Abstract
The proposed model is based on COVID-19 Big Data Hub. It enables us to predict pandemics development taking into account multiple virus strains and delays of infectiousness. Two-strain dynamic models with distributed delays have been fitted to the time series retrieved from COVID data hub. The data at the national, regional, and county-level which are seamlessly integrated with World Bank Open Data, Google Mobility Reports, Apple Mobility Reports, have been used. The parameter identification has been fulfilled with the help of COBYLA algorithm. The simulations have been implemented with the help of Julia high-performance computing. The effect of the time delays is analyzed. The considered pipeline utilizes the data from the Hub to generate the COVID model and to produce a reliable prediction.
Highlights
Nowadays the coronavirus pandemic is considered as a global medical, social, and economic problem
COVID BIG DATA DESCRIPTION The first three elements of a pipeline are provided by COVID-19 Big Data Hub project [29]
In the given model we have described the interaction between two COVID strains
Summary
Nowadays the coronavirus pandemic is considered as a global medical, social, and economic problem. It has affected all branches of human activity, both industry, and science. A lot of researchers are joined to the world-wide projects dealing with the diagnostics, prophylaxis, and treatment of COVID. One of the current topics, connected with COVID19, is the ability to model its spread and by doing so contain it by incorporating appropriate measures
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