The SARS-CoV-2 outbreak occurred in 2003, followed by a significant H1N1 swine flu pandemic in 2009 that started in Mexico. In 2012, Middle East respiratory syndrome (MERS) was identified in Saudi Arabia. The period from 2013 to 2016 saw the spread of the Ebola virus pandemic. In 2015, a Zika virus outbreak emerged. Most recently, in 2019, the COVID-19 pandemic originated in Wuhan, China (Li et al., 2023a). These pandemics not only pose a severe threat to global public health but also greatly disrupt normal social order, causing societal panic and having a substantial impact on socioeconomic development. The postpandemic era does not mean the complete disappearance of the pandemic, as the risk of transmission still exists and will continue to have profound effects on socioeconomic development. Therefore, how to effectively handle the joint prevention and control of COVID-19 and other common infectious diseases and minimize their impact on people's lives has become a new challenge in global public safety management.

Previous studies have shown that the majority of COVID-19 infections are transmitted by a small number of high-risk individuals in high-risk regions. These few superspreaders transmit the virus to a large number of people, playing a crucial role in the spread of the disease (Huang et al., 2021; Adam et al., 2020; Chang et al., 2021). Social networks can reveal the characteristics of pandemic transmission behavior, explore the structural features of transmission networks, and identify superspreaders and superspreading places, which are essential for controlling the spread of the disease. Virus transmission is caused by the behavioral activities of individuals with confirmed cases between cities and within cities. Individual behavioral activities are the foundation and key to pandemic prevention and control (Cheng et al., 2021). Spatial-time behavioral geography is a methodological approach that examines the interaction between human behavior and space-time. Utilizing high spatial-temporal resolution behavioral data helps to reveal the complex relationships between human behavior, pandemics, facilities, and other factors in the spatial-temporal domain, thereby improving the spatial-temporal precision of pandemic prevention and control (Chai et al., 2020).

Therefore, in this study, individual activity data from 82 confirmed cases in Lanzhou from October 19th, 2021, to November 9th, 2021, were obtained, and the transmission process of the pandemic was systematically analyzed by utilizing GIS spatial analysis, social network analysis and temporal geography methods. The aim of this study was to reveal the spatial-temporal characteristics of the pandemic at the street level, construct a pandemic transmission network to visualize the actual natural transmission chain of COVID-19, identify key information on pandemic transmission, and explore the space-time path of pandemic transmission. In summary, this paper has the following two main contributions. On the one hand, we used social network and behavioral trajectory methods to identify more detailed key information about the spread of the pandemic, which enhances people's understanding of the transmission of COVID-19. On the other hand, underdeveloped cities have relatively poor medical status, higher transmission risks, and greater difficulty in prevention and control. This study summarizes the relevant experience of underdeveloped cities in combating the COVID-19 pandemic, improving cities' ability to respond to major public health emergencies, and providing case-based experiences in the assessment of major emerging infectious diseases in other underdeveloped regions.