How to evaluate the success of the COVID-19 measures implemented by the Norwegian government by analyzing changes in doubling time
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Abstract
Doubling Time (DT) is typically calculated for growth curves that show exponential growth, such as the cumulative number of COVID-19 cases day by day. DT represents the time it takes before the number of COVID-19 cases, in a certain country or area, doubles.
Throughout the ongoing COVID-19 outbreak, DT values are continually changing. These changes are influenced by the measures that are recommended by the health authorities and implemented by governments.
After the government-imposed shutdowns of Nordic Countries that were announced around the 12 th of March 2020, we followed the development of the DT in the region. Governments put in place measures never before experienced during peace time; working from home, closed schools and kindergartens, travel bans and social distancing. We conducted analyses to evaluate the effectiveness of these measures. Does it work? The initial set of results following the shutdown are encouraging, demonstrating a trend towards slower growth; however, this could be reversed if the measures that are in place now are abandoned too early. Premature optimism can be very costly. In this report we describe a method for monitoring the epidemic in real time and evaluating the effectiveness of the implemented measures.
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SciScore for 10.1101/2020.03.29.20045187: (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 10.1101/2020.03.29.20045187: (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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