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Quantify Influence of Delay in Opinion Transmission of Opinion Leaders on COVID-19 Information Propagation in the Chinese Sina-microblog.

BACKGROUND
In a fast-evolving major public health crisis such as the COVID-19 pandemic, multiple pieces of relevant information can be posted sequentially on a social media platform. The interval between subsequent posting times may have different impact on the transmission and cross-propagation of the old and new information to result in different peak value and final size of forwarding users of the new information, depending on the content correlation and whether the new information is posted during the outbreak or quasi steady-state phase of the old information.


OBJECTIVE
This study aimed to help in designing effective communication strategies to ensure information is delivered to the maximal number of users.


METHODS
We develop and analyze two classes of susceptible-forwarding-immune information propagation models with delay in transmission, to describe the cross-propagation process of relevant information. A total of 28,661 retweets of typical information which were posted extremely frequently by each opinion leader related to COVID-19 with high influence (Data-acquisition up to February 19, 2020). The information was processed into discrete points with a frequency of 10 minutes, and the real data were fitted by the model numerical simulation. Furthermore, the influence of parameters on information dissemination and the design of publishing strategy are analyzed.


RESULTS
The current epidemic outbreak situation and epidemic prevention and other related information overloads, leading to authoritative information can not be timely and effectively browsed by the public attention. The ingenious use of information release intervals can effectively enhance the interaction between information and realize the effective diffusion of information. We parameterize our models using real data from the Sina-Microblog and use the parameterized models to define and evaluate mutual attractiveness indices, and we use these indices and parameter sensitivity analyses to inform strategies to ensure optimal strategies for new information to be effectively propagated in the microblog. The results of parameter analysis show that different attractiveness indices as the key parameters can control the information transmission with different release intervals, so it is considered as a key link in the design of information communication strategy. At the same time, the dynamic process of information is analyzed through index evaluation.


CONCLUSIONS
Our model can carry out accurate numerical simulation of information at different release intervals, and achieve a dynamic evaluation of information transmission by constructing an indicator system, so as to provide theoretical support and strategic suggestions for government decision-making. The study optimizes information posting strategies to maximize communication efforts to deliver key public health messages to the public for better outcomes of public health emergency management.