Recurring Spatiotemporal Patterns of COVID-19 in the United States
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Abstract
We analyzed the waxing and waning patterns (“surges”) of reported SARS-CoV-2 cases from January 1, 2020 through Oct 31, 2021 in all states and provinces (n = 93) in the USA, Mexico, and Canada, and across all counties (N = 3142) in the USA. A correlation matrix of the 576 × 576 daily case incidence rates in the 50 US states generates a distinctive “checkerboard” pattern showing that the epidemic has consisted of seven distinct internally coherent spatiotemporal wave patterns, four in the first year of the epidemic, and three thus far in the second year. Geoclustering of state case rate trajectories reveals three dominant co-varying spatial clusters of similar case rate trajectories, in the northeastern, southeastern and central/western regions of the USA. The spatiotemporal patterns of epidemic year 1 have thus far been repeated (p<.001) in epidemic year 2. The “checkerboard” pattern of the correlation matrix of case trajectories can be closely simulated as three sets of interacting sine waves with annual frequencies of 1:1:2 major cycles per year, corresponding to the northeastern, central/western, and southeastern state clusters. Case incidence patterns in Mexico and Canada have been similar to nearby regions in the southern US and the northern US, respectively. Time lapse videos allow visualization of the wave patterns. These highly structured geographical and temporal patterns, coupled with emerging evidence of annual repetition of these same patterns, show that SARS-CoV-2 case rates are driven at least in part by predictable seasonal factors.
Significance Statement
Local COVID-19 rates wax and wane. Often these epidemic changes are attributed to localized human behavioral factors. Our finding of highly structured continental scale spatiotemporal patterns that cross state and national boundaries, coupled with emerging evidence of annual repetition of these same patterns, shows that COVID-19 transmission is driven at least in part by seasonal factors. Other epidemic factors such as vaccine coverage rates, or emergence of new strains like the Delta variant of SARS-CoV-2 appear to modify, but not totally eclipse, these underlying seasonal patterns. COVID-19 seasonal transmission patterns are associated with, and may be driven by, seasonal weather patterns. Predictability of these patterns can provide opportunities for forecasting the epidemic and for guiding public health preparedness and control efforts.
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SciScore for 10.1101/2021.11.23.21266775: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. 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: We detected the following sentences addressing limitations in the study:This study has several limitations. There are inconsistencies and biases in case reporting among states and provinces and across states. In addition, reported incidence is a function of the number of tests administered which can fluctuate over time. To …
SciScore for 10.1101/2021.11.23.21266775: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. 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: We detected the following sentences addressing limitations in the study:This study has several limitations. There are inconsistencies and biases in case reporting among states and provinces and across states. In addition, reported incidence is a function of the number of tests administered which can fluctuate over time. To reduce these testing and reporting biases, we scaled cases within each state and province to focus on the timing of the waves rather than their intensity. In addition, at the county level, the Gi* statistic incorporates the variability in a county and its neighbors, thus reducing the noise from any individual county. In addition, it is not yet possible to determine exactly how seasons have affected COVID-19 incidence. It is likely that human and virus factors interact differentially at various seasons shaping the epidemic’s waves. As future research investigates this complex relationship, we recommend that policies and decisions should seriously consider the seasonal patterns of COVID-19. This analysis shows that patterns of waxing and waning of COVID-19 incidence at the state and county level are driven by continental-scale seasonal and geographical patterns. This in turn suggests that future state and county level COVID-19 surges should be at least in part predictable, and therefore preventable.
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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