Characterizing collective physical distancing in the U.S. during the first nine months of the COVID-19 pandemic

Klein, Brennan; LaRock, Timothy; McCabe, Stefan; Torres, Leo; Friedland, Lisa; Kos, Maciej; Privitera, Filippo; Lake, Brennan; Kraemer, Moritz U. G.; Brownstein, John S.; Gonzalez, Richard; Lazer, David; Eliassi-Rad, Tina; Scarpino, Samuel V.; Vespignani, Alessandro; Chinazzi, Matteo

doi:10.48550/arxiv.2212.08873

Cited by 4 publications

(3 citation statements)

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“…During the reopening phase, we found that mobility quickly rebounded within the first week (although it did not return to the pre-outbreak level). This recovering trend is substantially different from some European and US locations where the rebound was much slower, possibly due to the persistence of the epidemic or different levels of lockdown fatigue ( 12 , 17 , 24 , 25 ). Within the Shanghai population, we found a slower mobility recovery during reopening among older adults (70+ y), which suggests possible spontaneous choices to limit mobility to minimize the risk of infection given widespread information about the increased risk of developing severe symptoms by age if infected.…”

Section: Discussioncontrasting

confidence: 72%

Heterogeneous changes in mobility in response to the SARS-CoV-2 Omicron BA.2 outbreak in Shanghai

Zhang,

Tan,

Peng

et al. 2023

Proc. Natl. Acad. Sci. U.S.A.

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The coronavirus disease 2019 (COVID-19) pandemic and the measures taken by authorities to control its spread have altered human behavior and mobility patterns in an unprecedented way. However, it remains unclear whether the population response to a COVID-19 outbreak varies within a city or among demographic groups. Here, we utilized passively recorded cellular signaling data at a spatial resolution of 1 km × 1 km for over 5 million users and epidemiological surveillance data collected during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron BA.2 outbreak from February to June 2022 in Shanghai, China, to investigate the heterogeneous response of different segments of the population at the within-city level and examine its relationship with the actual risk of infection. Changes in behavior were spatially heterogenous within the city and population groups and associated with both the infection incidence and adopted interventions. We also found that males and individuals aged 30 to 59 y old traveled more frequently, traveled longer distances, and their communities were more connected; the same groups were also associated with the highest SARS-CoV-2 incidence. Our results highlight the heterogeneous behavioral change of the Shanghai population to the SARS-CoV-2 Omicron BA.2 outbreak and the effect of heterogenous behavior on the spread of COVID-19, both spatially and demographically. These findings could be instrumental for the design of targeted interventions for the control and mitigation of future outbreaks of COVID-19, and, more broadly, of respiratory pathogens.

show abstract

Section: Discussioncontrasting

confidence: 72%

Heterogeneous changes in mobility in response to the SARS-CoV-2 Omicron BA.2 outbreak in Shanghai

Zhang,

Tan,

Peng

et al. 2023

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

show abstract

“…During the reopening phase, we found that mobility quickly rebounded within the first week (although it did not return to the pre-outbreak level). This recovering trend is substantially different from some European and US locations where the rebound was much slower, possibly due to the persistence of the epidemic or different levels of lockdown fatigue (12,17,21,22). Within the Shanghai population, we found a slower mobility recovery during reopening among older adults (70+ years), which suggests possible spontaneous choices to limit mobility to minimize the risk of infection given widespread information about the increased risk of developing severe symptoms by age if infected.…”

Section: Discussioncontrasting

confidence: 62%

Heterogeneous changes in mobility in response to the SARS-CoV-2 Omicron BA.2 outbreak in Shanghai

Zhang

Tan

Peng

et al. 2023

Preprint

View full text Add to dashboard Cite

The coronavirus disease 2019 (COVID-19) pandemic and the measures taken by authorities to control its spread had altered human behavior and mobility patterns in an unprecedented way. However, it remains unclear whether the population response to a COVID-19 outbreak varies within a city or among demographic groups. Here we utilized passively recorded cellular signaling data at a spatial resolution of 1km x 1km for over 5 million users and epidemiological surveillance data collected during the SARS-CoV-2 Omicron BA.2 outbreak from February to June 2022 in Shanghai, China, to investigate the heterogeneous response of different segments of the population at the within-city level and examine its relationship with the actual risk of infection. Changes in behavior were spatially heterogenous within the city and population groups, and associated with both the infection incidence and adopted interventions. We also found that males and individuals aged 30-59 years old traveled more frequently, traveled longer distances, and their communities were more connected; the same groups were also associated with the highest SARS-CoV-2 incidence. Our results highlight the heterogeneous behavioral change of the Shanghai population to the SARS-CoV-2 Omicron BA.2 outbreak and the its effect on the heterogenous spread of COVID-19, both spatially and demographically. These findings could be instrumental for the design of targeted interventions for the control and mitigation of future outbreaks of COVID-19 and, more broadly, of respiratory pathogens.

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“…We also characterize the shift that occurred in the baseline patterns of indoor activity seasonality during the COVID-19 pandemic. We note that this seasonal variation in the propensity of human activity to be indoors differs from the variation in overall rates of contact or mobility, which does not appear to be highly seasonal (Figure 1—figure supplement 1, [38]). Lastly, we fit non-linear models to the indoor activity metric at baseline, comparing the ability of a simple model to capture seasonal variation in transmission risk.…”

Section: Resultsmentioning

confidence: 99%

Disentangling the rhythms of human activity in the built environment for airborne transmission risk: an analysis of large-scale mobility data

Susswein

Rest

Bansal

2022

Preprint

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Since the outset of the COVID-19 pandemic, substantial public attention has focused on the role of seasonality in suppressing transmission. Misconceptions have relied on seasonal mediation of respiratory diseases driven solely by environmental variables. However, seasonality is expected to be driven by host social behavior, particularly in highly susceptible populations. A key gap in understanding the role of social behavior in respiratory disease seasonality is our incomplete understanding of the seasonality of indoor human activity. We leverage a novel data stream on human mobility to characterize activity in indoor versus outdoor environments in the United States. We use a mobile app-based location dataset encompassing over 5 million locations nationally. We classify locations as primarily indoor (e.g. stores, offices) or outdoor (e.g. playgrounds, farmers markets), disentangling location-specific visitor counts into indoor and outdoor, to arrive at a fine-scale measure of indoor to outdoor human activity across time and space. We find the proportion of indoor to outdoor activity during a baseline year is seasonal, peaking in winter months. The measure displays a latitudinal gradient with stronger seasonality at northern latitudes and an additional summer peak in southern latitudes. We statistically fit this baseline indoor-outdoor activity measure to inform incorporation of this complex empirical pattern into infectious disease dynamic models. However, we find that the disruption of the COVID-19 pandemic caused these patterns to shift significantly from baseline, and the empirical patterns are necessary to predict spatio-temporal heterogeneity in disease dynamics. Our work empirically characterizes, for the first time, the seasonality of human social behavior at a large-scale with high spatio-temporal resolution, and provides a parsimonious parameterization of seasonal behavior that can be included in infectious disease dynamics models. We provide critical evidence and methods necessary to inform the public health of seasonal and pandemic respiratory pathogens and improve our understanding of the relationship between the physical environment and infection risk in the context of global ecological change.

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Characterizing collective physical distancing in the U.S. during the first nine months of the COVID-19 pandemic

Cited by 4 publications

References 0 publications

Heterogeneous changes in mobility in response to the SARS-CoV-2 Omicron BA.2 outbreak in Shanghai

Heterogeneous changes in mobility in response to the SARS-CoV-2 Omicron BA.2 outbreak in Shanghai

Heterogeneous changes in mobility in response to the SARS-CoV-2 Omicron BA.2 outbreak in Shanghai

Disentangling the rhythms of human activity in the built environment for airborne transmission risk: an analysis of large-scale mobility data

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