Identifiability and predictability of integer- and fractional-order epidemiological models using physics-informed neural networks
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SciScore for 10.1101/2021.04.05.21254919: (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
No key resources detected.
Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:The surge of COVID-19 disease prompted many researchers around the world to analyze the dynamics of disease transmission by employing different statistical and epidemiological models, providing the opportunity to further revise and modify to some extent the underlying assumptions, limitations, robustness, and the associated uncertainties of such models. While studying the behavior of virus in a pandemic presents a broader opportunity to …
SciScore for 10.1101/2021.04.05.21254919: (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
No key resources detected.
Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:The surge of COVID-19 disease prompted many researchers around the world to analyze the dynamics of disease transmission by employing different statistical and epidemiological models, providing the opportunity to further revise and modify to some extent the underlying assumptions, limitations, robustness, and the associated uncertainties of such models. While studying the behavior of virus in a pandemic presents a broader opportunity to interrogate how to manage pathogens in future events4, critical investigation of different epidemiological models can also shed light on their advantages, limitations, and sensitivity to small changes53. Appropriate mathematical models can be used to estimate parameters of pathogen spread, explore possible future scenarios for control measures, evaluate retrospectively the efficacy of specific interventions, and identify prospective strategies54. However, the main problem with these lumped epidemiological models is the lack of uniqueness of parameters for integer-order models and fractional operators for fractional-order models. At the present time, there are no rigorous a priori identifiability analysis for models with time-dependent parameters or time-dependent fractional orders. Hence, in the present work we resorted to systematic numerical experimentation to provide a posteriori some measures of identifiability using uncertainty quantification. We have learned some useful lessons by fitting the epidemic data using nine different epidemiolo...
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.
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