摘要
COVID-19疫情是进入21世纪以来最为严重的全球公共卫生事件,并成为不同学科共同关注的热点。根据文献计量学分析结果,从疫情开始直至近期,关于COVID-19疫情的文章已经超过13 000篇,相关研究除从医学及生物学角度探讨病毒致病机理、特效药物和疫苗研制之外,更多的是探索疫情的非药物防控方法。本文针对后者,从传播关系识别、疫情时空模式分析、疫情预测模型、疫情传播模拟、疫情风险评估和疫情影响评价6个方面梳理近期研究进展。传播关系识别的研究主要包括:聚集性疫情和传播关系的识别,其中,个体轨迹大数据已成为此类研究的关键。针对疫情的时空模式分析发现,疫情分布具有显著的时空异质性,而时空传播则呈现出典型的网络特征。针对疫情的预测仍主要依赖于动力学模型,而从宏观到微观的预测模型,人群流动的影响不容忽视,并成为模型预测精度的关键要素之一。针对疫情的情景模拟主要侧重于通过模拟手段评估交通限制、社区防控和医疗资源调配等措施的效果。在非药物的干预中,交通阻断和社区防控措施被证明是目前最有效的手段;医疗资源的保障和优化调配则是防控的基础;而复工复产的情景模拟显示,在防控措施到位的情况下,复工进程必须有序有节。针对疫情风险评估的研究,目前主要关注生物因素、自然因素和社会因素。具体地,疫情感染风险与个体是否具有基础性疾病关系密切,而感染病毒后的死亡率存在性别差异;在自然因素中,如温度、降水、气候等会影响疫情的传播,但影响有限;而社会因素中,除了人群流动和人口密度的影响外,社会不公平性所导致的就医条件差异也会对感染率产生显著影响。针对疫情对未来的影响,本文主要关注公众心理、自然环境和经济发展3个方面,即疫情对公众心理和经济的影响主要以负面为主,而对自然环境的影响则起正向作用。通过对现有研究的系统梳理,可以看出,大数据尤其是个体轨迹和群体大数据在非药物干预的各个方面均发挥了重要的作用;重大疫情的防控已经不是单一学科和手段所能解决的问题,需要多学科的交叉以及不同领域人员的协作;疫情期间各种防控措施的效果、影响因素等均已被明确的揭示,但疫情的空间溯源、精准预测以及对未来的影响仍然是未解的难题。
The COVID-19 pandemic is the most serious global public health event since the 21 stcentury, and has become a hot topic concerned by different disciplines. According to the bibliometric analysis, more than 13,000 papers related to the COVID-19 have been published since the beginning of the pandemic. Related researches include not only the pathogenic mechanism of the virus and the development of specific drugs and vaccines from the medical and biological perspectives, but also the non-pharmaceutical prevention and control methods for the pandemic. The latter is the focus of this paper, in which the research progress on the pandemic is discussed from six aspects: detection of transmission relationships, spatiotemporal pattern analysis, prediction models, spread simulation, risk assessment, and impact evaluation. The research on the detection of transmission relationship mainly includes the detection of cluster cases and transmission relations, among which individual trajectory big data have become the key to research. The progress of the analysis of spatiotemporal patterns of the pandemic shows that the spatiotemporal distribution of the pandemic has significant temporal and spatial heterogeneity,and the spatiotemporal transmission presents typical network characteristics. The prediction of the pandemic mainly relies on dynamic models scaling from macro to micro, in which the non-negligible impact of population migration makes the human flow big data become one of the key elements of model prediction accuracy. In the study of epidemic spread simulation, the focus is on evaluating the effects of controlling measures such as traffic restrictions, community prevention and control, and medical resources allocation through simulation methods.Results show that traffic interruption and community control measures are the most effective means among nonpharmaceutical interventions at present, and the guarantee and reasonable deployment of medical resources are the basis for pandemic prevention and control. After the pandemic is controlled under the effective measures, the resumption of work and production must be in an orderly manner. The research on pandemic risk assessment currently focuses on biological factors, natural factors and social factors. As to biological factors, researches show that the underlying disease and the male(due to their high mobility) are related to a higher risk of infection. Among natural factors, temperature, precipitation and climate have limited influence on the spread of the pandemic. As to social factors, human mobility, population density, and differences in medical conditions caused by social inequity have significant influences on the infection rate. Regarding the impact of the COVID-19 pandemic, we mainly focus on three aspects: the public psychology, natural environment and economic development. Specifically, the impact of the pandemic is mainly negative on the public psychology and economy, and positive on the natural environment. In conclusion, big data especially individual trajectories and population big data are indeed pervasive in research of non-pharmaceutical intervention. To prevent and control the major outbreaks, the intersection of multiple disciplines and the collaboration of personnel in different fields are indispensable. Although a great progress has been made on various aspects such as the effect of controlling measures and the influencing factors of the pandemic, the spatial traceability, precise prediction and future impact of the pandemic are still unsolved problems.
作者
裴韬
王席
宋辞
刘亚溪
黄强
舒华
陈晓
郭思慧
周成虎
PEI Tao;WANG Xi;SONG Ci;LIU Yaxi;HUANG Qiang;SHU Hua;CHEN Xiao;GUO Sihui;ZHOU Chenghu(State Key Laboratory of Resources and Environmental Information System,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China;University of Chinese Academy of Sciences,Beijing 100049,China)
出处
《地球信息科学学报》
CSCD
北大核心
2021年第2期188-210,共23页
Journal of Geo-information Science
基金
国家自然科学基金项目(42041001、41525004、41421001)。
关键词
新型冠状病毒肺炎
文献计量学分析
传播关系
时空模式
疫情预测
传播模拟
风险评估
疫情影响评估
COVID-19
bibliometric analysis
transmission relationships
spatiotemporal pattern
prediction models
spread simulation
risk assessment
impact evaluation for the epidemic
