Estimated effectiveness of symptom and risk screening to prevent the spread of COVID-19
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
Traveller screening is being used to limit further spread of COVID-19 following its recent emergence, and symptom screening has become a ubiquitous tool in the global response. Previously, we developed a mathematical model to understand factors governing the effectiveness of traveller screening to prevent spread of emerging pathogens (Gostic et al., 2015). Here, we estimate the impact of different screening programs given current knowledge of key COVID-19 life history and epidemiological parameters. Even under best-case assumptions, we estimate that screening will miss more than half of infected people. Breaking down the factors leading to screening successes and failures, we find that most cases missed by screening are fundamentally undetectable, because they have not yet developed symptoms and are unaware they were exposed. Our work underscores the need for measures to limit transmission by individuals who become ill after being missed by a screening program. These findings can support evidence-based policy to combat the spread of COVID-19, and prospective planning to mitigate future emerging pathogens.
Article activity feed
-
SciScore for 10.1101/2020.01.28.20019224: (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: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There are some limitations to our analysis. Parameter values for nCoV, such as the incubation period, are based on the limited data currently available. For such parameters, the tail of the distribution is important for understanding the potential for long delays until symptoms, but the tails of skewed distributions are notoriously difficult to characterize using limited data. In general, current parameter estimates may also be affected by bias …
SciScore for 10.1101/2020.01.28.20019224: (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: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There are some limitations to our analysis. Parameter values for nCoV, such as the incubation period, are based on the limited data currently available. For such parameters, the tail of the distribution is important for understanding the potential for long delays until symptoms, but the tails of skewed distributions are notoriously difficult to characterize using limited data. In general, current parameter estimates may also be affected by bias or censoring, particularly in the early stages of an outbreak when most cases have been recently infected, and when data are primarily available for relatively severe, hospitalized cases. Another crucial uncertainty highlighted by our analysis is the frequency of cases too mild or non-specific to be detected as nCoV infections. At least one asymptomatic case is known to have occurred in a child (Chan et al., 2020). Further, children and young adults have been conspicuously underrepresented among hospitalized cases (Chen et al., 2020; Huang et al., 2020; Li et al., 2020). The possibility cannot be ruled out that large numbers of subclinical cases are occurring, especially in young people. If an age-by-severity interaction does indeed exist, then the mean age of travellers should be taken into account when estimating screening effectiveness. Further, transmission occurred before the onset of symptoms in one recent case report (Rothe et al., 2020). While it is too early to draw conclusions from a single case report, determining whether pr...
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.
-
-
-