Uncertainty Reduction in Logistic Growth Regression Using Surrogate Systems Carrying Capacities: a COVID-19 Case Study
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
No abstract available
Article activity feed
-
-
SciScore for 10.1101/2020.12.14.20248184: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Optimization was performed with a trust-region-reflective algorithm, implemented in the function “lsqcurvefit”, from the Optimization Toolbox in MATLAB. MATLABsuggested: (MATLAB, RRID:SCR_001622)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:An intrinsic limitation to the GLF used is regarding its inflection point: it is limited between K/2 (for α = 1) and K/e (for α → 0). Yet, the literature shows that GLF represents a good balance between number of free parameters …
SciScore for 10.1101/2020.12.14.20248184: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Optimization was performed with a trust-region-reflective algorithm, implemented in the function “lsqcurvefit”, from the Optimization Toolbox in MATLAB. MATLABsuggested: (MATLAB, RRID:SCR_001622)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:An intrinsic limitation to the GLF used is regarding its inflection point: it is limited between K/2 (for α = 1) and K/e (for α → 0). Yet, the literature shows that GLF represents a good balance between number of free parameters and ability to describe well epidemic curves [8]. Indeterminacy of α vs. r remained unresolved in our study, but different from [14], here K is pre-determined, allowing for continuous transitions between curves for different K’s. Finally, we also observed that the product αr is proportional to the slope of the logistic function [15]. Because of the difficulty in determining both α and r independently, in some references αr ∝= ro, where ro is not represent the initial growth of the process [8]. More sophisticated logistic models may be used for the constrained regression for the creation of alternative predictive scenarios, for example, using generalized exponentials [20, 21]. In conclusion, the proposed method gives a complementary prescription to the traditional growth prediction methods. It can apply to any early logistic process, where there is an earlier surrogate process for estimation of the carrying capacity. This strategy overcomes the intrinsic inability logistic models have for prediction of the inflection point. It can be used not only for epidemics but also for commerce, economics, viral information dissemination in a population. Our method lowers the uncertainty in the prediction for optimistic and pessimistic epidemic scenarios without t...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
-
