Modeling COVID-19 Dynamics in Illinois under Nonpharmaceutical Interventions
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Abstract
No abstract available
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SciScore for 10.1101/2020.06.03.20120691: (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.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:The present study of the spread of COVID-19 in Illinois reveals both the strengths and limitations of modeling, and provides potentially actionable insights into the future spread of the disease. We begin with some technical points and best practices that we have developed during our work. Model calibration: Our analysis highlights …
SciScore for 10.1101/2020.06.03.20120691: (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.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:The present study of the spread of COVID-19 in Illinois reveals both the strengths and limitations of modeling, and provides potentially actionable insights into the future spread of the disease. We begin with some technical points and best practices that we have developed during our work. Model calibration: Our analysis highlights the importance of choosing appropriate data with which to calibrate models, and to perform calibration with precision. Due to the large number of parameters that inevitably enter epidemiological models, calibration requires parameter inference in a high dimensional space with strong potential for improper fits resulting from failure to reach global optima. Although the MCMC methods we use are computationally intensive, they are relatively efficient in exploring high(a) Northeastern dimensional, multi-modal distributions, and converge to well-behaved global posterior probability distributions. Bayesian inference enables the incorporation of previous studies (e.g., meta-analyses) to provide reasonable priors on parameters which are poorly constrained by the available data. As an example, although the data we calibrate to does not constrain the prevalence of the infection, we systematically account for this uncertainty by informing our prior on the infection-fatality rate (IFR) from serological studies [32]. The IFR is an important variable in terms of disease outcomes, and so model predictions must systematically account for the uncertainty in this v...
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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