Time-variant reliability-based prediction of COVID-19 spread using extended SEIVR model and Monte Carlo sampling
A probabilistic method is proposed in this study to predict the spreading profile of the coronavirus disease 2019 (COVID-19) in the United State (US) via time-variant reliability analysis. To this end, an extended susceptible-exposed-infected-vaccinated-recovered (SEIVR) epidemic model is first esta...
Main Authors: | Mahdi Shadabfar, Mojtaba Mahsuli, Arash Sioofy Khoojine, Vahid Reza Hosseini |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2021-07-01
|
Series: | Results in Physics |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2211379721004897 |
Similar Items
-
Probabilistic assessment of axial load-carrying capacity of FRCM-strengthened concrete columns using artificial neural network and Monte Carlo simulation
by: Mohammad Ali Irandegani, et al.
Published: (2022-12-01) -
A Study of Seismic Macroeconomic Losses Based on Monte Carlo Method—Take Tangshan City as an Example
by: Qing Wu, et al.
Published: (2018-12-01) -
MCPSHA: A New Tool for Probabilistic Seismic Hazard Analysis Based on Monte Carlo Simulation
by: Xiaoyi Shao, et al.
Published: (2024-01-01) -
A State-of-the-Art Review of Probabilistic Portfolio Management for Future Stock Markets
by: Longsheng Cheng, et al.
Published: (2023-02-01) -
Study, suspend and optimization a spread of epidemic infections. The dynamic Monte Carlo approach
by: Gennadiy Burlak
Published: (2020-10-01)