A novel extended model with versatile shaped failure rate: Statistical inference with Covid -19 applications

Shafiq, Anum; Sindhu, Tabassum Naz; Alotaibi, Naif

doi:10.1016/j.rinp.2022.105398

Cited by 12 publications

(7 citation statements)

References 33 publications

(36 reference statements)

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“…Non-Newtonian fluids, on the other hand, are thought to be more appropriate for heat mass transport than Newtonian fluids, notably in medical sciences where medication therapy is concerned. Numerical qualities of the combination of the Lindley model with two parts (2-CMLM) are examined by Anum Shafiq et al [26].…”

Section: Resultsmentioning

confidence: 99%

Mass Transfer Effects on the Mucus Fluid with Pulsatile Flow Influence of the Electromagnetic Field

et al. 2022

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The influence of pulsatile flow on the oscillatory motion of an incompressible conducting boundary layer mucus fluid flowing through porous media in a channel with elastic walls is investigated. The oscillatory flow is treated as a cyclical time-dependent flux. The Laplace transform method using the Womersley number is used to solve non-linear equations controlling the motion through porous media under the influence of an electromagnetic field. The theoretical pulsatile flow of two liquid phase concurrent fluid streams, one kinematic and the other viscoelastic, is investigated in this study. To extend the model for various physiological fluids, we postulate that the viscoelastic fluid has several distinct periods. We also apply our analytical findings to mucus and airflow in the airways, identifying the wavelength that increases dynamic mucus permeability. The microorganism’s thickness, velocity, energy, molecular diffusion, skin friction, Nusselt number, Sherwood number, and Hartmann number are evaluated. Discussion is also supplied in various sections to investigate the mucosal flow process.

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Section: Resultsmentioning

confidence: 99%

Mass Transfer Effects on the Mucus Fluid with Pulsatile Flow Influence of the Electromagnetic Field

et al. 2022

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“…For example, Sindhu et al [ 19 ] proposed a three parametric model named as Exponentiated transformation of Gumbel Type-II (ETGT-II) for analyzing the number of deaths due to COVID-19 for Europe and China. In addition, there are several studies have developed different types of statistical models based on COVID-19 mortality data and evaluated the performance of the models [ 20 , 21 ]. Rahman et al [ 22 ] developed a seasonal Autoregressive Integrated Moving Average (ARIMA) model and eXtreme Gradient Boosting (XGBoost) model to simulate the overall trend of confirmed cases and deaths of COVID-19 infection in Bangladesh, and compared the accuracy of predictions of two methods.…”

Section: Introductionmentioning

confidence: 99%

Combining the dynamic model and deep neural networks to identify the intensity of interventions during COVID-19 pandemic

He,

Tang,

Xiao

2023

PLoS Comput Biol

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During the COVID-19 pandemic, control measures, especially massive contact tracing following prompt quarantine and isolation, play an important role in mitigating the disease spread, and quantifying the dynamic contact rate and quarantine rate and estimate their impacts remain challenging. To precisely quantify the intensity of interventions, we develop the mechanism of physics-informed neural network (PINN) to propose the extended transmission-dynamics-informed neural network (TDINN) algorithm by combining scattered observational data with deep learning and epidemic models. The TDINN algorithm can not only avoid assuming the specific rate functions in advance but also make neural networks follow the rules of epidemic systems in the process of learning. We show that the proposed algorithm can fit the multi-source epidemic data in Xi’an, Guangzhou and Yangzhou cities well, and moreover reconstruct the epidemic development trend in Hainan and Xinjiang with incomplete reported data. We inferred the temporal evolution patterns of contact/quarantine rates, selected the best combination from the family of functions to accurately simulate the contact/quarantine time series learned by TDINN algorithm, and consequently reconstructed the epidemic process. The selected rate functions based on the time series inferred by deep learning have epidemiologically reasonable meanings. In addition, the proposed TDINN algorithm has also been verified by COVID-19 epidemic data with multiple waves in Liaoning province and shows good performance. We find the significant fluctuations in estimated contact/quarantine rates, and a feedback loop between the strengthening/relaxation of intervention strategies and the recurrence of the outbreaks. Moreover, the findings show that there is diversity in the shape of the temporal evolution curves of the inferred contact/quarantine rates in the considered regions, which indicates variation in the intensity of control strategies adopted in various regions.

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“…Nevertheless, statisticians provide a significant influence in understanding and modelling Covid-19 infections, thus they needed to develop a statistical model capable of fitting and modelling Covid-19 infections, regardless it be continuous or discrete random variables. Various authors developed statistical model for Covid-19 mortality data, for further information, please refer to [28] , [29] , Sindhu et al [30] , [31] , [32] , Wang [33] , Lalmuanawma et al [34] , Leon et al [35] , [36] , Shafiq et al [37] and Bullock et al [38] .…”

Section: Introductionmentioning

confidence: 99%

Comparative study of artificial neural network versus parametric method in COVID-19 data analysis

et al. 2022

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A novel extended model with versatile shaped failure rate: Statistical inference with Covid -19 applications

Cited by 12 publications

References 33 publications

Mass Transfer Effects on the Mucus Fluid with Pulsatile Flow Influence of the Electromagnetic Field

Mass Transfer Effects on the Mucus Fluid with Pulsatile Flow Influence of the Electromagnetic Field

Combining the dynamic model and deep neural networks to identify the intensity of interventions during COVID-19 pandemic

Comparative study of artificial neural network versus parametric method in COVID-19 data analysis

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