Information system for COVID-19 data visualization and study of control parameters of the pandemic spread model in Russia and the world

Abstract

We develop an information system for COVID-19 data visualization in the regions of Russia and the world. It includes: 1) an adaptive-compartmental multi-parametric model of the epidemic spread, which is a generalization of the classical SEIR models; and 2) a module for visualizing and setting the parameters of this model according to epidemiological data, implemented in a dashboard, called herein “coVID”. Data for testing have been collected since March 2020 on a daily basis from open Internet sources and placed on a "data farm" (automated system for collecting, storing and pre-processing data from heterogeneous sources) hosted on a remote server. The combination of the proposed model and the dashboard gives the ability to conduct visual numerical experiments and compare them with real data allowing most accurately tune the model parameters thus turning it into an intelligent information system to support a decision-making. The most important model parameters are also determined.