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

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...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.113866-113878
Main Authors: Martsenyuk, Vasyl, Bernas, Marcin, Klos-Witkowska, Aleksandra
Format: Article
Language:English
Subjects:
Algorithms
Big Data
Computational modeling
Coronaviruses
COVID
COVID-19
Data models
delayed dynamic system
Delays
distributed delays
Dynamic models
epidemiology
high-performance computing
Mathematical model
Parameter identification
Pipelines
scientific machine learning
virus strain
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3104519