Background: In a fast-evolving 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 a different impact on the transmission and cross-propagation of the old and new information that results in a different peak value and a 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 aims to help in designing effective communication strategies to ensure information is delivered to the maximal number of users. Methods: We developed and analyzed 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 were posted 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 a publishing strategy were analyzed. Results: The current epidemic outbreak situation, epidemic prevention, and other related authoritative information cannot be timely and effectively browsed by the public. The ingenious use of information release intervals can effectively enhance the interaction between information and realize the effective diffusion of information. We parameterized our models using real data from Sina Microblog and used the parameterized models to define and evaluate mutual attractiveness indexes, and we used these indexes and parameter sensitivity analyses to inform optimal strategies for new information to be effectively propagated in the microblog. The results of the parameter analysis showed that using different attractiveness indexes 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 an information communication strategy. At the same time, the dynamic process of information was analyzed through index evaluation. Conclusions: Our model can carry out an 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. This study optimizes information posting strategies to maximize communication efforts for delivering key public health messages to the public for better outcomes of public health emergency management.

This is the abstract only. Read the full article on the JMIR site. JMIR is the leading open access journal for eHealth and healthcare in the Internet age.

中文翻译：

---

量化延迟对COVID-19信息传播中意见传递的影响：建模研究

背景：在迅速发展的公共卫生危机（例如COVID-19大流行）中，可以在社交媒体平台上顺序发布多条相关信息。后续发布时间之间的时间间隔可能会对旧信息和新信息的传输和交叉传播产生不同的影响，从而导致新信息的转发用户的峰值和最终大小不同，具体取决于内容相关性以及是否新信息将在旧信息的爆发或准稳态阶段发布。目的：本研究旨在帮助设计有效的交流策略，以确保将信息传递给最大数量的用户。方法：我们开发并分析了两类具有传播延迟的易感性免疫信息传播模型，以描述相关信息的交叉传播过程。每个意见领袖与COVID-19相关的意见领袖经常发布了28,661条典型信息的转发，影响力很大（数据获取截止到2020年2月19日）。信息以10分钟的频率被处理成离散点，并通过模型数值模拟拟合实际数据。此外，分析了参数对信息传播的影响以及发布策略的设计。结果：公众无法及时有效地浏览当前的流行病爆发情况，流行病预防和其他相关权威信息。巧妙地利用信息发布间隔可以有效地增强信息之间的交互作用，实现信息的有效传播。我们使用来自新浪微博的真实数据对模型进行参数化，并使用参数化模型定义和评估相互吸引力指标，然后使用这些指标和参数敏感性分析为在微博中有效传播新信息提供最佳策略。参数分析结果表明，采用不同的吸引力指标作为关键参数可以控制信息在不同释放间隔下的传递，因此被认为是信息通信策略设计中的关键环节。同时，通过指标评价对信息的动态过程进行了分析。结论：通过构建指标体系，我们的模型可以对不同发布间隔的信息进行精确的数值模拟，并实现对信息传递的动态评估，从而为政府决策提供理论支持和战略建议。这项研究优化了信息发布策略，以最大程度地进行沟通，以向公众传递关键的公共卫生信息，从而实现公共卫生应急管理的更好结果。

这仅仅是抽象的。阅读JMIR网站上的全文。JMIR是互联网时代电子健康和医疗保健领域领先的开放获取期刊。