An aberration detection-based approach for sentinel syndromic surveillance of COVID-19 and other novel influenza-like illnesses
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
No abstract available
Article activity feed
-
-
SciScore for 10.1101/2020.06.08.20124990: (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 and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Data and Study Limitations: Our study faced several challenges from a data perspective. Firstly, it must be noted that patient profiles significantly change between normal work-week operations and weekends/holidays, which are far more likely to be acute/emergency care. As such, to prevent these from becoming a confounding factor and unduly influencing our anomaly detection error plots, data points relating to weekends, US federal …
SciScore for 10.1101/2020.06.08.20124990: (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 and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Data and Study Limitations: Our study faced several challenges from a data perspective. Firstly, it must be noted that patient profiles significantly change between normal work-week operations and weekends/holidays, which are far more likely to be acute/emergency care. As such, to prevent these from becoming a confounding factor and unduly influencing our anomaly detection error plots, data points relating to weekends, US federal holidays, Christmas Eve and New Year’s Eve were excluded from our datasets. We do not believe that this has affected the validity of our results, further evidenced by the plot in Figure 4, showing that the period of elevated ILI activity that occurred from January through mid-March of 2018 was correctly reflected, while December of 2017 did not display anomalous results, indicating that our model is not simply picking up on proximity to holidays. We will, however, work on incorporating weekend and holiday data as part of our models as part of future work. Additionally, several limitations within our data sources hampered our efforts to evaluate our methods: as previously noted, anomalies may also be caused by problems with the input data unrelated to the syndromic surveillance task. Specifically, in our case, we faced two major EHR/data platform shifts within our source data that led to irregular disruption of clinical documentation within our data warehouse, one occurring throughout the entirety of Q1 2016, and the other occurring beginning May 1st ...
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.
-
