Human-Machine Interaction for Monitoring COVID-19 Internet Data in Russia and the World

Abstract

We develop a human-machine interaction via dashboard for COVID-19 data visualization in the regions of Russia and the world. In particular, it includes an adaptive-compartmental multi-parametric model of the epidemic spread, which is a generalization of the classical SEIR models; and a module for visualizing and setting the parameters of this model according to epidemiological data, implemented in a dashboard. Data for testing have been collected since March 2020 on a daily basis from open Internet sources and placed on a “data farm” (an automated system for collecting, storing and pre-processing data from heterogeneous sources) hosted on a remote server. The combination of the proposed approach and its implementation in the form of a dashboard with the ability to conduct visual numerical experiments and compare them with real data allows most accurately tune the model parameters thus turning it into an intelligent system to support a decision-making. That is a small step towards Industry 5.0.