Other Mobility Studies

[jiweiqi]

• http://socialmobility.covid19dataresources.org
  Social distancing is an important component of the response to the novel Coronavirus (COVID-19) pandemic. Minimizing social in- teractions and travel reduces the rate at which the infection spreads, and ”flattens the curve” such that the medical system can better treat infected individuals. However, it remains un- clear how the public will respond to these poli- cies. This paper presents the Twitter Social Mobility Index, a measure of social distancing and travel derived from Twitter data. We use public geolocated Twitter data to measure how much a user travels in a given week. We find a large reduction in travel in the United States after the implementation of social distanc- ing policies, with larger reductions in states that were early adopters and smaller changes in states without policies.

[jiweiqi]

• https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30553-3/fulltext#%20
  Badr, H. S., Du, H., Marshall, M., Dong, E., Squire, M. M., & Gardner, L. M. (2020). Association between mobility patterns and COVID-19 transmission in the USA: a mathematical modelling study. The Lancet Infectious Diseases.

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

[jiweiqi]

• https://arxiv.org/pdf/2008.06549.pdf
  Wang, H., Ghosh, A., Ding, J., Sarkar, R., & Gao, J. (2020). Targeted Interventions Reduce the Spread of COVID-19: Simulation Study on Real Mobility Data. arXiv preprint arXiv:2008.06549.

Various intervention methods have been introduced worldwide to slow down
the spread of the SARS-CoV-2 virus, by limiting human mobility in different ways. While large scale lockdown strategies are effective in reducing the
spread rate, they come at a cost of significantly limited societal functions. We
show that natural human mobility has high diversity and heterogeneity such
that a small group of individuals and gathering venues play an important role
in the spread of the disease. We discover that interventions that focus on protecting the most active individuals and most popular venues can significantly
reduce the peak infection rate and the total number of infected people while retaining high levels of social activity overall. This trend is observed universally
in multi-agent simulations using three mobility data sets of different scales,
resolutions, and modalities (check-ins at seven different cities, WiFi connection events at a university, and GPS traces of electric bikes), and suggests that
strategies that exploit the network effect in human mobility provide a better
balance between disease control and normal social activities.