ALeRT-COVID: Attentive Lockdown-awaRe Transfer Learning for Predicting COVID-19 Pandemics in Different Countries
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Abstract
No abstract available
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SciScore for 10.1101/2020.07.09.20149831: (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 and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There are several limitations of our research. First, the lockdown measure was modeled as a binary variable representing the lockdown is on or off. However, different countries conducted different levels of the lockdown. Even within the same country, such as United States, different states had different lockdown measures. A more fine-grained quantification of the lockdown measures may enhance the prediction performance. In addition, …
SciScore for 10.1101/2020.07.09.20149831: (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 and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There are several limitations of our research. First, the lockdown measure was modeled as a binary variable representing the lockdown is on or off. However, different countries conducted different levels of the lockdown. Even within the same country, such as United States, different states had different lockdown measures. A more fine-grained quantification of the lockdown measures may enhance the prediction performance. In addition, more related information could be introduced to the model such as the number of recovered cases, death cases, available healthcare resources, etc. Although the proposed method aims at predicting the cumulative case numbers, the prediction targets can be conveniently changed to the number of recovery or death cases. In conclusion, our predictions provide valuable data-driven insights for better understanding the current situation and could help policy makers and health authorities make plans to manage the future situation.
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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