In this work, we develop a systemic approach to the study of a new model of COVID-19 pandemic. The main goal is to minimize the pandemic damage to economy and society by defining the model optimal management parameters. Our approach consists of two main parts: 1) the adaptive-compartmental model of the epidemic (ACM-SEIR) – a generalization of the classical SEIR model and 2) the module to tune ACM-SEIR parameters using artificial intelligence methods (collection, storage and processing of big data from heterogeneous sources) that allow the most accurate adjustment of ACM-SEIR parameters turning it into an intelligent system for decision support called herein iACM-SEIR. We show that among iACM-SEIR parameters, the most important are individual economic, demographic and psychologic characteristics of society and the governmental actions.
Purpose The COVID-19 pandemic is creating serious challenges for modern society that leads to develop new information models and methods of digital monitoring not only of the spread of the virus, but also of the socio-economic environment. Materials and methods: As sources for clarifying the parameters of such models, it is advisable to choose not a limited set of predefi ned Internet sources, but unstructured media data on an unlimited set of resources, which leads to the need to build a system for complex monitoring of social phenomena. Such system can supplement and correct mathematical and information models for the spread of viruses, aimed at minimizing the damage caused by any pandemic. Results: It is proposed to create a software system that includes a Data Retrieving subsystem (for collecting and preprocessing media data) combined with a headless browser. This allows to build a system for monitoring of social phenomena, complementing mathematical and information models of the spread of viruses, aimed at minimizing the damage they cause. The feature of developed system is the using of a natural language processing framework based on the associative-ontological approach, and software implementation of the adaptive-behavioral SEIR model, as well as a subsystem for interpreting the collected data, generating metadata for identifying and correcting the model. Conclusions: The proposed system allows to make more balanced management decisions based on the analysis of the current situation in the infosphere. An additional advantage of the system is the ability to identify poorly predictable reactions of society to certain events expressed in media content.
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