Estimating the Serial Interval of the Novel Coronavirus Disease (COVID-19): A Statistical Analysis Using the Public Data in Hong Kong From January 16 to February 15, 2020
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SciScore for 10.1101/2020.02.21.20026559: (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: We detected the following sentences addressing limitations in the study:We note that extra-cautious should be needed to interpret the clusters of cases because of this potential limitation. Although we used interval censoring likelihood to deal with the multiple-infector matching issue, more …
SciScore for 10.1101/2020.02.21.20026559: (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: We detected the following sentences addressing limitations in the study:We note that extra-cautious should be needed to interpret the clusters of cases because of this potential limitation. Although we used interval censoring likelihood to deal with the multiple-infector matching issue, more detailed information of the exposure history and clue on ‘who acquires infection from whom’ (WAIFW) would improve our estimates. Longer SI might be difficult to occur in reality due to the isolation of confirmed infections, or to identify and link together due to the less accurate information associated with memory error occurred in the backward contact tracing exercise. The issue associated with isolation could possibly bias the SI estimates and lead to an underestimated result. Due to lack of information in the public dataset, our estimation framework could be benefit from detailed records on the date of isolation of individual cases. But isolation occurs for all severe infectious diseases such as SARS and COVID-19, there is no reason to believe that isolation play a bigger role in one than the other, at least more evidence is needed to support that claim. It is however possible that at the initial stage the SI is longer than later when strict isolation takes place. Nevertheless, a comparison of estimated SI for SARS and COVID-19 in Hong Kong is still meaningful. And we found that the SI of COVID-19 estimated appears shorter than that of SARS, which is the key message. It would be hard to imagine that isolation is responsible for the difference. There is no...
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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