Preventing COVID-19 from the perspective of industrial information integration: Evaluation and continuous improvement of information networks for sustainable epidemic prevention.

Abstract

COVID-19 is accelerating industrial information integration (III) for sustainable epidemic prevention and innovation design. It is important to emphasize that this interaction makes it reciprocal. To prevent COVID-19, the III of industrial sectors should be strengthened to encourage innovation for sustainable epidemic prevention. Accordingly, we studied the overall dynamic change trend of industrial sectors' information integration networks (IIN), the characteristics of individual IIN, and their influence on IIN performance. In the study, the gravity model and social network analysis were used to determine the variables of industrial sectors' information distance and quality, and to construct the IIN of industrial sectors. The results show that the overall relevance of the IIN of industrial sectors is low, and the network density fluctuates, with high network efficiency and poor stability. Two-way, strong correlation between industrial sectors is relatively low. The spillover effect of industrial sectors in the upstream of the industrial chain is poor, and it is difficult to have a strong information integration driving effect on the downstream industrial sectors. The interplate linkage of the IIN of industrial sectors is insufficient. Compared with point centrality and closeness, improvement of the betweenness centrality of industrial sectors can significantly improve IIN performance.