Research on the collaborative evolution process of information in public health emergencies based on complex adaptive system theory and social network analysis: a case study of the COVID-19 pandemic - Lv, Kun; Luo, Xingyu; Shan, Jiaoqiao; Guo, Yuntong; Xiang, Minhao (Frontiers in Public Health, 2023)
The efficacy of information collaboration holds a significant position in the prevention and control of public health emergencies. The process of uncovering information collaboration patterns under these circumstances can enhance information processing efficiency, consequently delivering valuable insights to boost emergency management capabilities. The present study initiates with the theory of complex adaptive systems, proposing an evolutionary pathway for information collaboration during public health emergencies. Time-slicing techniques and social network analysis are applied to transpose this dynamic evolution into a stage-based static depiction. The law of evolution is assessed via cohesive subgroup analysis within the information collaboration network. The research incorporates data accumulated between January and April 2020, concentrating on the evolution of information collaboration during the COVID-19 pandemic. Python was utilized to compile data from a variety of sources, such as government portals, public commentary, social organizations, market updates, and healthcare institutions. Following data collection, social network analysis techniques were implemented to investigate the network structures, collaboration objectives, and participant entities within each time slice. The study revealed that the law of evolution for information collaboration in public health emergencies primarily exhibits small-scale information collaboration at the onset, escalates to full-scale information collaboration in the middle phase, and reverts to small-scale information collaboration in the final stage. The network's complexity initially amplifies before gradually diminishing over time, corresponding to modifications in collaboration tasks, objectives, and strategies.