COVIDNearTerm: A simple method to forecast COVID-19 hospitalizations
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Abstract
Introduction:
COVID-19 has caused tremendous death and suffering since it first emerged in 2019. Soon after its emergence, models were developed to help predict the course of various disease metrics, and these models have been relied upon to help guide public health policy.
Methods:
Here we present a method called COVIDNearTerm to “forecast” hospitalizations in the short term, two to four weeks from the time of prediction. COVIDNearTerm is based on an autoregressive model and utilizes a parametric bootstrap approach to make predictions. It is easy to use as it requires only previous hospitalization data, and there is an open-source R package that implements the algorithm. We evaluated COVIDNearTerm on San Francisco Bay Area hospitalizations and compared it to models from the California COVID Assessment Tool (CalCAT).
Results:
We found that COVIDNearTerm predictions were more accurate than the CalCAT ensemble predictions for all comparisons and any CalCAT component for a majority of comparisons. For instance, at the county level our 14-day hospitalization median absolute percentage errors ranged from 16 to 36%. For those same comparisons, the CalCAT ensemble errors were between 30 and 59%.
Conclusion:
COVIDNearTerm is a simple and useful tool for predicting near-term COVID-19 hospitalizations.
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SciScore for 10.1101/2021.10.08.21264785: (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:There are a few caveats to our modeling. Our comparisons to the models in COVIDNearTerm may have been slightly biased to favor COVIDNearTerm. First, we know the publication date of the CalCAT component models, which is when the predictions appeared on the CalCAT site, but not the actual date the predictions were made. We assume that the publication date was close to the prediction date, and the modelers did have the option of making …
SciScore for 10.1101/2021.10.08.21264785: (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:There are a few caveats to our modeling. Our comparisons to the models in COVIDNearTerm may have been slightly biased to favor COVIDNearTerm. First, we know the publication date of the CalCAT component models, which is when the predictions appeared on the CalCAT site, but not the actual date the predictions were made. We assume that the publication date was close to the prediction date, and the modelers did have the option of making frequent prediction updates. Second, we looked at the data retrospectively with COVIDNearTerm. Therefore, we had the most updated data on actual hospitalizations, which might be slightly different than when the other models made their predictions. But since we did not start our comparisons until June 2020, most data issues should have been resolved. Overall, we believe the performance metrics we have presented are valid. We saw a modest impact on hospitalizations based on the day of the week, which was much less than the impact of day of the week on cases (data not shown). If adjusting for day of the week is desired, we suggest making the adjustment outside of COVIDNearTerm. By making predictions 14, 21 or 28 days in the future we mostly avoided the day of the week problem. We also did not address the modest impact of holidays. One weakness of COVIDNearTerm is that its predictions generally monotonically increase or decrease over time. We might believe that hospitalizations will, for instance, decrease four weeks out based on a recent decrease in ...
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.
Results from scite Reference Check: We found no unreliable references.
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