In-depth analysis uncovers north-south oscillations in COVID-19 cases, revealing how regional waves shape the pandemic across the U.S. and providing key insights for future health interventions.

Study: Oscillating spatiotemporal patterns of COVID-19 in the United States. Image Credit: Cryptographer / Shutterstock.com

In a recent study published in the journal Scientific Reports, researchers analyze previously unrecognized spatiotemporal oscillations in coronavirus disease 2019 (COVID-19) cases throughout the United States.

COVID-19 trends in the U.S.

Since January 2020, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is the pathogen that causes COVID-19, has infected nearly 100 million individuals, over one million of whom have succumbed to the disease in the U.S. Spatiotemporal patterns of SARS-CoV-2 transmission have been observed since emerging in the U.S., with COVID-19 cases often surging during the winter and varying based on different regions.

Further research is needed to elucidate the mechanisms involved in these patterns, enhancing our understanding of disease dynamics, improving the accuracy of forecasting future surges, and optimizing public health interventions.

About the study 

Daily COVID-19 case rates were acquired from The New York Times for the 48 continental states. Data were obtained from the beginning of the epidemic through August 15, 2022, which led to a total of 937 days of observation included in the analysis. Although data were available beyond this date, reporting accuracy and frequency diminished, particularly in the later stages of the pandemic. 

For county-level analyses, 3,108 counties within the continental U.S. were included, which led to 2.9 million data points and 150 million reported cases by day and county. The log of case rates for every 100,000 people was used for all analyses due to its log-normal distribution.

Before taking the log, a 14-day moving average was applied to smooth trends. A seven-day window was not selected for the analysis because some counties reported COVID-19 cases biweekly.

Hierarchical clustering was used to compare state-by-state daily case rates, with Pearson correlation coefficients utilized as the distance metric. Ward’s method minimized within-cluster variance during clustering. Based on Spearman’s correlation coefficient, a heatmap of state rankings across dates was used to visualize shifts in state-level case trends over time and reveal oscillatory ranking patterns.

For county-level analysis, singular value decomposition (SVD) was employed to decompose log case rates and identify the most significant spatiotemporal patterns.

Study findings 

A prominent north-south oscillation of COVID-19 case rates was identified using state-level data. The hierarchical clustering analysis revealed distinct clusters of states, with a boundary between northern and southern states observed along the 37°-38° north latitude.

A cross-date state rank correlation matrix showed a structured “checkerboard” pattern, with blocks of internally high correlations lasting about three months. These blocks indicated recurring cycles of case rate similarities across states. The matrix also revealed parallel diagonal bands, with strong correlations observed in state rankings from one and two years earlier.

The emergence of the SARS-CoV-2 Omicron variant temporarily disrupted this pattern. However, these trends resumed following the peak of Omicron cases, in which consistent oscillations were observed between northern and southern regions of the U.S.

The first four SVD modes accounted for over 85% of the variance in case rates across counties. Mode I, which captured the overall cyclical trends in case rates, explained 75% of the variance. Comparatively, mode II showed a clear north-south oscillation in the eastern U.S., with high case rates alternating between the northeast and southeast over time.

Mode III highlighted oscillations in the northern U.S., particularly during the fall and winter, with high case rates in northcentral and northeastern regions observed during the fall and spring, respectively. Mode IV, which explained less variance, correlated with specific waves in the Northeast and Midwest regions.

These spatiotemporal patterns demonstrated that COVID-19 case rates follow regular and oscillatory patterns across latitudes, with epidemic waves that traveled between the north and south regions over time. Although these oscillations were prominent by latitude, some evidence of additional structure by longitude was also observed to a lesser degree.

Conclusions 

The COVID-19 epidemic generated an unprecedented spatiotemporal dataset, with approximately three million case data points recorded across 3,108 US counties over 937 days. Despite reporting imperfections, these data provided a valuable opportunity for advanced computational analyses.

Researchers discovered previously unrecognized oscillatory trends in the epidemic by analyzing recurring patterns. One significant finding was the Eastern U.S. COVID-19 Oscillation (EUCO), which is a north-south pattern where case rates alternated around the 37-degree north latitude. This oscillation accounted for only 5.2% of the total variance; however, it explained much of the regional variation in the northeast and southeast.