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

• Main Authors: Vasyl Martsenyuk, Marcin Bernas, Aleksandra Klos-Witkowska
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: Big data; COVID; delayed dynamic system; distributed delays; epidemiology; high-performance computing
• Online Access: https://ieeexplore.ieee.org/document/9513276/

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.