Weighted Centrality and Retail Store Locations in Beijing, China: A Temporal Perspective from Dynamic Public Transport Flow Networks

The spatial relationship between transport networks and retail store locations is an important topic in studies related to commercial activities. Much effort has been made to study physical street networks, but they are seldom empirically discussed with considerations of transport flow networks from a temporal perspective. By using Beijing’s bus and subway smart card data (SCD) and point of interest (POI) data, this study examined the location patterns of various retail stores and their daily dynamic relationships with three weighted centrality indices in the networks of public transport flows: degree, betweenness, and closeness. The results indicate that most types of retail stores are highly correlated with weighted centrality indices. For the network constructed by total public transport flows in the week, supermarkets, convenience stores, electronics stores, and specialty stores had the highest weighted degree value. By contrast, building material stores and shopping malls had the weighted closeness and weighted betweenness values, respectively. From a temporal perspective, most retail types’ largest correlations on weekdays occurred during the after-work period of 19:00 to 21:00. On weekends, shopping malls and electronics stores changed their favorite periods to the daytime, while specialty stores favored the daytime on both weekdays and weekends. In general, the higher store type level of the shopping malls correlates more to weighted closeness or betweenness, and the lower-level store type of convenience stores correlates more to weighted degree. This study provides a temporal analysis that surpasses previous studies on street centrality and can help with urban commercial planning.

Clinical outcomes of hemodialysis patients infected with severe acute respiratory syndrome coronavirus 2 and impact of proactive chest computed tomography scans.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is an emerging infectious disease that first manifested in humans in Wuhan, Hubei Province, China, in December 2019, and has subsequently spread worldwide. METHODS: We conducted a retrospective, single-center case series of the seven maintenance hemodialysis (HD) patients infected with COVID-19 at Zhongnan Hospital of Wuhan University from 13 January to 7 April 2020 and a proactive search of potential cases by chest computed tomography (CT) scans. RESULTS: Of 202 HD patients, 7 (3.5%) were diagnosed with COVID-19. Five were diagnosed by reverse transcription polymerase chain reaction (RT-PCR) because of compatible symptoms, while two were diagnosed by RT-PCR as a result of screening 197 HD patients without respiratory symptoms by chest CT. Thirteen of 197 patients had positive chest CT features and, of these, 2 (15%) were confirmed to have COVID-19. In COVID-19 patients, the most common features at admission were fatigue, fever and diarrhea [5/7 (71%) had all these]. Common laboratory features included lymphocytopenia [6/7 (86%)], elevated lactate dehydrogenase [3/4 (75%)], D-dimer [5/6 (83%)], high-sensitivity C-reactive protein [4/4 (100%)] and procalcitonin [5/5 (100%)]. Chest CT showed bilateral patchy shadows or ground-glass opacity in the lungs of all patients. Four of seven (57%) received oxygen therapy, one (14%) received noninvasive and invasive mechanical ventilation, five (71%) received antiviral and antibacterial drugs, three (43%) recieved glucocorticoid therapy and one (14%) received continuous renal replacement therapy. As the last follow-up, four of the seven patients (57%) had been discharged and three patients were dead. CONCLUSIONS: Chest CT may identify COVID-19 patients without clear symptoms, but the specificity is low. The mortality of COVID-19 patients on HD was high.

Co-infection with SARS-CoV-2 and parainfluenza virus in a hemodialysis patient: A case report.

BACKGROUND: In December 2019, the 2019 novel coronavirus disease (COVID-19) caused by SARS-CoV-2 emerged in China and now has spread to many countries. Limited data are available for hemodialysis patients with COVID-19. CASE PRESENTATION: We report a 66-year-old man with confirmed COVID-19 and parainfluenza virus infection in Wuhan. We describe the clinical characteristics, radiological findings, and treatment of the hemodialysis patient, including the patient's initial pneumonia at presentation with progression to acute respiratory distress syndrome (ARDS). DISCUSSION AND CONCLUSION: Our case underscores the possibility of SARS-CoV-2 co-infection with other pathogens in hemodialysis patients and the importance of early identification of COVID-19.

Distribution of the COVID-19 epidemic and correlation with population emigration from Wuhan, China.

BACKGROUND: The ongoing new coronavirus pneumonia (Corona Virus Disease 2019, COVID-19) outbreak is spreading in China, but it has not yet reached its peak. Five million people emigrated from Wuhan before lockdown, potentially representing a source of virus infection. Determining case distribution and its correlation with population emigration from Wuhan in the early stage of the epidemic is of great importance for early warning and for the prevention of future outbreaks. METHODS: The official case report on the COVID-19 epidemic was collected as of January 30, 2020. Time and location information on COVID-19 cases was extracted and analyzed using ArcGIS and WinBUGS software. Data on population migration from Wuhan city and Hubei province were extracted from Baidu Qianxi, and their correlation with the number of cases was analyzed. RESULTS: The COVID-19 confirmed and death cases in Hubei province accounted for 59.91% (5806/9692) and 95.77% (204/213) of the total cases in China, respectively. Hot spot provinces included Sichuan and Yunnan, which are adjacent to Hubei. The time risk of Hubei province on the following day was 1.960 times that on the previous day. The number of cases in some cities was relatively low, but the time risk appeared to be continuously rising. The correlation coefficient between the provincial number of cases and emigration from Wuhan was up to 0.943. The lockdown of 17 cities in Hubei province and the implementation of nationwide control measures efficiently prevented an exponential growth in the number of cases. CONCLUSIONS: The population that emigrated from Wuhan was the main infection source in other cities and provinces. Some cities with a low number of cases showed a rapid increase in case load. Owing to the upcoming Spring Festival return wave, understanding the risk trends in different regions is crucial to ensure preparedness at both the individual and organization levels and to prevent new outbreaks.