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arxiv; 2022.
Preprint in English | PREPRINT-ARXIV | ID: ppzbmed-2208.00548v1


In the aim to support London's safer recovery from the pandemic by improving road safety intelligently, this study investigated the spatiotemporal patterns of age-involved car crashes and affecting factors, upon answering two main research questions: (1)"What are the spatial and temporal patterns of car crashes as well as their changes in two typical years, 2019 and 2020, in London, and how the influential factors work?"; (2)"What are the spatiotemporal patterns of casualty by age groups, and how people's daily activities affect the patterns pre- and para- the pandemic"? Three approaches, i.e., spatial analysis (network Kernel Density Estimation, NetKDE), factor analysis, and spatiotemporal data mining (tensor decomposition), had been implemented to identify the temporal patterns of car crashes on weekly and daily basis respectively, detect the crashes' hot spots, and to gain better understanding the effect from citizens' daily activity on crashes' patterns pre- and para- the pandemic. It had been found from the study that car crashes mainly clustered in the central part of London, especially busier areas around denser hubs of point-of-interest (POIs); the POIs, as a reflector for citizens' daily activities and travel behaviours, can be of help to gain a better understanding of the crashes' patterns, upon further assessment on interactions through the geographical detector; the crashes' casualty patterns varied by age group, with distinctive relationships between POIs and crashes' pattern for corresponding age group categorised. In all, the paper provided an in-depth exploratory analysis of car crashes and their casualty patterns in London to facilitate deployment policies towards post-pandemic safer recovery upon COVID-19.

researchsquare; 2022.


The expression of m6A-related gene and its significance in COVID − 19 patients are still unknown. In the present study, we used the GSE177477 dataset of Gene Expression Omnibus to extract RNA-seq data of patients with COVID-19. The expression levels of 26 m6A-related genes between COVID-19 patients and healthy people were analyzed, thereby gaining 8 different expression genes. COVID-19 patients were classified into two categories, cluster A and cluster B, based on the expression level of the differential gene, which in this study happened to be the asymptomatic and symptomatic groups. In addition, differential expression of immune cells and the correlation with m6A-related genes was also examined in COVID-19 patients. Finally, 5 m6A-related disease characteristic genes, HNRNPA2B1, FTO, YTHDC1, HNRNPC, and WTAP, were screened by random forest and support vector machine algorithm. These five genes were applied to construct a nomogram model for predicting the risk of COVID-19. The calibration curve and decision curve analysis (DCA) show that the nomogram model is effective and has high net benefit. Then clinical impact curve analysis (CICA) revealed that the high-risk patients classified by the nomogram model had a high degree of coincidence with the actual positive patients. In summary, it has been found that m6A-related genes correlate with immune cells. The nomogram model effectively predicts the risk of COVID-19. And m6A-related genes may be associated with the presence or absence of symptoms in COVID-19 patients.

China City Planning Review ; 29(3):44-54, 2020.
Article in English | ProQuest Central | ID: covidwho-1391178


The outbreak of the novel coronavirus epidemic in early 2020 triggered off worldwide concerns for epidemic prevention as a coping strategy against public health crises. Facing the epidemic which has been characterized by wide spreading range and a long incubation period, communities have become the frontier in the war against it. At the same time, various problems have been exposed at the community level, including the lack of epidemic prevention planning, the high workload of community staff, and the insufficient public awareness on public health, etc. Focusing on the construction of Taiwan's community epidemic prevention system, this paper elaborates the significance and necessity of the community's participation in epidemic prevention based on a systematic literature review and policy study. It analyzes the restructuring and transition of a community epidemic prevention system and the primary strategies of community epidemic prevention planning, community health building, community health resources networking, community epidemic prevention practice, and the emergency planning in response to the novel coronavirus epidemic. It finally summarizes the methods of building a sustainable community epidemic prevention system.