[BostonCard]

https://www.nature.com/articles/s41586-020-2923-3#Abs1

Good work (and with a Stanford connection)

Our model predicts that a small minority of “superspreader” POIs account for a large majority of infections and that restricting maximum occupancy at each POI is more effective than uniformly reducing mobility. Our model also correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups2–8 solely from differences in mobility: we find that disadvantaged groups have not been able to reduce mobility as sharply, and that the POIs they visit are more crowded and therefore higher-risk. By capturing who is infected at which locations, our model supports detailed analyses that can inform more effective and equitable policy responses to COVID-19.

Not surprising the POIs:

Certain categories of POIs also contributed far more to infections (e.g., full-service restaurants, hotels), although our model predicted time-dependent variation in how much each category contributed (ED Figure 2). For example, restaurants and fitness cent- ers contributed less to predicted infections over time, likely due to lockdown orders closing these POIs, while grocery stores remained steady or even grew in their contribution, which concords with their status as essential businesses.

Figure 4d if you can download the full paper is the most relevant.

BC

[BostonCard]

https://www.nature.com/articles/s41586-020-2923-3#Abs1

Good work (and with a Stanford connection)

Our model predicts that a small minority of “superspreader” POIs account for a large majority of infections and that restricting maximum occupancy at each POI is more effective than uniformly reducing mobility. Our model also correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups2–8 solely from differences in mobility: we find that disadvantaged groups have not been able to reduce mobility as sharply, and that the POIs they visit are more crowded and therefore higher-risk. By capturing who is infected at which locations, our model supports detailed analyses that can inform more effective and equitable policy responses to COVID-19.

Not surprising the POIs:

Certain categories of POIs also contributed far more to infections (e.g., full-service restaurants, hotels), although our model predicted time-dependent variation in how much each category contributed (ED Figure 2). For example, restaurants and fitness cent- ers contributed less to predicted infections over time, likely due to lockdown orders closing these POIs, while grocery stores remained steady or even grew in their contribution, which concords with their status as essential businesses.

Figure 4d if you can download the full paper is the most relevant.

BC

[BostonCard]

Also, you can play with the models:

http://covid-mobility.stanford.edu/

By far, the biggest contributors to increasing cases are dine-in restaurants.  Everything else makes a more marginal contribution.

BC

[BostonCard]

Also, you can play with the models:

http://covid-mobility.stanford.edu/

By far, the biggest contributors to increasing cases are dine-in restaurants.  Everything else makes a more marginal contribution.

BC

[oldalum]

great article, and 5 of 7 authors are or were Stanford people, including the 3 first authors and the senior author, so I'd say it's a Stanford article.
I hope people are trying to understand better the reasons for the differences among MSAs in these data. For examples, grocery stores accounted for a relatively small but increasing percentage of new infections everywhere except for the Bay Area where they remained basically constant, and Philadelphia MSA where the percentage increased enormously -- why? Did the Bay Area restrict more tightly the numbers of people who could be in the store at once, or did we have greater penetration of grocery delivery services than other areas?

[oldalum]

great article, and 5 of 7 authors are or were Stanford people, including the 3 first authors and the senior author, so I'd say it's a Stanford article.
I hope people are trying to understand better the reasons for the differences among MSAs in these data. For examples, grocery stores accounted for a relatively small but increasing percentage of new infections everywhere except for the Bay Area where they remained basically constant, and Philadelphia MSA where the percentage increased enormously -- why? Did the Bay Area restrict more tightly the numbers of people who could be in the store at once, or did we have greater penetration of grocery delivery services than other areas?

[M T]

I haven't played with their numbers or given the study a critical read, but it sounds promising.  I like the idea of highlighting what places are getting the most traffic and associating that with transmission.  I'd think that must involve some assumptions about transmissibility.
I'm curious how they distinguish (say) indoor vs outdoor dining (or barber shops, etc.).  I see some biases showing up in the text.

I believe the study will have little or no information regarding the movement of younger children, the homeless, or others without cell phones.  I believe the study was at a time when very few schools were in session.  (I tried to think of any proxy gathering that gets people from many of a community's households into close proximity of each other.  Maybe churches in communities where most attend church regularly or sports events in college towns.)

(Re transmissibility:  if the same number of people are in a store with the same square footage with a 10' ceiling (enclosed) as a 30' ceiling with large open doors (Lowe's or Home Depot), is transmission similar?)

Hopefully, this will result in somewhere like California or SCC adjusting what is open or not at the different epidemic levels.  Even if that doesn't happen (WHY did California open indoor dining??), it gives the public a chance to understand what risks are associated with their activities, and adjust according to their risk tolerance.

[M T]

I haven't played with their numbers or given the study a critical read, but it sounds promising.  I like the idea of highlighting what places are getting the most traffic and associating that with transmission.  I'd think that must involve some assumptions about transmissibility.
I'm curious how they distinguish (say) indoor vs outdoor dining (or barber shops, etc.).  I see some biases showing up in the text.

I believe the study will have little or no information regarding the movement of younger children, the homeless, or others without cell phones.  I believe the study was at a time when very few schools were in session.  (I tried to think of any proxy gathering that gets people from many of a community's households into close proximity of each other.  Maybe churches in communities where most attend church regularly or sports events in college towns.)

(Re transmissibility:  if the same number of people are in a store with the same square footage with a 10' ceiling (enclosed) as a 30' ceiling with large open doors (Lowe's or Home Depot), is transmission similar?)

Hopefully, this will result in somewhere like California or SCC adjusting what is open or not at the different epidemic levels.  Even if that doesn't happen (WHY did California open indoor dining??), it gives the public a chance to understand what risks are associated with their activities, and adjust according to their risk tolerance.