The long-run analysis of COVID-19 dynamic using random evolution, peak detection and time series-Reference-Cited by-同舟云学术

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The long-run analysis of COVID-19 dynamic using random evolution, peak detection and time series

Published:2023-05-08 Issue:9 Volume:37 Page:3401-3419
ISSN:1436-3240
Container-title:Stochastic Environmental Research and Risk Assessment
language:en
Short-container-title:Stoch Environ Res Risk Assess

Author:

Ojha Vaghawan Prasad,Yarahmadian Shantia,Bobo Richard Hunt

Publisher

Springer Science and Business Media LLC

Subject

General Environmental Science,Safety, Risk, Reliability and Quality,Water Science and Technology,Environmental Chemistry,Environmental Engineering

Link

https://link.springer.com/content/pdf/10.1007/s00477-023-02455-8.pdf

Reference39 articles.

1. Allen LJS, Brauer F, Van den Driessche P, Wu J (2008) Mathematical epidemiology, vol 1945. Springer

2. ArunKumar KE, Kalaga DV, Sai Kumar CM, Chilkoor G, Kawaji M, Brenza TM (2021) Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: auto-regressive integrated moving average (ARIMA) and seasonal auto-regressive integrated moving average (SARIMA). Appl Soft Comput 103:107161

3. Ayoobi N, Sharifrazi D, Alizadehsani R, Shoeibi A, Gorriz JM, Moosaei H, Khosravi A, Nahavandi S, Chofreh AG, Goni FA, Klemeš JJ, Mosavi A (2021) Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods. Results Phys 27:104495

4. Baraniuk RG (2007) Compressive sensing [lecture notes]. IEEE Signal Process Mag 24(4):118–121

5. Bena I (2006) Dichotomous Markov Noise: exact results for out of equilibrium systems. Int J Mod Phys B 20(20):2825–2888

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