Social vulnerability amplifies the disparate impact of mobility on COVID-19 transmissibility across the United States

Abstract

Although human mobility is considered critical for the spread of the new coronavirus disease (COVID-19) both locally and globally, the extent to which such an association is impacted by social vulnerability remains unclear. Here, using multisource epidemiological and socioeconomic data of US counties, we develop a COVID-19 pandemic vulnerability index (CPVI) to quantify their levels of social vulnerability and examine how social vulnerability moderated the influence of mobility on disease transmissibility (represented by the effective reproduction number, Rt) during the US summer epidemic wave of 2020. We find that counties in the top CPVI quintile suffered almost double in regard to COVID-19 transmission (45.02% days with an Rt higher than 1) from mobility, particularly intracounty mobility, compared to counties in the lowest quintile (21.90%). In contrast, counties in the bottom CPVI quintile were only slightly affected by the level of mobility. As such, a 25% intracounty mobility change was associated with a 15.28% Rt change for counties in the top CPVI quintile, which is eight times the 1.81% Rt change for those in the lowest quintile. These findings suggest the need to account for the vulnerability of communities when making social distancing measures against mobility in the future.

Revealing geographic transmission pattern of COVID-19 using neighborhood-level simulation with human mobility data and SEIR model: A case study of South Carolina

“…CBGs with lower SES might have lower mobility (fewer POI visits) due to relatively low financial status, which results in a lower COVID-19 transmission rate. Huang et al, (2022) ’s work observed a similar trend that mobility reduced less in the less vulnerable counties during the 2020 lockdown summer in the US. Mobility data from SafeGraph shows that the per capita visits to Malls and Full-Service Restaurants of the top decile CBGs in median household income were two times more than the bottom decile ( Fig.…”

“… Levy et al (2022) also documented the disparity of COVID-19 incidences among neighborhood socioeconomics, but they did not observe generality in the study area of three metro regions of the US (San Francisco, Seattle, and Wisconsin). Huang et al, (2022) investigated the correlation between human mobility and COVID-19 transmissibility at county level. They used mobility changes and pandemic vulnerability index to model reproduction numbers, and found that vulnerable counties suffered a higher infectious risk with increasing mobility.…”

Greener Neighbourhoods Show Resilience to the Spread but Not Severity of COVID-19 Infection in South Africa

“…However, some studies (two so far) reported significant positive correlations between greenness and infection [5,42]. In those two studies, greenness was simply approximated.…”

“…In those two studies, greenness was simply approximated. For example, Huang et al [5] estimated greenness as "green space density" which is the proportion of all grass cover, shrubland, and woodland covers in the spatial Unit used in the study, and they correlated this with infection risk measured as "venue density" (number of venues or building visited by confirmed COVID-19 positive cases). In this context, the positive correlation they found simply suggested that green space may attract more visitors, including those tested positive, for outdoor activities, thus leading to a positive correlation [5].…”

“…In response to the pandemic, various non-pharmaceutical preventative measures were taken by different countries around the world (e.g., social distancing, lockdown, working-from-home regulations, self-isolation and quarantine, etc. ; [4][5][6]). Although these measures showed the expected outcome, which was to curb the spread of the COVID-19 infections [7,8], their implementations could not be indefinitely advocated for, given the associated socio-economic consequences, such as psychological damages [9] and economic losses [10].…”