ArticleInfectious Disease Modelling2020
Adequacy of Logistic models for describing the dynamics of COVID-19 pandemic.
Article in Infectious Disease Modelling, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Modeling and inference of mixed dynamics and detection of causal emergent features.Scientific reports · 2026Article
- A causal framework to determine the effectiveness of dynamic quarantine policy to mitigate COVID-19.Applied soft computing · 2021Article
- An improved SIR model describing the epidemic dynamics of the COVID-19 in China.Results in physics · 2021Article
- A simple mathematical tool to forecast COVID-19 cumulative case numbers.Clinical epidemiology and global healthArticle
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Logistic models have been widely used for modelling the ongoing COVID-19 pandemic. This study used the data for Kuwait to assess the adequacy of the two most commonly used logistic models (Verhulst and Richards models) for describing the dynamics COVID-19. Specifically, the study assessed the predictive performance of these two models and the practical identifiability of their parameters. Two model calibration approaches were adopted. In the first approach, all the data was used to fit the models as per the heuristic model fitting method. In the second approach, only the first half of the data was used for calibrating the models, while the other half was left for validating the models. Analysis of the obtained calibration and validation results have indicated that parameters of the two models cannot be identified with high certainty from COVID-19 data. Further, the models shown to have structural problems as they could not predict reasonably the validation data. Therefore, they should not be used for long-term predictions of COVID-19. Suggestion have been made for improving the performances of the models.
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