Recovery Ratios Reliably Anticipate COVID-19 Pandemic Progression
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Abstract
The COVID-19 pandemic is placing unprecedented demands on healthcare systems worldwide and exacting a massive humanitarian toll. This makes the development of accurate predictive models imperative, not just for understanding the course of the pandemic but more importantly for gaining insight into the efficacy of public health measures and planning accordingly. Epidemiological models are forced to make assumptions about many unknowns and therefore can be unreliable. Here, taking an empirical approach, we report a 20-30 day lag between the peak of confirmed to recovered cases and the peak of daily deaths in each country, independent of the epoch of that country in its pandemic cycle. This analysis is expected to be largely independent of the proportion of the population being tested and therefore should aid in planning around the timing and easing of public health measures. Our data also suggests broad predictions for the number of fatalities, generally somewhat lower than most other models. Finally, our model suggests that the world as a whole is shortly to enter a recovery phase, at least as far as the first pandemic wave is concerned.
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SciScore for 10.1101/2020.04.09.20059824: (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: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:These data and projections are, naturally, subject to limitations. The most important of these relate to whether there will be a large rebound in cases nationally and globally once restrictions on movement and gatherings are relaxed. Our analysis cannot predict this aspect. There is also uncertainty around whether, independent of …
SciScore for 10.1101/2020.04.09.20059824: (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: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:These data and projections are, naturally, subject to limitations. The most important of these relate to whether there will be a large rebound in cases nationally and globally once restrictions on movement and gatherings are relaxed. Our analysis cannot predict this aspect. There is also uncertainty around whether, independent of rebounds connected to the easing of public health measures, there would be a resurgence in COVID-19 cases in the Northern Hemisphere autumn, as is often the case with seasonal influenza.
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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