A recent study published on medRxiv* The preprint server compared the different waves of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the United States.

context

Determining the underlying factors contributing to a positive or negative response to pandemic waves has been challenging, primarily due to inconsistent implementation of non-pharmaceutical interventions (NPIs). However, identifying these factors is essential for designing targeted public health measures.

About the study

The present study assessed the relationship between consecutive waves of coronavirus disease 2019 (COVID-19) to assess the relative pandemic response of a county in the United States.

Data on SARS-CoV-2 cases in US counties was obtained from the Center for Systems Science and Engineering at Johns Hopkins University. The study focused on three initial waves of COVID-19 infection. The first wave, which lasted from January 2020 to May 2020, was characterized by the first known detection of SARS-CoV-2 in US counties and the introduction of NPIs. The second wave spanned July 2020 through September 2020 and represented the initial major resurgence of COVID-19 infection in the United States due to reduced NPI reduction. The third wave was observed from September 2020 to January 2021 and represented the last wave of infections before COVID-19 vaccines became available.

A modified algorithm for precise peak detection was used to identify the peaks of the epidemic curves in each county across the three waves of COVID-19. The algorithm identified the highest positive “peak” of each wave period as the peak of the curve for that particular epidemic curve. Additionally, the algorithm determined the last “negative” peak found before the apex of the curve as the point of the previous valley. Additionally, the algorithm noted the start of the next wave as the first positive “peak” detected between the last “negative” peak and the largest positive “peak”.

Results

The study results showed that the highest peaks of infection waves were seen in different counties across the United States in different waves, depending on the dynamics of the COVID-19 pandemic. The Northeastern United States saw initial waves of more severe infections, but smaller subsequent waves compared to the rest of the counties. The southeastern portion of the United States experienced spatial pockets of large waves in the first wave, larger waves in the second, and smaller waves in the third.

Spatial-temporal patterns between subsequent waves were observed in different counties. The southern and midwestern regions of the United States had a strong positive correlation between the first and second waves, while the Midwestern region had a strong correlation between the second and third waves. Additionally, the southeastern regions had a negative relationship between the second and third waves. It was noted that while the Southeast and Plains regions had more lax NPI policies, these two regions reacted contrastingly to later waves of COVID-19.

Spatial correlation analyzes showed that the standardized difference between the slopes of the first and second waves and the second and third waves had strong positive correlations at short distances, changing to a weak negative correlation at the middle distances. However, the first and second wave had positive correlations at short distances, changing to positive correlations at long distances. The magnitude of these shifted to negative correlations at the shortest distance in the northeastern United States and positive correlations at the longest distance in the western United States.

Demographically similar counties had negative relationships between the first and second waves, indicating that these counties’ pandemic response improved significantly with subsequent waves of infection. The counties with the most negative relationships for the first and second waves represented the counties with the highest population densities in urban areas.

Conclusion

The study results revealed strong similarities in responses to different waves of COVID-19 infection at regional and local levels. Additionally, predictors of pandemic responses over different distances shifted from county-level to state-level demographics.

The researchers predicted that the importance of state-level factors should improve over time, as uniform policies undertaken against COVID-19 in early waves are typically broken down into NPI regulations by state. Similarly, an increase in the influence of county-level factors can be predicted, as many states relied more on individual hygiene behaviors and county-level policies. Identifying these factors will better protect areas most at risk of extreme epidemic impact and improve the overall response to the pandemic.

*Important Notice

medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be considered conclusive, guide clinical practice/health-related behaviors, or treated as established information.