Community movement and COVID-19: a global study using Google's Community Mobility Reports

Abstract

Google's 'Community Mobility Reports' (CMR) detail changes in activity and mobility occurring in response to COVID-19. They thus offer the unique opportunity to examine the relationship between mobility and disease incidence. The objective was to examine whether an association between COVID-19-confirmed case numbers and levels of mobility was apparent, and if so then to examine whether such data enhance disease modelling and prediction. CMR data for countries worldwide were cross-correlated with corresponding COVID-19confirmed case numbers. Models were fitted to explain case numbers of each country's epidemic. Models using numerical date, contemporaneous and distributed lag CMR data were contrasted using Bayesian Information Criteria. Noticeable were negative correlations between CMR data and case incidence for prominent industrialised countries of Western Europe and the North Americas. Continent-wide examination found a negative correlation for all continents with the exception of South America. When modelling, CMR-expanded models proved superior to the model without CMR. The predictions made with the distributed lag model significantly outperformed all other models. The observed relationship between CMR data and case incidence, and its ability to enhance model quality and prediction suggests data related to community mobility could prove of use in future COVID-19 modelling.

Spatial-Temporal Relationship Between Population Mobility and COVID-19 Outbreaks in South Carolina: Time Series Forecasting Analysis

“…Population mobility could reflect the impacts of NPIs, reopening policies, and public holidays, and estimate social movement during the current COVID-19 pandemic. It is closely related to COVID-19 outbreaks, which is in accordance with the findings of prior research [ 9 , 11 , 17 - 19 ]. This study adds value to previous studies by examining the impacts of population mobility on COVID-19 incidence at both the state and county level in South Carolina.…”

“…Existing literature has suggested that population mobility may reflect the influences (both positive and negative) of NPIs, reopening actions, and public holidays [ 7 - 9 ]. For instance, in the early stages of the COVID-19 pandemic, the governor of South Carolina issued a series of NPIs, such as shelter-in-place and the closure of schools and nonessential businesses, to reduce social interaction.…”

“…Indeed, the tremendous volume of user-generated geoinformation from social media enables the real-time or near real-time surveillance of population mobility and provides timely data on how population mobility changes in response to different phases of the COVID-19 outbreak, policy reactions, and public holidays [ 14 - 16 ]. Several studies have leveraged mobility data from social media (eg, Google, Facebook, Twitter) to investigate the relationship between population mobility and COVID-19 transmission [ 9 , 11 , 17 - 19 ]. These studies identified a consistently positive relationship between population mobility and COVID-19 incidence.…”

Spatial-temporal relationship between population mobility and COVID-19 outbreaks in South Carolina: A time series forecasting analysis

“…One possible explanation for this finding is that we did not capture or account for the influences of contextual factors (i.e., population density) and the roles of mitigating factors (e.g., wearing face mask, practicing social distancing) [16,20,21]. Additionally, the Twitter-based mobility did not differentiate the social movement at different locations, such as movement around parks, workplace, and retail places, which show different impacts on COVID-19 incidence [6]. Furthermore, in this study, we only captured population mobility at state- and county- levels while population mobility at zip code level might provide more accurate prediction.…”

The Relationship between Mobility and COVID-19 in Germany: Modeling Case Occurrence using Apple's Mobility Trends Data

“…provides a comparable source of movement data from users of its applications: Google's "Community Movement Trends." 12 Another limitation is that data are obtained only from those users seeking directions, and presumably only from those mobile in unfamiliar locations, and is thus only an indirect indicator of general community mobility. It could be that there was increased reluctance to travel in unfamiliar locations during the COVID-19 pandemic, and levels of mobility in familiar well-known locations differed from those recorded by this dataset.…”