Modeling the Coronavirus Disease 2019 Incubation Period: Impact on Quarantine Policy
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Abstract
The incubation period of coronavirus disease 2019 (COVID-19) is not always observed exactly due to uncertain onset times of infection and disease symptoms. In this paper, we demonstrate how to estimate the distribution of incubation and its association with patient demographic factors when the exact dates of infection and symptoms’ onset may not be observed. The findings from analysis of the confirmed COVID-19 cases indicate that age could be associated with the incubation period, and an age-specific quarantine policy might be more efficient than a unified one in confining COVID-19.
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SciScore for 10.1101/2020.06.27.20141002: (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: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:The application to the COVID-19 data sets has some notable limitations. Our inferences relied on publicly reported confirmed cases that might over represent more severely symptomatic patients. Moreover, the definition of COVID-19 symptoms and hospitalization criteria could differ by country, especially during the initial …
SciScore for 10.1101/2020.06.27.20141002: (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: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:The application to the COVID-19 data sets has some notable limitations. Our inferences relied on publicly reported confirmed cases that might over represent more severely symptomatic patients. Moreover, the definition of COVID-19 symptoms and hospitalization criteria could differ by country, especially during the initial outbreak. We combined the data sets from two different sources, and the potential variation in source criteria for tracing infected cases may lead to different exposure records. However, we obtained similar findings when fitting the model to each data set separately. The same trend was observed for the incubation period by the age groups (Supplementary Table S4 and Table S5), though one indicated no statistically significant difference. We dichotomized age to show the difference in the incubation period time that might exist between the two groups. However, in no case was it our goal to identify an optimal cut-off age, since we are aware of the risks involved in the dichotomization of the explanatory variables [17, 16]. The longer incubation periods experienced by older patients might have been due to a delayed immune response system, given the mechanism of immune systems against COVID-19 [18]. However, the results may not be directly applicable to affect the public health policy globally, because the distribution of the incubation period could differ by other factors such as case reporting system, and co-infection levels in different regions and countries. T...
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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SciScore for 10.1101/2020.06.27.20141002: (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 Results In a total of 312 patients , the median age was 42 ( interquartile range 3355 ) years and 126 ( 40.4 % ) were women . Table 2: Resources
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
- This analysis has some notable limitations.
- Our inferences relied on publicly reported confirmed cases that might over represent more severely symptomatic patients.
- Moreover, the definition of COVID-19 symptoms and hospitalization criteria could differ by country, especially during …
SciScore for 10.1101/2020.06.27.20141002: (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 Results In a total of 312 patients , the median age was 42 ( interquartile range 3355 ) years and 126 ( 40.4 % ) were women . Table 2: Resources
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
- This analysis has some notable limitations.
- Our inferences relied on publicly reported confirmed cases that might over represent more severely symptomatic patients.
- Moreover, the definition of COVID-19 symptoms and hospitalization criteria could differ by country, especially during the initial outbreak.
- We combined the data sets from two different sources, and the potential variation in source criteria for tracing infected cases may lead to different exposure records.
- However, we obtained similar findings when fitting the model to each dataset separately, although one indicated no signif-icant differences but the same trend in the incubation period between the age groups (Supplement S8).
- An optimal differentiating cut-off age for the incubation period was not identified here due to the limited available data, which is worth pursuing to reduce risks to public health most effectively.
Results from OddPub: We did not find a statement about open data. We also did not find a statement about open code. Researchers are encouraged to share open data when possible (see Nature blog).
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.
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