Influencing Factors of Environmental Risk Perception during the COVID-19 Epidemic in China.

The spread of COVID-19 is having a serious impact on socioeconomic development, and increased environmental risk perception (ERP). ERP provide new ideas for the orderly recovery of society. However, there have been studies that often pay attention to individual factors, and less concerned about the external environment. In fact, ERP will be affected by the external environment and individual factors. We used a Python script to collect 65,277 valid Weibo comments during the COVID-19 epidemic in China to assess urban residents' environmental risk perception (ERP). SnowNLP emotion analysis was used to measure the ERP of 366 urban in China, and the structural proportion characteristics and spatial-temporal differentiation of ERP were analyzed. Then, an order logistic regression model was used to investigate the relationship between economic level, social security, medical facilities and ERP. The study investigated the Chinese cities have a higher ERP during the COVID-19 period, and it shows marked fluctuations. As COVID-19 spreads, the ERP shows a distribution pattern of "high in the southeast and low in the northwest" with Hu line as the boundary and "from high to low" with Wuhan as the high value center. COVID-19 serves as catalysts for ERP, the impact of COVID-19 is enhanced after socioeconomic factors are considered. The economic level effectively regulates ERP, except the stage of accelerating diffusion. ERP is effectively stabilized by social security and medical facilities. After considering all the variables simultaneously, we found that the mitigation effect of social security and medical facilities on ERP has improved.

The Spatio-Temporal Characteristics and Influencing Factors of Covid-19 Spread in Shenzhen, China-An Analysis Based on 417 Cases.

The global pandemic of COVID-19 has made it the focus of current attention. At present, the law of COVID-19 spread in cities is not clear. Cities have long been difficult areas for epidemic prevention and control because of the high population density, high mobility of people, and high frequency of contacts. This paper analyzed case information for 417 patients with COVID-19 in Shenzhen, China. The nearest neighbor index method, kernel density method, and the standard deviation ellipse method were used to analyze the spatio-temporal characteristics of the COVID-19 spread in Shenzhen. The factors influencing that spread were then explored using the multiple linear regression method. The results show that: (1) The development of COVID-19 epidemic situation in Shenzhen occurred in three stages. The patients showed significant hysteresis from the onset of symptoms to hospitalization and then to diagnosis. Prior to 27 January, there was a relatively long time interval between the onset of symptoms and hospitalization for COVID-19; the interval decreased thereafter. (2) The epidemic site (the place where the patient stays during the onset of the disease) showed an agglomeration in space. The degree of agglomeration constantly increased across the three time nodes of 31 January, 14 February, and 22 February. The epidemic sites formed a "core area" in terms of spatial distribution and spread along the "northwest-southeast" direction of the city. (3) Economic and social factors significantly impacted the spread of COVID-19, while environmental factors have not played a significant role.

Risk factors associated with occurrence of COVID-19 among household persons exposed to patients with confirmed COVID-19 in Qingdao Municipal, China.

Tracing and isolation of close contacts is used to control outbreaks of coronavirus disease 2019 (COVID-19) in China. However, risk factors associated with the occurrence of COVID-19 among close contacts have not been well described. A total of 106 household contacts were included in this study, of whom 19 developed into COVID-19 cases, and the secondary attack rate was 17.9%. Multivariable analysis showed that increasing risk of occurrence of COVID-19 among household contacts was associated with female index patients (adjusted hazard ratio [aHR] = 3.84, 95% CI = 1.07-13.78), critical disease index patients (aHR = 7.58, 95% CI = 1.66-34.66), effective contact duration with index patients > 2 days (aHR = 4.21, 95% CI = 1.29-13.73), and effective contact duration > 11 days (aHR = 17.88, 95% CI = 3.26-98.01). The sex and disease severity of index patients with COVID-19 and longer effective contact duration with patients with confirmed COVID-19 could help epidemiologists to identify potential COVID-19 cases among household contacts at an early stage.

Epidemiological Characteristics on the Clustering Nature of COVID-19 in Qingdao City, 2020: A Descriptive Analysis.

OBJECTIVES: As an emerging infectious disease, COVID-19 has involved many countries and regions. With the further development of the epidemic, the proportion of clusters has increased. METHODS: In our study, we collected information on COVID-19 clusters in Qingdao City. The epidemiological characteristics and clinical manifestations were analyzed. RESULTS: Eleven clusters of COVID-19 were reported in Qingdao City between January 29, and February 23, 2020, involving 44 confirmed cases, which accounted for 73.33% of all confirmed cases. From January 19 to February 2, 2020, the cases mainly concentrated in the district that had many designated hospitals. Patients aged 20-59 y old accounted for the largest proportion (68.18%) of cases; the male-to-female sex ratio was 0.52:1. Three cases were infected from exposure to confirmed cases. The average incubation period was 6.28 d. The median number of cases per cluster was 4, and the median duration time was 6 d. The median cumulative number of exposed persons was 53. CONCLUSION: More attention should be paid to the epidemic of clusters in prevention and control of COVID-19. In addition to isolating patients, it is essential to track, screen, and isolate those who have come in close contact with patients. Self-isolation is the key especially for healthy people in the epidemic area.