It is of great importance to accurately assess the safety risk of chemical contaminants in meat products as one of the causes of food safety problems, which could provide a basis for food safety supervision. Considering the advantages and disadvantages of the existing risk assessment methods, a risk assessment method of chemical contaminants in meat products based on the improved matter-element extension model was proposed, which takes into account the maximum limit of each contaminant and the statistical distribution pattern of sampling results, and selects the detection rate, failure rate, failure degree, mean contents and coefficient of variation of contaminants as risk evaluation indexes. In addition, the weight of each evaluation index was determined by introducing the best-worst method, and the risk level of each contaminant was determined from the asymmetric closeness calculated based on the improved matter-element extension model. Moreover, a case study was conducted with the sampling results of five food additives and four heavy metal elements in meat products in a certain province of China in 2018, and a comparative experiment was conducted with the commonly used Nemerow index evaluation method. The results of both methods showed basically identical rank order of the nine chemical contaminants, and the results of the method developed in this study were distributed at level Ⅰ-Ⅴ, while those of the Nemerow index method were concentrated at level Ⅲ-Ⅴ. Our method can effectively highlight the difference in the risk of different contaminants, and the results from it comply more with reality, which provide a scientific basis for the development of pollutant sampling plans and the optimization of detection resources.

The visual modeling method enables flexible interactions with rich graphical depictions of data and supports the exploration of the complexities of epidemiological analysis. However, most epidemiology visualizations do not support the combined analysis of objective factors that might influence the transmission situation, resulting in a lack of quantitative and qualitative evidence. To address this issue, we deve-loped a portrait-based visual modeling method called +msRNAer. This method considers the spatiotemporal features of virus transmission patterns and multidimensional features of objective risk factors in communities, enabling portrait-based exploration and comparison in epidemiological analysis. We applied +msRNAer to aggregate COVID-19-related datasets in New South Wales, Australia, combining COVID-19 case number trends, geo-information, intervention events, and expert-supervised risk factors extracted from local government area-based censuses. We perfected the +msRNAer workflow with collaborative views and evaluated its feasibility, effectiveness, and usefulness through one user study and three subject-driven case studies. Positive feedback from experts indicates that +msRNAer provides a general understanding for analyzing comprehension that not only compares relationships between cases in time-varying and risk factors through portraits but also supports navigation in fundamental geographical, timeline, and other factor comparisons. By adopting interactions, experts discovered functional and practical implications for potential patterns of long-standing community factors regarding the vulnerability faced by the pandemic. Experts confirmed that +msRNAer is expected to deliver visual modeling benefits with spatiotemporal and multidimensional features in other epidemiological analysis scenarios.