Comparing Waves of COVID-19 in the US: Scale of response changes over time
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Abstract
Local response to the SARS-CoV-2 pandemic differed spatially across the United States but the drivers of this spatial variation remain unclear. We approach this open question by studying the relationship between the growth rate of subsequent waves of the pandemic at the county level during the first year of the pandemic, asking whether state or county demographics better explain variation in this relationship. We found clear spatiotemporal patterns in the relationship between the slopes of subsequent waves in a given county. Generally the standardized difference between the growth rates of waves 1 and 2 and waves 2 and 3 were strongly positively correlated over short distances and shifted to a weak negative correlation at intermediate distances. We also found that peer county health group (a categorization of counties by demographic information relevant to public health) explained variation in response better between wave 1 and 2, while state identity was most important between wave 2 and 3. Taken together, we suggest that there are identifiable spatial patterns in pandemic response across the US but that the nature of these patterns change over the course of the pandemic.
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SciScore for 10.1101/2022.03.01.22271713: (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
Software and Algorithms Sentences Resources All models were fit using the the package glmmTMB [16] and we identified the best models using the the MuMIn package to compare model AICc [17, 18]. MuMInsuggested: NoneResults from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:So despite the clear limitations of this analysis we find that it contains useful insight into the patterns and processes of response to repeated waves of COVID-19. Additionally, we note that it is highly likely that multiple variants with different …
SciScore for 10.1101/2022.03.01.22271713: (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
Software and Algorithms Sentences Resources All models were fit using the the package glmmTMB [16] and we identified the best models using the the MuMIn package to compare model AICc [17, 18]. MuMInsuggested: NoneResults from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:So despite the clear limitations of this analysis we find that it contains useful insight into the patterns and processes of response to repeated waves of COVID-19. Additionally, we note that it is highly likely that multiple variants with different transmission rates were circulating during this period and could have contributed to some of the patterns observed. Unfortunately it is beyond the scope of this work to control for variant patterns, given the available data for this time period but we believe our analysis largely avoids these issues for two reasons. First, there is evidence that the first identified variant of concern (alternatively B.1.1.7 or Alpha) was uncommon in the US until after our study period [33]. Second, much of the problem with spatial patterning of variants comes when directly comparing case counts in two locations at the same time. As we compare locations instead at their respective epidemic peaks within a broad date window we likely sidestep much of this concern. Although much has been written about the relative effectiveness of various NPIs as well as the extent of implementation across the US, many gaps remain in our understanding of how humans responded to the repeated waves of COVID-19 over the past 2 years. We found that the predictors of which communities learn (or don’t learn) to deal with a pandemic tended to shift from county-level demographics to state-level policies over the course of the 2020 COVID-19 pandemic in the US. Although we focu...
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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