Modeling of anxiety spread during the pandemics

Abstract

The study aims to develop a hybrid model of pandemic anxiety and panic dynamics, calibrated through indirect social anxiety and emotion level indicators, in order to apply it to the analysis, forecasting and management of anxiety and related panic scenarios. The developed model combines agent modeling, dynamic systems modeling with differential equations and machine learning methods. Because direct investigation of the prevalence and spread of anxiety and related panic is a complex task, coronavirus morbidity data and indirect anxiety indicator data will be used to calibrate and verify the developed model, which will be processed by machine learning algorithms. One of the possible reflections of public anxiety is the web content. The sentiment analysis will identify and extract subjective information about anxiety and emotional level from the web media content. In this way, the application of machine learning methods would help to identify additional variables and their properties in the development of a hybrid model.