Defining high-value information for COVID-19 decision-making
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Abstract
Initial projections from the first generation of COVID-19 models focused public attention on worst-case scenarios in the absence of decisive policy action. These underscored the imperative for strong and immediate measures to slow the spread of infection. In the coming weeks, however, as policymakers continue enlisting models to inform decisions on COVID-19, answers to the most difficult and pressing policy questions will be much more sensitive to underlying uncertainties. In this study, we demonstrate a model-based approach to assessing the potential value of reducing critical uncertainties most salient to COVID-19 decision-making and discuss priorities for acquiring new data to reduce these uncertainties. We demonstrate how information about the impact of non-pharmaceutical interventions could narrow prediction intervals around hospitalizations over the next few weeks, while information about the prevalence of undetected cases could narrow prediction intervals around the timing and height of the peak of the epidemic.
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SciScore for 10.1101/2020.04.06.20052506: (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 numerous limitations to our analysis, including uncertainty around parameter prior distributions and those excluded from the uncertainty analysis for simplicity. Nonetheless, our analysis highlights the benefits of a decision analytic approach for prioritizing the collection of high-value information for policy-making concerning the COVID-19 pandemic.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from B…
SciScore for 10.1101/2020.04.06.20052506: (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 numerous limitations to our analysis, including uncertainty around parameter prior distributions and those excluded from the uncertainty analysis for simplicity. Nonetheless, our analysis highlights the benefits of a decision analytic approach for prioritizing the collection of high-value information for policy-making concerning the COVID-19 pandemic.
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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