Characterizing the Dynamic Evolution of Interagency Collaborative Decision-Making Networks in Response to COVID-19 in China: A Policy Document Analysis.

Collaborative decision-making across multiple government agencies is considered a critical and effective strategy to combat public health crisis; however, we know little about how the collaborative decision-making works and evolves during periods of crisis. To fill this lacuna, this study uncovers the structure and evolving dynamics of the network by employing a policy document analysis. Based on the policy documents, jointly issued by the agencies of Chinese central government in four phases regarding COVID-19 control, we first constructed a co-occurrence matrix of policy-issuing agencies to outline the network structure, then drew a breadth-depth matrix to identify the role evolution of agencies, and lastly built a two-mode network consisting of policy topics and agencies to determine the evolution mechanisms of policy attentions for each agency. It was found that the network structure of interagency collaboration involves three forms: discrete structure in the early phase, subgroup structure in the middle phase, and connected structure in the latter phase. Agencies embedded in the network can be categorized into three types: leading agencies, key agencies, and auxiliary agencies, with their constituent members changed as the pandemic risks are gradually becoming under control. Furthermore, each type has its own primary policy attentions, but shares some common foci in all four phases and shifts attention in the emergency management process. This study contributes to shedding light on the formation of and variations in collaborative networks in health emergencies and provides policy implications for other countries that have struggled against COVID-19.

Understanding the Evolution of Government Attention in Response to COVID-19 in China: A Topic Modeling Approach.

The effective control over the outbreak of COVID-19 in China showcases a prompt government response, in which, however, the allocation of attention, as an essential parameter, remains obscure. This study is designed to clarify the evolution of the Chinese government's attention in tackling the pandemic. To this end, 674 policy documents issued by the State Council of China are collected to establish a text corpus, which is then used to extract policy topics by applying the latent dirichlet allocation (LDA) model, a topic modelling approach. It is found that the response policies take different tracks in a four-stage controlling process, and five policy topics are identified as major government attention areas in all stages. Moreover, a topic evolution path is highlighted to show internal relationships between different policy topics. These findings shed light on the Chinese government's dynamic response to the pandemic and indicate the strength of applying adaptive governance strategies in coping with public health emergencies.