Literature DB >> 34786316

The Chaotic Behavior of the Spread of Infection During the COVID-19 Pandemic in the United States and Globally.

Nabin Sapkota1, Waldemar Karwowski2, Mohammad Reza Davahli2, Awad Al-Juaid3, Redha Taiar4, Atsuo Murata5, Grzegorz Wrobel6, Tadeusz Marek7.   

Abstract

In December 2019, China announced the breakout of a new virus identified as coronavirus SARS-CoV-2 (COVID-19), which soon grew exponentially and resulted in a global pandemic. Despite strict actions to mitigate the spread of the virus in various countries, COVID-19 resulted in a significant loss of human life in 2020 and early 2021. To better understand the dynamics of the spread of COVID-19, evidence of its chaotic behavior in the US and globally was evaluated. A 0-1 test was used to analyze the time-series data of confirmed daily COVID-19 cases from 1/22/2020 to 12/13/2020. The results show that the behavior of the COVID-19 pandemic was chaotic in 55% of the investigated countries. Although the time-series data for the entire US was not chaotic, 39% of individual states displayed chaotic infection spread behavior based on the reported daily cases. Overall, there is evidence of chaotic behavior of the spread of COVID-19 infection worldwide, which adds to the difficulty in controlling and preventing the current pandemic. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.

Entities:  

Keywords:  0–1 test; COVID-19 pandemic; Chaotic behavior; spread of infections

Year:  2021        PMID: 34786316      PMCID: PMC8545195          DOI: 10.1109/ACCESS.2021.3085240

Source DB:  PubMed          Journal:  IEEE Access        ISSN: 2169-3536            Impact factor:   3.367


  18 in total

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8.  Is spread of COVID-19 a chaotic epidemic?

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4.  The Chaotic Behavior of the Spread of Infection during the COVID-19 Pandemic in Japan.

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5.  The COVID-19 Infection Diffusion in the US and Japan: A Graph-Theoretical Approach.

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6.  Four Types of Multiclass Frameworks for Pneumonia Classification and Its Validation in X-ray Scans Using Seven Types of Deep Learning Artificial Intelligence Models.

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