ABSTRACT
Emergency policing has played a significant role in controlling the spread of COVID-19 in various countries. China is one of the few countries that has successfully controlled the pandemic. What are the factors that determine the effectiveness of emergency policing in China? This study argues that the social capital played a supportive role in emergency policing during COVID-19. Based on the data analysis, we construct a theoretical framework to explain why pandemic control in China has been effective. The effectiveness of the police response to the COVID-19 pandemic in Zhejiang, China, displays the importance of all three types of social capital for emergency policing, including interaction-assisted social capital represented by 'grid-governance', technology-driven social capital represented by 'data sharing', and service-assisted social capital represented by 'safety construction'.
ABSTRACT
In this COVID-19 epidemic, due to insufficient awareness of the impact of sudden public health emergencies on agricultural logistics at this stage, agricultural products were left unsold, stocks were backlogged, and losses were severe. In the process of distribution, we should not only ensure a short time cycle and avoid the contamination of agricultural products by foreign bacteria, but also pay attention to the waste of human, material, and financial resources. Therefore, this study mainly adopts the combination of the petrochemical network and block chain to build an agricultural products emergency logistics model. This paper first shows the operation mechanism of the petri dish network and blockchain coupling in the form of a graph and then uses the culture network modelling and simulation tool PIPE to directly verify the construction model. It is proved that the structure and overall business process of the agricultural products logistics system constructed by combining the Petri net and block chain are reasonable, reliable, and feasible in practical application and development. It is hoped that this study can provide a reference direction for agricultural emergency logistics.
ABSTRACT
OBJECTIVE: The aim of the study was to analyze spatiotemporal changes of CT manifestations in patients with COVID-19 pneumonia. PATIENTS AND METHODS: In this retrospective review, 110 patients with confirmed COVID-19 by RT-PCR form February 16, 2020, to March 28, 2020 were included. A total of 449 CT scans were reviewed. We analyze the type and distribution of lung abnormalities, and CT general assessment and lesion area statistics were performed. Patients were divided into mild, moderate, and severe disease based on Chinese guidelines: mild (patients with minimal symptoms, CT scans showed no pneumonia or a small area of pneumonia infection), moderate (different extent of clinical manifestations and CT scans showed multiple pneumonia infections in both lungs), severe disease (respiratory distress, CT scans lesion area exceeds 50%, and the lesion contains consolidation). The proportion of patients with mild, moderate and severe diseases was counted. RESULTS: The CT score and the area involved reached a peak (median 10) on illness days 7-12, and then, continued to be at a high level. The main abnormal pattern after symptoms appeared GGO (36/94 [36%] to 40/65 [62%] in different periods). The proportion of mixed reached its peak on illness days 13-18 (36/93 [39%]). Pure GGO was the most common subtype of GGO (24 of 60 CT scans [40%] to 23 of 33 CT scans [70%]) after symptoms onset. The ratio of GGO with irregular lines and interfaces peaked on illness days 7-12 (6/34 [18%]). The lesions are mainly distributed on both sides and under the pleura. 76/84 (90%) of discharged patients had residual lesions on the final CT scans. 4 confirmed patients' CT scans did not show lesions (on illness days 1-24 days). There were 47 mild cases (42.7%), 46 moderate cases (41.8%), and 7 severe cases (6.3%). CONCLUSIONS: The degree of lung abnormality on the CT of the patients reached the peak on the 7th to 12th days of the disease. CT performance changes with time have a certain regularity, which may indicate the progress and recovery of the disease. 90% of patients still observed residual lung abnormalities in CT images at the time of discharge. There were 4 confirmed cases where the CT images did not show the lesion; hence, CT cannot be used as a basis for judging COVID-19 as a single tool.