Zhang Chao, Yang Yanzhao, Feng Zhiming, Xiao Chiwei, Liu Ying, Song Xinzhe, Lang Tingting
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.
Foods. 2022 Mar 22;11(7):908. doi: 10.3390/foods11070908.
Since the outbreak of the coronavirus disease 2019 (COVID-19), political and academic circles have focused significant attention on stopping the chain of COVID-19 transmission. In particular outbreaks related to cold chain food (CCF) have been reported, and there remains a possibility that CCF can be a carrier. Based on CCF consumption and trade matrix data, here, the "source" of COVID-19 transmission through CCF was analyzed using a complex network analysis method, informing the construction of a risk assessment model reflecting internal and external transmission dynamics. The model included the COVID-19 risk index, CCF consumption level, urbanization level, CCF trade quantity, and others. The risk level of COVID-19 transmission by CCF and the dominant risk types were analyzed at national and global scales as well as at the community level. The results were as follows. (1) The global CCF trade network is typically dominated by six core countries in six main communities, such as Indonesia, Argentina, Ukraine, Netherlands, and the USA. These locations are one of the highest sources of risk for COVID-19 transmission. (2) The risk of COVID-19 transmission by CCF in specific trade communities is higher than the global average, with the Netherlands-Germany community being at the highest level. There are eight European countries (i.e., Netherlands, Germany, Belgium, France, Spain, Britain, Italy, and Poland) and three American countries (namely the USA, Mexico, and Brazil) facing a very high level of COVID-19 transmission risk by CCF. (3) Of the countries, 62% are dominated by internal diffusion and 23% by external input risk. The countries with high comprehensive transmission risk mainly experience risks from external inputs. This study provides methods for tracing the source of virus transmission and provides a policy reference for preventing the chain of COVID-19 transmission by CCF and maintaining the security of the global food supply chain.
自2019年冠状病毒病（COVID-19）疫情爆发以来，政界和学术界高度关注阻断COVID-19传播链。特别是有报道称出现了与冷链食品（CCF）相关的疫情，且CCF仍有可能成为传播载体。基于CCF消费和贸易矩阵数据，本文采用复杂网络分析方法分析了通过CCF传播COVID-19的“源头”，为构建反映内外传播动态的风险评估模型提供依据。该模型包括COVID-19风险指数、CCF消费水平、城市化水平、CCF贸易量等。在国家和全球尺度以及社区层面分析了CCF传播COVID-19的风险水平和主要风险类型。结果如下：（1）全球CCF贸易网络通常由六个主要社区的六个核心国家主导，如印度尼西亚、阿根廷、乌克兰荷兰和美国。这些地区是COVID-19传播的最高风险源之一。（2）特定贸易社区中CCF传播COVID-19的风险高于全球平均水平，其中荷兰-德国社区风险最高。有八个欧洲国家（即荷兰、德国、比利时、法国、西班牙、英国、意大利和波兰）和三个美洲国家（即美国、墨西哥和巴西）面临CCF传播COVID-19的极高风险。（3）在这些国家中，62%以内部扩散为主，有23%面临外部输入风险。综合传播风险高的国家主要面临外部输入风险。本研究为追踪病毒传播源头提供了方法，为阻断CCF传播COVID-19链条、维护全球食品供应链安全提供了政策参考。
