A seven-day cycle in COVID-19 infection, hospitalization, and mortality rates: Do weekend social interactions kill susceptible people?
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Abstract
Seven-day cycles in numbers of COVID-19 new-cases and deaths are markedly evident in most public databases (e.g. Worldometer, ECDC), but it is unclear whether they reflect systematic artifacts of delays in information reporting/gathering, or have a more profound basis. To address this question we located 11 databases of US states that provide date- authenticated information (actual date of symptom onset and/or specimen collection, or actual hospitalization or death date) that reported more than 1,000 deaths each. Numbers of new cases showed a weekly cyclic pattern in 10 out of 11 states, commonly peaking on weekdays, 2-6 days after the weekend, corresponding with a reported median 5-day lag between infection and the manifestation of clinical symptoms. We postulate that this pattern emerges from interactions with different and/or extended social-circles during weekends, including increased inter-generational meetings, which in turn facilitate transfer of COVID-19 from younger people to older vulnerable individuals. Furthermore, we found weekly periodicity in hospitalizations in 2 out of 2 authenticated databases providing this information. Actual death date, which is more difficult to attribute to individual choice, and is expected to occur approximately 2-3 weeks following hospitalization, showed significant 7-day periodicity in 1 out of 11 states, and a trend in 2 additional states. If weekly peaks in new cases can be truncated by physical/social distancing, especially during weekends, the mortality of COVID-19 may be reduced, or at least hospitalization and mortality curves may be flattened.
Significance Statement
We believe our findings are significant, as it appears that it is difficult for people to grasp an ambiguous “price” for visiting their friends and family, when they cannot be certain that they are not carrying and spreading the virus. Our findings could be used as an effective “tool” to demonstrate such a cost, clearly presented in terms of number of excessive infections. During these days of uncertainly, we believe it is fundamental to provide scientific facts that could illuminate this connection and make it tangible for the general. The amplitude of the cycles we describe here is such that many thousands of infections could be averted by carefully scrutinizing local policies, medical practice, and social norms.
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SciScore for 10.1101/2020.05.03.20089508: (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
Experimental Models: Organisms/Strains Sentences Resources Finally, for the purpose of comparison of the detrended time series with a fixed 7-day periodic sinusoidal pattern, we fitted the following model y (t) = b1 * (sin (2πt/c + 2πt/c + b2)) + b3 where b1 represents the amplitude, b2 phase shift and b3 an offset while c was held constant to match the desired period of 7 days. 2πt/c + 2πt/c + b2)) + b3 where b1suggested: NoneSoftware and Algorithms Sentences Resources Next, autocorrelations were calculated (implemented in SPSS, Version 25), using lags ranging between 1 and 16 days. SPSSsuggested: (SPSS, RRID:SCR_002865)The fit was … SciScore for 10.1101/2020.05.03.20089508: (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
Experimental Models: Organisms/Strains Sentences Resources Finally, for the purpose of comparison of the detrended time series with a fixed 7-day periodic sinusoidal pattern, we fitted the following model y (t) = b1 * (sin (2πt/c + 2πt/c + b2)) + b3 where b1 represents the amplitude, b2 phase shift and b3 an offset while c was held constant to match the desired period of 7 days. 2πt/c + 2πt/c + b2)) + b3 where b1suggested: NoneSoftware and Algorithms Sentences Resources Next, autocorrelations were calculated (implemented in SPSS, Version 25), using lags ranging between 1 and 16 days. SPSSsuggested: (SPSS, RRID:SCR_002865)The fit was computed with a simplex search method as part of the Matlab Optimization Toolbox (Matworks Inc). 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: 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.
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