基于个体“移动—接触”的空间交互网络理论构建与疫情风险评估

个体在空间移动过程中不可避免地产生“人与人”之间的接触,使疫情传播具有复杂性和空间不确定性。但现有学术研究较少在理论上综合考虑个体的空间移动及移动过程中近距离接触分析疫情的空间扩散。本文综合考虑个体移动及移动过程中接触,提出基于个体“移动—接触”的空间交互网络的理论构建框架。鉴于公交刷卡数据能够有效地反映个体的移动路径及车厢内接触的群体,以北京市公交系统为例开展实证研究,构建基于个体“移动—接触”的空间交互网络,采用加权度中心性和K-shell分解方法识别疫情高风险区域,提出管控措施,并通过模拟管控措施前后网络社团结构变化来评价管控措施实施效果。结果发现疫情高风险区域集中分布在城际交通枢纽、商务中心、居住区周边区域。本文提出的理论框架对基于各类交通系统的疫情风险评估具有普适性,研究结果可为突发公共卫生事件中及时启动有效的疫情防控应急响应政策具有借鉴作用。

Identifying high-risk areas of airborne disease in "movement-contact" network

Transportation is a necessary way to realize human mobility. Spaces in transportations, such as buses and subways, are confined and crowded, which could induce a large volume of physical contact between passengers and thus promotes disease spread between areas with no human connection. Mobility and physical contact are widely regarded as two critical components in disease spread over space. However, current studies only considered a single aspect, human mobility or physical contact, in identifying high-risk areas of airborne disease. Without comprehensive consideration of human mobility and physical contact, health planners may misidentify areas with a high risk of airborne disease and design less-effective interventions. To fill the gaps, this study first proposed a theoretical framework that comprehensively considers human movement and physical contact during movement to construct a "movement-contact". Using public transit system in Beijing, we built "movement-contact" network with the Thiessen polygon centered by bus station as the node and the number of individuals moved or contacted as weight. Then, the weighted degree centrality of each node was calculated. Further, the K-shell decomposition algorithm was used to divide the importance degree of each node, thereby extracting the nodes (areas) with a high risk of disease transmission. Accordingly, we offer some suggestions to control the spread of the disease. Communities, despite being the basic unit of human activity, are rarely used to evaluate the effectiveness of disease control measures. To fill the gap, the effectiveness of control measures was evaluated by comparing the modularity of community structure before and after taking control measures. The results showed that there were three types of high-risk areas. The first type is the intercity transportation hubs, such as Beijing Capital International Airport, Beijing South Railway Station, and Beijing West Railway Station. The second type is some residential areas, e.g. Qinghe, Shahe, and Anhuiqiao. The third type is the business centers represented by Wangjing, Sanlitun, and Guomao. Based on the findings, this study suggests stopping bus services in high-risk areas to impede the spread of disease. After taking control measures, the modularity of the community structure reduced significantly. This study is of great significance for identifying high-risk areas of airborne disease, developing prevention measures for disease transmission, and evaluating the effectiveness of control measures. It provides some suggestions to prevent the spread of disease in a timely manner under the promise of guaranteeing the normal life of residents.