Date: January 19 2021
Summary: How estimations of mobility patterns were used to estimate infections and identify disparities in Census Block Groups
Keywords: ##bibliography #places #interest #policy #bipartite #graphs #counterfactual #mobility #models #archive
S. Chang et al., "Mobility network models of COVID-19 explain inequities and inform reopening," Nature, vol. 589, no. 7840, pp. 82–87, Jan. 2021, doi: 10.1038/s41586-020-2923-3.
Iterative proportional fitting [1] was used to make POI –> CBG networks from SafeGraph data. SafeGraph mobility data was validated against Google mobility data.
THOUGHTS: What is iterative proportional fitting?
Model fits observed daily case counts from 8 March to 9 May 2020
POI hourly infection rate determined via:
Area
Median visit duration
Time-varying density of infectious individuals
Three free time-invariant parameters that scale:
POI transmission rates
CBG transmission rates
Initial amount of exposed individuals
Points of interest for this paper are such as:
Restaurants
Grocery stores
Religious establishments
Certain categories of POIs contributed far more to infections like restaurants and hotels The model predicted time-dependent variation - likely due to policy change.
Model can identify at-risk populations and determined that disadvantaged racial socioeconomic groups face higher rates of infection.
THOUGHTS: This conclusion seems very obvious – of course people are going to have to move and keep doing things.
Counterfactual mobility network creation:
Scaling magnitude of mobility reduction down
Positioning the model at different times
Apply model to counterfactual networks to simulate infection trajectories.
THOUGHTS: What is a counterfactual network? I wonder in what sense they are using the word "counterfactual"?
Limitations of dataset:
Some populations missing
All POIs not ID'd
Does not break up CBGs
The model is sparse. It does not include all real-world disease transmission variables.
COVID-19 superspreader events [2]–[5] motivate risk modeling. Infection rates among different people groups [6]–[12] require modeling effects of the virus on disadvantaged groups.
The observed disparity was driven by a few POIs. Majority of infections from small fraction of superspreader POIs.
"Probably because they cannot work from home as easily." [10]
THOUGHTS: It almost feels insulting that after this entire analysis, there is only one footnote on the topic of what people actually experience. On the flip side, I can empathize where a research group could lack time and/or means to contextualize results. Either way, it feels ripe to build on top of this work/paper to better examine the actual effects of policy adjustments and life changes.
CBGs with fewer white residents had higher predicted infection risks.
THOUGHTS: Seems somewhat odd? This is interesting to me in how they keep bringing up race or economic status as a variable, but never fully commit to an investigation. Feels really weird to talk about issues without actually addressing them.
Mobility data showed average grocery store visits from lower-income CBGs had 59% more hourly visitors per square foot. Median metro area data showed they stayed 17% longer.
Zelko, Jacob. Mobility Network Models of COVID-19 Explain Inequities and Inform Reopening. https://jacobzelko.com/01202021043643-mobility-network-models. January 19 2021.
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