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Computers & Industrial Engineering ; 176, 2023.
Article in English | Web of Science | ID: covidwho-2237651


Aiming at the complex and changeable environment and the low public participation in emergency decision making, this article proposes a method for the dynamic collaboration of the public and experts in large-scale group emergency decision-making (LSGEDM) based on social media data. First, sentiment analysis is carried out on text data from social media platforms to evaluate the quality of LSGEDM at both the attribute and comprehensive levels. Then, according to the decision-making quality at the attribute level, a method for the dynamic updating of attribute weights is proposed. Next, in the social network environment, the trust relationship between experts is dynamically updated based on the comprehensive quality of decision-making and the distance between the expert and group preferences, and expert weights are calculated by the improved PageRank algorithm. Finally, the effectiveness and superiority of the proposed method are verified via its application to the COVID-19 epidemic in China and a comparative analysis.

Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) ; 55(11):2194-2206, 2021.
Article in Chinese | Scopus | ID: covidwho-1551654


New methods of spatial allocation of medical resources should be investigated under the background of healthy cities and COVID-19 pandemic. Hangzhou was taken as an example and the improved Gaussian two-step floating catchment area method (2SFCA) was used to study the space allocation of public medical resources from the perspective of "supply to demand". And the theory of planned behavior (TPB) was used as represent of personal choice preference to study the influence of personal choice behavior on the medical resources from the perspective of "demand to supply". Results show that Hangzhou's medical resources have obvious central agglomeration, and the total amount of medical resources available to residents is related to whether the hospital's medical resources are run on and the extent. Meanwhile, it is proved that residents' personal choice has an impact on the allocation of urban medical resources. The analysis results can be the optimization to the medical facility distribution scheme of the current "Hangzhou Urban Medical and Health Facility Layout Planning (2016-2035)", which has analyzed the rationality and also improved the scientificity of the distribution scheme. © 2021, Zhejiang University Press. All right reserved.

Epidemiol Infect ; 148: e141, 2020 07 06.
Article in English | MEDLINE | ID: covidwho-633492


The pandemic of coronavirus disease 2019 (COVID-19) has posed serious challenges. It is vitally important to further clarify the epidemiological characteristics of the COVID-19 outbreak for future study and prevention and control measures. Epidemiological characteristics and spatial-temporal analysis were performed based on COVID-19 cases from 21 January 2020 to 1 March 2020 in Shandong Province, and close contacts were traced to construct transmission chains. A total of 758 laboratory-confirmed cases were reported in Shandong. The sex ratio was 1.27: 1 (M: F) and the median age was 42 (interquartile range: 32-55). The high-risk clusters were identified in the central, eastern and southern regions of Shandong from 25 January 2020 to 10 February 2020. We rebuilt 54 transmission chains involving 209 cases, of which 52.2% were family clusters, and three widespread infection chains were elaborated, occurring in Jining, Zaozhuang and Liaocheng, respectively. The geographical and temporal disparity may alert public health agencies to implement specific measures in regions with different risk, and should attach importance on how to avoid household and community transmission.

Coronavirus Infections/epidemiology , Coronavirus Infections/transmission , Pneumonia, Viral/epidemiology , Pneumonia, Viral/transmission , Adult , COVID-19 , China/epidemiology , Contact Tracing , Female , Geographic Information Systems , Humans , Male , Middle Aged , Pandemics , Time Factors