A New Flexible Sigmoidal Growth Model

Cao, Lei; Shi, Peijian; Li, Lin; Chen, Guifen

doi:10.3390/sym11020204

Cited by 48 publications

(46 citation statements)

References 57 publications

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“…There are variant reparamerized forms of the Richards curve in the literature [ 23 – 26 ], and we shall use the following form in this research where θ 1 , θ 2 , and θ 3 are real numbers, and ξ is a positive real number. The utility of the Richards curve ( 1 ) is its ability to describe a variety of growing processes, endowed with strong flexibility due to the shape parameter ξ [ 24 ]: analytically, the Richards curve ( 1 ) (i) becomes the logistic growth curve [ 27 ] when ξ = 1, and (ii) converges to Gompertz growth curve [ 28 ] as the ξ converges to zero from positive side of real numbers.…”

Section: Methodsmentioning

confidence: 99%

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Estimation of COVID-19 spread curves integrating global data and borrowing information

2020

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Currently, novel coronavirus disease 2019 (COVID-19) is a big threat to global health. The rapid spread of the virus has created pandemic, and countries all over the world are struggling with a surge in COVID-19 infected cases. There are no drugs or other therapeutics approved by the US Food and Drug Administration to prevent or treat COVID-19: information on the disease is very limited and scattered even if it exists. This motivates the use of data integration, combining data from diverse sources and eliciting useful information with a unified view of them. In this paper, we propose a Bayesian hierarchical model that integrates global data for real-time prediction of infection trajectory for multiple countries. Because the proposed model takes advantage of borrowing information across multiple countries, it outperforms an existing individual country-based model. As fully Bayesian way has been adopted, the model provides a powerful predictive tool endowed with uncertainty quantification. Additionally, a joint variable selection technique has been integrated into the proposed modeling scheme, which aimed to identify possible country-level risk factors for severe disease due to COVID-19.

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Section: Methodsmentioning

confidence: 99%

“…There are variant reparamerized forms of the Richards curve in the literature [23][24][25][26], and we shall use the following form in this research…”

Section: Richards Growth Curve Modelsmentioning

confidence: 99%

Estimation of COVID-19 spread curves integrating global data and borrowing information

2020

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“…Very often the logistic function is used in another form (Fanizza and Colonna, 1996;Tello and Forneck, 2018;Cao et al, 2019;Ellis et al, 2020). We have derived the growth metrics for this case as well (see Supplementary data -Appendix 2).…”

Section: Curvature Change Rate (Ccr) Methods To Identify Growth Metricsmentioning

confidence: 99%

Metrics for studying berry growth kinetics in seedless grape cultivars (<i>Vitis vinifera</i> L.)

2021

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The process related to the changes in dimensions and mass of grape berry passes through two growth phases separated by a lag phase, and can be described by a double sigmoid curve. The onset of the growth phases and their duration are important factors for understanding the growth processes in grape berries. A new method for their quantitative determination was developed in the present study. In this method, the phase transition dates correspond to the times at which the rate of change of the curvature of the logistic (sigmoid) curve reaches an extreme value. The method was tested on three seedless grape varieties, Sultanina, Ruby Seedless and Rusalka 3, and the changes in grape berry dimensions and mass were tracked from anthesis to harvest. For each of the varieties, a double logistic model of change in berry length, width and mass from anthesis to harvest was developed and the metrics of growth - beginning, stabilisation and end of growth - for each of the two phases were determined. It was found that the metrics in mass and berry dimensions do not match and shift relative to each other over time. A comparison of growth metrics with phenological metrics, such as anthesis, veraison and ripening, showed that phenophases cannot be used as a time scale to record the acceleration of growth processes, as they shifted in time with growth metrics. An exception was veraison, which coincided with the beginning of the accelerated growth of grapes during the second growth phase, following the lag phase. The time scale presented in the current research is a new tool for monitoring growth processes and could help clarify the links between visible changes in the grape berries and the ongoing processes within them. The developed method can also be used for the analysis of various growth processes that follow the logistic law.

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“…There are variant reparamerized forms of the Richards curve in the literature (Birch, 1999;Cao et al, 2019;Causton, 1969;Kahm et al, 2010), and we shall use the following form in this research…”

Section: Richards Growth Curve Modelsmentioning

confidence: 99%

Estimation of COVID-19 spread curves integrating global data and borrowing information

Lee

Lei

Mallick

2020

Preprint

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Currently, novel coronavirus disease 2019 (COVID-19) is a big threat to global health. The rapid spread of the virus has created pandemic, and countries all over the world are struggling with a surge in COVID-19 infected cases. There are no drugs or other therapeutics approved by the US Food and Drug Administration to prevent or treat COVID-19: information on the disease is very limited and scattered even if it exists. This motivates the use of data integration, combining data from diverse sources and eliciting useful information with a unified view of them. In this paper, we propose a Bayesian hierarchical model that integrates global data for real-time prediction of infection trajectory for multiple countries. Because the proposed model takes advantage of borrowing information across multiple countries, it outperforms an existing individual countrybased model. As fully Bayesian way has been adopted, the model provides a powerful predictive tool endowed with uncertainty quantification. Additionally, the proposed model uses countrywide covariates to adjust infection trajectories in curve fitting. A joint variable selection technique has been integrated into the proposed modeling scheme, which aimed to identify possible country-level risk factors for severe disease due to COVID-19.

show abstract

A New Flexible Sigmoidal Growth Model

Cited by 48 publications

References 57 publications

Estimation of COVID-19 spread curves integrating global data and borrowing information

Estimation of COVID-19 spread curves integrating global data and borrowing information

Metrics for studying berry growth kinetics in seedless grape cultivars (<i>Vitis vinifera</i> L.)

Estimation of COVID-19 spread curves integrating global data and borrowing information

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