Bringing accountability to the peak of the pandemic using linear response theory
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Abstract
The peak of the daily new infections in COVID-19 remained qualitative in description and elusive in arrival. Because of the lack of clarity in what to expect from the peak, apart from the hope that one day the peak will be reached, there has been no metric to describe the success of the implemented strategies. We propose a way of predicting the number of infections that can be expected after a lockdown, assuming they come from the asymptomatic cases prior to the lockdown and using linear response theory. These predictions for several western countries faithfully follow the observed infections for several weeks after the lockdown, suggesting universalities in the recovery pattern of several countries. At the same time, the gap between the quantitative predictions of the recovery patterns for New York and Milan and the observations is striking. These gaps which arise even while emulating the recovery patterns of other western countries raise the possibility of an audit of the success of the implemented strategies, and the potential newer sources of infection.
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SciScore for 10.1101/2020.04.21.20068478: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. 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: 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 …
SciScore for 10.1101/2020.04.21.20068478: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. 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: 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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