Oscillations in U.S. COVID-19 Incidence and Mortality Data Reflect Diagnostic and Reporting Factors
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Abstract
The incidence and mortality data for the COVID-19 data in the United States show periodic oscillations, giving the curve a distinctive serrated pattern. In this study, we show that these periodic highs and lows in incidence and mortality data are due to daily differences in testing for the virus and death reporting, respectively. These findings are important because they provide an explanation based on public health practices and shortcomings rather than biological explanations, such as infection dynamics. In other words, when oscillations occur in epidemiological data, a search for causes should begin with how the public health system produces and reports the information before considering other causes, such as infection cycles and higher incidences of events on certain days. Our results suggest that when oscillations occur in epidemiological data, this may be a signal that there are shortcomings in the public health system generating that information.
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SciScore for 10.1101/2020.06.08.20123786: (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
Software and Algorithms Sentences Resources Initially, a power spectrum analysis on the raw data was performed using MATLAB ( MATLABsuggested: (MATLAB, RRID:SCR_001622)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: We detected the following sentences addressing limitations in the study:Regardless of the above caveats, we considered the possibility of biological causes for …
SciScore for 10.1101/2020.06.08.20123786: (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
Software and Algorithms Sentences Resources Initially, a power spectrum analysis on the raw data was performed using MATLAB ( MATLABsuggested: (MATLAB, RRID:SCR_001622)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: We detected the following sentences addressing limitations in the study:Regardless of the above caveats, we considered the possibility of biological causes for oscillation but were able to rule these out. The periodicity at 2.4 days observed in NYC incidence data is very close to the time required between infection and capacity to spread the virus, which was estimated to be 2.3 d (7), bringing up the question of whether this more minor periodicity reflects any biological process. However, such a time delay is will not lead to periodicity in incidence unless it is coupled with periodic bursts of increased interaction and infection, for example, as may be caused by increased activity in the weekend. However, our above analysis of NYC data, demonstrating a strong correlation with testing, ruled out increased infection rate as a significant driver of oscillation, making it unlikely that the time delay of 2.3 days manifests in periodicity in the data. Indeed, the strong correlation of incidence with testing data suggests that the periodicity of 2.3 days does not reflect real periodicity in incidence. This reasoning led us away from any biological explanation of the apparent periodicities. In summary, we observed periodic weekly oscillations in the incidence and mortality data for COVID-19. The oscillations in incidence data showed a very strong correlation to the day-today testing numbers implying that these were directly caused by variation in testing. Mortality oscillations appear to reflect the weekly tempo of reporting, rather than true oscillatio...
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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