Hybrid Modeling of Anxiety Propagation in Response to Threat Stimuli Flow

Abstract

Previous studies have demonstrated that the rates of anxiety have been constantly increasing worldwide in recent years. To understand this phenomenon, based on the complemented cognitive model TVAPA of anxiety, the hybrid method of modeling and simulating the dynamics of anxiety in the population is proposed. The suggested method combines agent-based modeling, dynamic systems modeling with differential equations, and machine learning methods. The four-level STAI methodology is applied to assess anxiety in the proposed models. Sentiment analysis of social media content is used to identify the parameters of triggering stimuli flow. The proposed models were implemented and verified using open access data sets. Created models are characterized by simplicity, and the parameters used in them have a clear socio-informational meaning. The developed models can be calibrated by applying statistical methods according to indicators of anxiety measured at discrete sets of time intervals by associating them with parameters of the threat stimuli flow taken from statistical data and/or Internet content tracking data.