SARS-CoV-2 RNA Wastewater Settled Solids Surveillance Frequency and Impact on Predicted COVID-19 Incidence Using a Distributed Lag Model
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
Article activity feed
-
-
SciScore for 10.1101/2022.02.21.22270864: (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: 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:4.3 DML limitations: This work illustrates limitations with DLM estimated via ordinary least squares. The residual plots illustrate that the selected DLM approach did not characterize all the structure of data. For example, the daily DLMs generally underpredicted during periods of peak incidence. Further, the residuals were highly …
SciScore for 10.1101/2022.02.21.22270864: (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: 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:4.3 DML limitations: This work illustrates limitations with DLM estimated via ordinary least squares. The residual plots illustrate that the selected DLM approach did not characterize all the structure of data. For example, the daily DLMs generally underpredicted during periods of peak incidence. Further, the residuals were highly autocorrelated. Modifications to capture the error structure (e.g., using weighted least squares regression) could further improve model performance. Multicollinearity was also present in the regressors, particularly for the data collected at higher sampling frequency, which can lead to unreliable coefficient estimates with large standard errors.26 However, this was not observed for the model fit to daily sampling data (Table 1 and Tables S2-S5). Multicollinearity can be addressed by putting constraints on the lagged effects such as adopting a spline function for the lag weights.26 Despite the flaws in the DLM approach selected, the out-of-sample prediction of IR was useful. Improvements to the models could further improve performance. In practice, the DLM can be continually updated over time by fitting to the entire dataset or a subset of interest. The use of DLM models relies on a fixed relationship between COVID-19 infections and wastewater concentrations of SARS-COV-2 RNA. During emergence of new variants, the relationship could change if shedding dynamics and loads vary. The models also rely on the existence of reliable COVID-19 infection data....
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).
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.
Results from scite Reference Check: We found no unreliable references.
-
