Tracking COVID-19 Cases and Deaths in the United States: Distribution of Events by Day of Pandemic
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Abstract
In this paper, we analyze the progression of COVID-19 in the United States over a nearly one-year period beginning March 1, 2020, with a novel metric representing the partial-average day-of-event, where events are new cases and new deaths. The metric is calculated as a function of date and location to illustrate patterns of disease, showing growing or waning cases and deaths. The metrics enable the direct comparison of the time distribution of cases and deaths, revealing data coherence and how patterns varied over a one-year period. We also compare different methods of estimating actual infections and deaths to better understand on the timing and dynamics of the pandemic by state. We used three example states to graphically compare metrics as functions of date and also compared statistics derived from all 50 states. Over the period studied, average case day and average death day vary by two to five months among the 50 states, depending on data source, with the earliest averages in New York and surrounding states, as well as Louisiana. The average day of death has preceded the average day of case in Centers for Disease Control (CDC) data for most states and most dates since June of 2020. In contrast, “COVID-19 Projections” more closely align deaths and cases, which are similarly distributed.
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SciScore for 10.1101/2021.08.30.21262851: (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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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…
SciScore for 10.1101/2021.08.30.21262851: (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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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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