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- Badr HS
- You h
- Marshall M
- Dong E
- Squire MM
- Gardner LM
We reveal the strong correlation between population-level mobility patterns and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission patterns for the 25 most affected counties in the US in the early stages of the COVID-19 pandemic. We have since expanded the analysis to include all affected counties in the US, and a longer time period spanning from March 16 to September 16, during which mobility and local outbreaks exhibited patterns of much more complex growth and decline. Furthermore, we applied the analysis to subgroups of counties grouped spatially and temporally according to the magnitude of their outbreaks and the rates of growth (or decline) during specified periods, in an effort to distinguish the role of mobility patterns in the transmission of SARS- CoV-2 as a function of this characteristic shoot dynamics. Critically, the results of our most comprehensive analysis reveal that the strong linear association between mobility and previously observed case growth rates is absent after April.
to calculate the COVID-19 growth rate index and aggregated anonymized location data from SafeGraph to estimate the time-varying mobility index. The methodology of Badr and colleagues was used to calculate each metric and lagged correlations. The correlation distribution for all counties at different time periods is illustrated in the appendix, with the counties grouped by outbreak magnitude (low, medium, or high) or outbreak phase (growth rate increases, decreases, or does not increase or decreases). Both sets of analyzes confirm a uniquely strong association between mobility patterns and SARS-CoV-2 transmission in March-April, while clustering analysis reveals that the relationship is strongest for those counties with larger outbreaks that were experiencing a decline in case growth (which aligns with the 25 counties evaluated in our previous study
- Badr HS
- You h
- Marshall M
- Dong E
- Squire MM
- Gardner LM
). However, after April, there is no consistent and generalizable relationship between mobility patterns and SARS-CoV-2 transmission between counties or for any subgroup.
- Gatalo O
- Tseng K
- Hamilton A
- Lin G
- Lowercase E.
suggest that mobility plays a less important role in the transmission of SARS-CoV-2 than other adopted behavior changes and NPIs, such as wearing masks, washing hands, maintaining physical distance, avoiding large gatherings, and closing schools. While it is possible that transmission during the initial phase of the pandemic in the US was more sensitive to mobility patterns, the stronger correlation revealed in March-April is more likely due to the initial adoption of many NPIs in parallel, while erratic policies and highly variable behaviors at the individual level after the initial shutdown period confuse the role of mobility. Furthermore, after the initial phase of the pandemic, complex patterns of movement between counties, coupled with variable epidemic establishment and seeding patterns, may have further affected the contribution of mobility to growth rate.
- Grantz KH
- Meredith HR
- Cummings DAT
- et al.
Based on our most recent findings, parameterization of SARS-CoV-2 transmission models using only mobility data is likely to result in poor performance models and inaccurate forecasts. The absence of a significant effect of mobility on SARS-CoV-2 transmission in all counties after April, even when the magnitude and phase of the outbreak is taken into account, suggests that there are likely more critical factors than mobility. alone to control COVID-19. More research is needed to quantify the impact of each NPI and their interactions; however, this analysis requires data that is not currently available, specifically high-resolution data on compliance rates for each NPI over time.
We declare that there are no competing interests.
Supplementary material
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Posted: November 02, 2020
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DOI: https://doi.org/10.1016/S1473-3099(20)30861-6
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© 2020 Published by Elsevier Ltd.
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