Association between mobility patterns and COVID-19 transmission in the USA: a mathematical modelling study

Abstract

Background Within 4 months of COVID-19 first being reported in the USA, it spread to every state and to more than 90% of all counties. During this period, the US COVID-19 response was highly decentralised, with stay-at-home directives issued by state and local officials, subject to varying levels of enforcement. The absence of a centralised policy and timeline combined with the complex dynamics of human mobility and the variable intensity of local outbreaks makes assessing the effect of large-scale social distancing on COVID-19 transmission in the USA a challenge.Methods We used daily mobility data derived from aggregated and anonymised cell (mobile) phone data, provided by Teralytics (Zürich, Switzerland) from Jan 1 to April 20, 2020, to capture real-time trends in movement patterns for each US county, and used these data to generate a social distancing metric. We used epidemiological data to compute the COVID-19 growth rate ratio for a given county on a given day. Using these metrics, we evaluated how social distancing, measured by the relative change in mobility, affected the rate of new infections in the 25 counties in the USA with the highest number of confirmed cases on April 16, 2020, by fitting a statistical model for each county.Findings Our analysis revealed that mobility patterns are strongly correlated with decreased COVID-19 case growth rates for the most affected counties in the USA, with Pearson correlation coefficients above 0•7 for 20 of the 25 counties evaluated. Additionally, the effect of changes in mobility patterns, which dropped by 35-63% relative to the normal conditions, on COVID-19 transmission are not likely to be perceptible for 9-12 days, and potentially up to 3 weeks, which is consistent with the incubation time of severe acute respiratory syndrome coronavirus 2 plus additional time for reporting. We also show evidence that behavioural changes were already underway in many US counties days to weeks before state-level or local-level stay-at-home policies were implemented, implying that individuals anticipated public health directives where social distancing was adopted, despite a mixed political message.Interpretation This study strongly supports a role of social distancing as an effective way to mitigate COVID-19 transmission in the USA. Until a COVID-19 vaccine is widely available, social distancing will remain one of the primary measures to combat disease spread, and these findings should serve to support more timely policy making around social distancing in the USA in the future.

Characterizing superspreading events and age-specific infectiousness of SARS-CoV-2 transmission in Georgia, USA

“…Our results suggest the shelter-in-place order was effective, and, in all of the counties, the declined below 1 in about 1 wk to 3 wk after the intervention. This is consistent with a recent study which shows that the effect of changes of mobility on reducing transmission may become noticeable after 9 d to 12 d ( 14 ). It is also worth noting that appears to begin to decline 1 wk to 2 wk before the shelter-in-place order in urban areas, and earlier in Dougherty.…”

Decline in Mobility: Public Transport in Poland in the time of the COVID-19 Pandemic

“…Moreover, the highest mobility decrease, regardless the stringency level, is observed in Masovian (MZ), Lower Silesian (DS) and Lesser Poland (MA), while Kuyavian-Pomeranian (KP), West Pomeranian (ZP) and Warmian-Masurian (WN) are characterised by the lowest decline in people's appearance in public transportation system and hubs. Our research results do not correspond with Badr et al (2020), who reveal the strong correlation between mobility patterns and decreasing number of newCOVID-19 cases. There are certainly other drivers/factors explaining voivodeship-specific relationship between changes in mobility in public transport and epidemic status.…”

“…Our research results do not correspond with Badr et al (2020), who reveal the strong correlation between mobility patterns and decreasing number of newCOVID-19 cases. There are certainly other drivers/factors explaining voivodeship-specific relationship between changes in mobility in public transport and epidemic status.…”

“…Glaeser et al (2020) show a visible decrease in urban mobility in USA accompanied by substantial heterogeneity both across space and time. Badr et al (2020) reveal the mobility patterns are strongly correlated with decreased COVID-19 case growth rates for the most affected counties in the USA. Coven and Gupta (2020) and Almagro and Orane-Hutchinson (2020) find disparities in mobility responses to the COVID-19 pandemic rooted in income.…”

Modeling the effects of contact tracing on COVID-19 transmission

“…In the case of apparition of the symptoms related to the disease, they are isolated, tested and hospitalized. Several mathematical models related to the COVID-19 epidemic have been studied (see [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]). In [18], Imai et al conducted computational modeling to establish the size of the disease outbreak in Wuhan.…”