Mathematical framework to model Covid-19 daily deaths
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Abstract
The novel coronavirus (2019-nCoV) pandemic has caused widespread socio-economic disruption and, as of 04/07/2020, resulted in more than 72,614 confirmed deaths worldwide. Robust prediction of the trajectory of the death incidence curve is helpful for policy decisions during this ongoing crisis. We propose a non-parametric model to fit the number of daily deaths in a region, which hypothesizes that the death incidence curve will have a convex shape in the beginning, a concave shape near the peak, and a convex shape in the final stage of the death incidence curve after the peak. Using this, we performed robust short-term predictions on phases in five countries worldwide and five US states. Our analysis shows while the five states are all at peaks or past their peaks, US as a country is possibly not at peak yet. Our model can be easily fitted on daily death data from any region.
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SciScore for 10.1101/2020.05.18.20106104: (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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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 …
SciScore for 10.1101/2020.05.18.20106104: (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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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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