Abstract: In order to accurately and effectively help the relevant departments at the higher level to accurately develop community-based COVID-19 outbreak prevention and control programs, we propose a framework for decision support in response to the current outbreak situation.In response to the actual needto help the government classify and determine the size of community safety risks from the perspective of the epidemic, a community epidemic risk assessment index system is proposed. To effectively assist decision-makers in developing outbreak prevention and control plans, this paper provides a new approach to risk factor assessment from the perspective of outbreak prevention and control as well as supporting decision-making. We develop epidemic risk assessment indicators for relevant communities with the aim of preventing the spread of the epidemic on a community basis, using FAHP as the criterion for calculating the indicator weights and will combine it with the FTOPSIS paradigm to calculate the true epidemic risk factor for the selected communities and an evaluation model is established by combining the fuzzy analytic hierarchy process and the fuzzy distance between superior and inferior solutions (FAHP-TOPSIS). The subjectivity and ambiguity of language variables in expert evaluation are expressed by triangular fuzzy numbers. The integrated fuzzy evaluation is obtained by taking A, B, C, and D communities as examples. Then, the corresponding community security risk value and ranking are calculated through the FAHP-FTOPSIS model.Through comparison and analysis, it is concluded that the model is more effective and accurate. This plan also provides a reference for the government to accurately implement policies, and prevent and control deployment.The effect of our proposed model has a greater advantage over the traditional decision model, as reflected in our ability to provide decision makers with certain decisions in the form of fine-grained indicators, and our effect is more effective than the traditional approach, solving the competent ambiguity brought about by the expert scoring of previous studies and reducing the bias brought about by subjectivity to a certain extent. Subsequent research can be further explored and studied for how to eliminate subjectivity,the method proposed in this paper makes certain exploration and assumptions for the field of epidemic prevention and control, and future research ideas can start from the overall decision-making. The research idea of dynamic selection through fuzzy sets and triangular fuzzy numbers is adjusted, and how to further explore in depth the new field of intelligent decision-making today, and the decision paradigm in future research is not necessarily limited to a specific field but experiments and research in multiple fields to truly achieve macroscopic global considerations for decision-makers.We alsobelieve that further studies can provide more accurate outbreak assessments with respect to the transmission pathways of the epidemic and the way the epidemic is traced, thus providing more comprehensive thoughts and ideal decision-makers effectively. Finally, we also believe that the follow-up study should consider how the different weights of indicators affect the assessment objectives as well as the assessment subjects and that the trade-off indicators regarding the model capabilities can be analyzed in depth because the assessment indicators of the results are not unique, and one indicator may lead to very different results, so the diversity of assessment instruments should also be one of the focuses of the follow-up study. The follow-up research will focus on intelligent decision-aided deployment to provide support and help for realistic and complex scenarios of deployment and control, which is also the focus and difficulty of the work, and has the possibility of intelligent decision-making.

Key words: epidemic prevention and control, community safety risk, FAHP-FTOPSIS, evaluation index system

摘要： 针对疫情视角下如何帮助政府划分及确定社区安全风险大小的现实需求,提出一种社区疫情风险评估指标体系,结合模糊层次分析法与模糊优劣解距离法(FAHP-TOPSIS)建立评估模型。通过三角模糊数表示专家评估中语言变量的主观性与模糊性,以某市4个社区为实例得到集成模糊评价,再通过FAHP-FTOPSIS模型计算得到相应社区安全风险值与排名,并通过对比与分析得出该模型的实效性、准确性更高,为政府精准施策、防控布控提供参考。

关键词: 疫情防控, 社区安全风险, FAHP-TOPSIS, 评估指标体系