Daily Forecasting of Regional Epidemics of Coronavirus Disease with Bayesian Uncertainty Quantification, United States
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Abstract
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SciScore for 10.1101/2020.07.20.20151506: (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:We caution that our work has several limitations. One is that trend detection is data-driven, which means that a new trend cannot be detected until enough evidence of it has accumulated. The data we are using are reports of new cases, which reflect transmission dynamics of the past vs. current transmission dynamics. Other types of surveillance data, such as assays of viral RNA in wastewater samples, may permit improved situational …
SciScore for 10.1101/2020.07.20.20151506: (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:We caution that our work has several limitations. One is that trend detection is data-driven, which means that a new trend cannot be detected until enough evidence of it has accumulated. The data we are using are reports of new cases, which reflect transmission dynamics of the past vs. current transmission dynamics. Other types of surveillance data, such as assays of viral RNA in wastewater samples, may permit improved situational awareness. Another limitation is that our inferences are based on a mathematical model associated with considerable structure and fixed parameter uncertainties as well as simplifications. Among the simplifications is the replacement of certain time-varying parameters, such as that characterizing testing capacity, with constants, which are assumed to provide an adequate time-averaged characterization. The model form is that of a deterministic compartmental model. A stochastic version of the model may be more appropriate if conditions change (from the current situation of high disease prevalence to low prevalence). Although the model is capable of reproducing historical data and making accurate short-term forecasts, its structure and fixed parameters are subject to revision as we learn more about COVID-19. In the future, results from serological studies and estimates of excess deaths should allow model improvements.
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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