Adaptive-compartmental model of coronavirus epidemic and its optimization by the methods of artificial intelligence

Abstract

Abstract 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.