The Peak and Size of COVID-19 in India: SARIMA and Forecast (Preprint)

Yadav, Suryakant; Yadav, Pawan Kumar; Yadav, Neha; Yadav, Chandra Kant

doi:10.2196/24477

Cited by 1 publication

(2 citation statements)

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“…2021 ; Konarasinghe 2021 ; Yadav et al. 2022 ) used either Auto Regressive Integrated Moving Average (ARIMA) model or Seasonal ARIMA also known as SARIMA model or SARIMAX model. And some works (Rguibi et al.…”

Section: Long-run Data Analysis Resultsmentioning

confidence: 99%

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

Ojha,

Yarahmadian,

Bobo

2023

Stoch Environ Res Risk Assess

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It is now almost three years that COVID-19 has been the cause of misery for millions of people around the world. Many countries are in process of vaccination. Due to the social complexity of the problem, the future of decisions is not clear. As such, there is a need for the mathematical modeling to predict the long-run behavior of the COVID-19 dynamic for the decision-making with regard to the result of the pandemic on the economy, health, and others. In this paper, we have studied the short and long-run behavior of COVID-19. In a novel way, random evolution (Trichotomous and Dichotomous Markov Noise) is used to model and analyze the long-run behavior of the pandemic in different phases of the pandemic in different countries. On the given conditions, the random evolution model can help us establish the long-run asymptotic behaviour of the pandemic. This allows us to consider different phases of the pandemic as well as the effect of vaccination and other measures taken. The simplicity of the model makes it a practical tool for decision-making based on the long-run behavior of the pandemic. As such, we have established a criterion for the comparison of different regions and countries in different phases. In this regard, we have used real pandemic data from different countries to validate our results.

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Section: Long-run Data Analysis Resultsmentioning

confidence: 99%

“…2021 ; Konarasinghe 2021 ; Yadav et al. 2022 ; Paiva et al. 2021 ; Wei and Billings 2021 ; Ayoobi et al.…”

Section: Long-run Data Analysis Resultsunclassified