city center with low population density and fewer transit stations are more adaptable to policy interventions for reducing SMUs, while the recovery effect of RTW is relatively low for the elderly living in less mixed-use neighborhoods with reduced transit service. These findings can help policymakers design more socially- and spatially-precise and equity mobility intervention policies during a pandemic.

A quasi-experimental analysis on the causal effects of COVID-19 on urban park visits: The role of park features and the surrounding built environment

Although many studies have explored the correlations between mobility intervention policies and park use during COVID-19, only a few have used causal inference approaches to assess the policy's treatment effects and how such effects vary across park features and surrounding built environments. In this study, we develop an interrupted time series quasi-experimental design based on three-month mobile phone big data to infer the causal effects of mobility intervention policies on park visits in Shenzhen, including the first-level response (FLR) and return-to-work (RTW) order. The results show that the FLR caused an abrupt decline of 2.21 daily visits per park, with a gradual reduction rate of 0.54 per day, whereas the RTW order helped recover park visits with an immediate increase of 2.20 daily visits and a gradual growth rate of 0.94 visits per day. The results also show that the impact of COVID-19 on park visits exhibited social and spatial heterogeneity: the mobility-reduction effect was smaller in low-level parks (e.g., community-level parks) with small sizes but without sports facilities and water scenes, whereas parks surrounded by compact neighborhoods and land use were more impacted by the pandemic. These findings provide planners with important insights into resilient green space and sustainable neighborhood planning for the post-COVID era.

A quasi-experimental analysis on the causal effects of COVID-19 on urban park visits: The role of park features and the surrounding built environment.

Although many studies have explored the correlations between mobility intervention policies and park use during COVID-19, only a few have used causal inference approaches to assessing the policy's treatment effects and how such effects vary across park features and surrounding built environments. In this study, we develop an interrupted time-series quasi-experimental design based on three-month mobile phone big data to infer the causal effects of mobility intervention policies on park visits in Shenzhen, including the first-level response (FLR) and return-to-work (RTW) order. The results show that the FLR caused an abrupt decline of 2.21 daily visits per park, with a gradual reduction rate of 0.54 per day, whereas the RTW order helped recover park visits with an immediate increase of 2.20 daily visits and a gradual growth rate of 0.94 visits per day. The results also show that the impact of COVID-19 on park visits exhibited social and spatial heterogeneities: the mobility-reduction effect was smaller in low-level parks (e.g., community-level parks) with small sizes but without sports facilities and water scenes, whereas parks surrounded by compact neighborhoods and land use were more impacted by the pandemic. These findings provide planners with important insights into resilient green space and sustainable neighborhood planning for the post-COVID era.

Causal effects of mobility intervention policies on intracity flows during the COVID-19 pandemic: The moderating role of zonal locations in the transportation networks.

Many studies have investigated the impact of mobility restriction policies on the change of intercity flows during the outbreak of COVID-19, whereas only a few have highlighted intracity flows. By using the mobile phone trajectory data of approximately three months, we develop an interrupted time series quasi-experimental design to estimate the abrupt and gradual effects of mobility intervention policies during the pandemic on intracity flows of 491 neighborhoods in Shenzhen, China, with a focus on the role of urban transport networks. The results show that the highest level of public health emergency response caused an abrupt decline by 4567 trips and a gradually increasing effect by 34 trips per day. The effectiveness of the second return-to-work order (RtW2) was found to be clearly larger than that of the first return-to-work order (RtW1) as a mobility restoration strategy. The causal effects of mobility intervention policies are heterogenous across zonal locations in varying urban transport networks. The declining effect of health emergency response and rebounding effect of RtW2 are considerably large in better-connected neighborhoods with metro transit, as well as in those close to the airport. These findings provide new insights into the identification of pandemic-vulnerable hotspots in the transport network inside the city, as well as of crucial neighborhoods with increased adaptability to mobility interventions during the onset and decline of COVID-19.

Serum peptidome profiles immune response of COVID-19 Vaccine administration.

Background: Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused significant loss of life and property. In response to the serious pandemic, recently developed vaccines against SARS-CoV-2 have been administrated to the public. Nevertheless, the research on human immunization response against COVID-19 vaccines is insufficient. Although much information associated with vaccine efficacy, safety and immunogenicity has been reported by pharmaceutical companies based on laboratory studies and clinical trials, vaccine evaluation needs to be extended further to better understand the effect of COVID-19 vaccines on human beings. Methods: We performed a comparative peptidome analysis on serum samples from 95 participants collected at four time points before and after receiving CoronaVac. The collected serum samples were analyzed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to profile the serum peptides, and also subjected to humoral and cellular immune response analyses to obtain typical immunogenicity information. Results: Significant difference in serum peptidome profiles by MALDI-TOF MS was observed after vaccination. By supervised statistical analysis, a total of 13 serum MALDI-TOF MS feature peaks were obtained on day 28 and day 42 of vaccination. The feature peaks were identified as component C1q receptor, CD59 glycoprotein, mannose-binding protein C, platelet basic protein, CD99 antigen, Leucine-rich alpha-2-glycoprotein, integral membrane protein 2B, platelet factor 4 and hemoglobin subunits. Combining with immunogenicity analysis, the study provided evidence for the humoral and cellular immune responses activated by CoronaVac. Furthermore, we found that it is possible to distinguish neutralizing antibody (NAbs)-positive from NAbs-negative individuals after complete vaccination using the serum peptidome profiles by MALDI-TOF MS together with machine learning methods, including random forest (RF), partial least squares-discriminant analysis (PLS-DA), linear support vector machine (SVM) and logistic regression (LR). Conclusions: The study shows the promise of MALDI-TOF MS-based serum peptidome analysis for the assessment of immune responses activated by COVID-19 vaccination, and discovered a panel of serum peptides biomarkers for COVID-19 vaccination and for NAbs generation. The method developed in this study can help not only in the development of new vaccines, but also in the post-marketing evaluation of developed vaccines.

Structural changes in intercity mobility networks of China during the COVID-19 outbreak: A weighted stochastic block modeling analysis.

This study focuses on a mesoscale perspective to examine the structural and spatial changes in the intercity mobility networks of China from three phases of before, during and after the Wuhan lockdown due to the outbreak of COVID-19. Taking advantages of mobility big data from Baidu Maps, we introduce the weighted stochastic block model (WSBM) to measure and compare mesoscale structures in the three mobility networks. The results reveal significant changes to volume and structure of the intercity mobility networks. Particularly, WSBM results show that the intercity network transformed from a typical core-periphery structure in the normal phase, to a hybrid and asymmetric structure with mixing core-peripheries and local communities in the lockdown phase, and to a multi-community structure with nested core-peripheries during the post-lockdown phase. These changes suggest that the outbreak of COVID-19 and the travel restrictions deconstructed the original hierarchy of the intercity mobility network in China, making the network more locally or regionally fragmented, even at the recovery stage. This study provides new empirical and methodological insights into understanding mobility network dynamics under the impact of COVID-19, helping assess the emergency-induced impact as well as the recovery process of the mobility network.

Artificial Intelligence Clinicians Can Use Chest Computed Tomography Technology to Automatically Diagnose Coronavirus Disease 2019 (COVID-19) Pneumonia and Enhance Low-Quality Images.

PURPOSE: Nowadays, the number of patients with COVID-19 pneumonia worldwide is still increasing. The clinical diagnosis of COVID-19 pneumonia faces challenges, such as the difficulty to perform RT-PCR tests in real time, the lack of experienced radiologists, clinical low-quality images, and the similarity of imaging features of community-acquired pneumonia and COVID-19. Therefore, we proposed an artificial intelligence model GARCD that uses chest CT images to assist in the diagnosis of COVID-19 in real time. It can show better diagnostic performance even facing low-quality CT images. METHODS: We used 14,129 CT images from 104 patients. A total of 12,929 samples were used to build artificial intelligence models, and 1200 samples were used to test its performance. The image quality improvement module is based on the generative adversarial structure. It improves the quality of the input image under the joint drive of feature loss and content loss. The enhanced image is sent to the disease diagnosis model based on residual convolutional network. It automatically extracts the semantic features of the image and then infers the probability that the sample belongs to COVID-19. The ROC curve is used to evaluate the performance of the model. RESULTS: This model can effectively enhance the low-quality image and make the image that is difficult to be recognized become recognizable. The model proposed in this paper reached 97.8% AUC, 96.97% sensitivity and 91.16% specificity in an independent test set. ResNet, GADCD, CNN, and DenseNet achieved 80.9%, 97.3%, 70.7% and 85.7% AUC in the same test set, respectively. By comparing the performance with related works, it is proved that the model proposed has stronger clinical usability. CONCLUSION: The method proposed can effectively assist doctors in real-time detection of suspected cases of COVID-19 pneumonia even faces unclear image. It can quickly isolate patients in a targeted manner, which is of positive significance for preventing the further spread of COVID-19 pneumonia.