Predicting multi-subsequent events and actors in public health emergencies: : An event-based knowledge graph approach

Public health emergencies trigger series of chain reactions that have devastating impacts on society. In addition, the subsequent events and actors in public health emergencies represent comprehensive emergency scenarios. Taking this information into account, predicting subsequent events and actors could motivate governments to take necessary and effective countermeasures. Therefore, we develop a model for predicting subsequent events and potential actors, i.e., a subsequent multievent graph convolutional network (SMEGCN), by utilizing the evolutionary information of events. Specifically, we take both relational information and semantic information into consideration to achieve improved prediction performance and simultaneously predict subsequent actors in a convenient manner. Specifically, we collect data from the Sina microblog concerning the COVID-19 pandemic to form five news datasets by employing a Python-based agent to practically test the performance of our model. The agile principle is applied to identify and handle a series of subsequent events and potential actors. The results show that embedding relational information, semantic information, and context inferences into the prediction model can improve the model performance by approximately 20%. Additionally, a comparative analysis indicates that the SMEGCN model is superior to other methods in terms of predicting both subsequent events and actors. From the perspective of an example analysis, social media, especially official media accounts, stimulates interactions between governments and the public and improves the management effectiveness of governments. However, during periods of emerging public health emergencies, the most important events that should be noted diachronically are treatment, daily life guarantees, and acute and chronic disease treatments, whereas at the end of a public health emergency, the key tasks include how to revive commercial activities and improve the vaccination rate. Drawing conclusions from these discussions, the present study not only contributes to the literature on theoretically and methodologically predicting events and actors but also provides practical suggestions for managing public health emergencies.