Two-Strain COVID-19 Model Using Delayed Dynamic System and Big Data

doi:10.1109/ACCESS.2021.3104519

2021-01-01

Vasyl Martsenyuk *

Marcin Bernaś

Aleksandra Kłos-Witkowska

DOI：10.1109/ACCESS.2021.3104519

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摘要

En 中文

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.

Keyword:

COVID-19

Big Data

Data models

Mathematical model

Delays

Computational modeling

Pipelines

Big data

COVID

delayed dynamic system

distributed delays

epidemiology

high-performance computing

parameter identification

scientific machine learning

virus strain

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期刊

IEEE Access

IF:

3.6

论文数:

9.8W

被引数:

29.4W

机构

University of Bielsko Biala

学者数:

287

论文数: 272

被引数: 0

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