1. Introduction

The prevention and treatment of public health emergencies is no longer a simple medical technology issue, but a complex global governance issue. Starting from the case of COVID-19 prevention and control in China, it is of great practical significance to explore the realization process of community-based prevention and control of public health empowered by big data under the guidance of public institutions and clarify the basic characteristics and governance path of public health prevention and control mode of grassroots communities under the established political framework.

2.Research Methods and Analytical Framework

2.1. Research Methods

In terms of method selection, the research mainly adopts the methods of text analysis and limits-to-growth archetype analysis. By extracting information from the cases of community-based prevention and control capacity building, and in the stage of integration and refinement of coding-based conceptual categories, government officials and relevant experts are invited to test and demonstrate the axis codes one by one to ensure the objectivity of the results. At the same time, the limits-to-growth model of the constraints on the improvement of community-based prevention and control capacity of public health events is constructed, and an analytical framework for the generation of community-based prevention and control capacity of public health events from the perspective of optimization of governance elements is formed.

2.2 Analytical Framework Construction

Based on the feedback model in system dynamics, this study attempts to establish the limits-to-growth archetype of the constraints on the improvement of community-based prevention and control capacity of public health events and form an analytical framework for the generation of community-based prevention and the control capacity of public health events from the perspective of optimization of governance elements.

The community-based prevention and control system for public health emergencies is characterized by multiple feedback loops and hysteresis effects of system dynamics. The “limits-to-growth” archetype system mainly includes the “reinforcing feedback” of the bureaucratic coordination model under the decision logic and the “regulatory feedback” of the technology-driven model under the creation logic. Taking the COVID-19 outbreak in 2020 as an example, China adopted extensive political mobilization in epidemic prevention and control and realized an efficient response in the initial stage of epidemic prevention and control through vertical centralized management, which fully reflected the institutional advantages of Chinese governance with Chinese efficiency. However, with the development of the epidemic showing a strong trend of being latent and wide spread, the “reinforcing feedback” from the bureaucratic coordination model on the effect of epidemic prevention and control encounters the “regulatory feedback” that inhibits the growth at the technology-driven level. The research believes that when there is a limits-to-growth structure in the system, it is useless to promote the “reinforcing feedback” loop, and the reason should be found in the negative feedback loop to eliminate the constraints of the negative feedback loop so as to continuously enhance the positive feedback.

3.Discussion and Conclusions

3.1 Discussion

At the theoretical level, this study explores the generation process and internal mechanism of community-based prevention and control capacity of public health events from a new research perspective of governance element optimization.

Firstly, different community-based prevention and control capacities generated by different governance models of public health events are explored based on different stages of prevention and control field.

Secondly, from the perspective of the internal mechanism of the formation of data-driven community-based public health prevention and control capacity, a new idea of data-driven embedded bureaucratic coordination to improve governance capacity is proposed.

3.2 Conclusions

The path to improve the data-driven community prevention and control capacity of public health events is the foothold of the study. The transition from bureaucratic coordination to a data-driven prevention and control model requires the transformation of digital thinking, the sharing of multi-party resources and the use of information tools. Through the data-driven model, the benign interaction between government management, social governance and residents’ self-governance under the community prevention and control of public health events can be realized. The building of data-driven community-based prevention and control capacity of public health events needs to seek breakthroughs in three stages: foundation building, governance empowerment and value enhancement