ABSTRACT
COVID-19 continues to spread and spread, and the epidemic situation in China and Japan is not optimistic, which brings huge hidden dangers and serious impact to the economic cooperation and development of China and Japan. Aiming at the comments and feedback of Chinese and Japanese people on government policies, this paper proposes a text emotional tendency analysis method based on maximum entropy model, which extracts word relation features and semantic features from the comment context. The maximum entropy model is used to identify the emotional tendency of words, and the smoothing technique is used to solve the problem of sparse features. At the same time, through the weighting and normalization of specific emotional words and punctuation marks, the uncertainty of the emotional tendency of words in the context is further reduced, and the experimental results verify the effectiveness of the model.
ABSTRACT
Objective: To investigate and analyze the epidemiological characteristics of a cluster of coronavirus disease 2019 (COVID-19) spread by a super spreader in Taizhou, Zhejiang Province, and provide reference for prevention and control of COVID-19. Methods: The field epidemiological investigation was conducted to investigate the confirmed cases and close contacts;the data were analyzed with descriptive method. Chi-squared test was used to compare the differences of attack rates among close contacts. Results: A total of 23 epidemiological related cases were identified, including 20 confirmed cases and 3 asymptomatic infection cases. Thirteen (56.52%) were males, ten (43.48%) were females, and the median age was 51 years old with the range of 30 to 70 years old. The second generation case firstly developed symptoms on Jan 19th and was confirmed on Jan 30th, 2020.The first generation cases were from Wuhan and the last case was confirmed on Feb 3rd, 2020.The epidemic spread to the fifth generation with a total attack rate of 6.07% (21/346), among which the third generation case was a super spreader who directly transmitted to 12 subsequent cases with a significantly higher attack rate than other cases (27.27% vs. 2.98%, χ2=39.754, P<0.001). Conclusion: The expansion of the epidemic can be attributed to the lack of timely control of imported personnel from high risk regions, the lacking awareness of novel infectious diseases at the early stage of the epidemic and the appearance of the super spreader. To form a normalized prevention mechanism, it is necessary to improve the alertness of novel infectious diseases among medical staffs and the masses, implement prevention and control strategies in time. © 2021, Editorial Department of Fudan University Journal of Medical Sciences. All right reserved.
ABSTRACT
Objective: To describe the COVID-19 epidemic and its characteristics in Heilongjiang province, and provide evidence for the further prevention and control of COVID-19 in the province. Methods: The information of COVID-19 cases and clusters were collected from national notifiable disease report system and management information system for reporting public health emergencies of China CDC. The Software's of Excel 2010 and SPSS 23.0 were applied for data cleaning and statistical analysis on the population, time and area distributions of COVID-19 cases. Results: On January 22, 2020, the first confirmed case of COVID-19 was reported in Heilongjiang. By March 11, 2020, a total of 482 cases domestic case of COVID-19, The incidence rate was 1.28/100 000, the mortality rate was 2.70% (13/482) in 13 municipalities in Heilongjiang. There were 81 clusters of COVID-19, The number of confirmed cases accounted for 79.25% (382/482) of the total confirmed cases and 12 cases of deaths. The family clusters accounted for 86.42% (70/81). Compared with the sporadic cases, the mortality rate, proportion of elderly cases aged 60 or above and severe or critical cases of clinical classification were all higher in the clusters especially the family clusters, but the differences were not significant (P>0.05). There were 34 clusters involving more than 5 confirmed cases accounted for 41.98% (34/81) of the total clusters, the involved cases accounted for 68.31% (261/382) of the total cases of clusters. There were significant differences in age distribution of the cases among the case clusters with different case numbers. In the clusters involving 6-9 cases, the proportion of cases aged 65 years or above was more (26.53%, 39/147). Conclusions: The incidence rate of COVID-19 was relatively high and the early epidemic was serious in Heilongjiang, The number of cases was large in clusters especially family clusters.