descarteslabs / dl-covid-19 Goto Github PK

The raw data contains information for the City of Bedford, VA, FIPS code 51515 as well as for Bedford County, VA, FIPS code 51019. Unless I am mistaken, the City of Bedford lost its status as an independent city on July 1, 2013, and is currently a town within Bedford County.

See e.g. https://www.cdc.gov/nchs/nvss/bridged_race/county_geography-_changes2015.pdf.

Hi Teams,

Thank you very much for sharing such useful data to the Public. I just found that data on April 20 is missing in https://github.com/descarteslabs/DL-COVID-19/blob/master/DL-us-m50_index.csv. I'm wondering could you please add data on that day? Thanks.

Here the mobility data has been provided at county level. On your website I can see graphs at state level too. I was wondering, since the mobility provided at county level is the median mobility, is there a way to aggregate them to get the mobility for the whole state.

Any idea would be really helpful.

There have been values in the past week for Washington D.C. in excess of 300 and 400 by m50_index. Are these valid data points? Do these suggest the baseline normalization period was incorrect?

Hello,
Your dataset was added to CoronaWhy (https://www.coronawhy.org/) Data Lake on Dataverse as a piece of common COVID-19 data https://datasets.coronawhy.org/dataset.xhtml?persistentId=doi:10.5072/FK2/FGPDWQ
Would you be willing to help with the maintenance of your dataset in Dataverse, e.g. adding the relevant metadata and keeping the dataset up-to-date? That will help to make the dataset findable and accessible for the medical science community.

I expected that there would only be a single entry for each FIPS + date combination.

However, I find that for FIPS code 29189, there are two entries, which differ in terms of values for admin2 and the values for samples, m50 etc. Specifically, there's one set of entries for City of Saint Louis, and another for Saint Louis County.

Is my expectation incorrect?

Hi team,

First of all I wanna thank you for doing such a great analysis on huge scale in such a short time. The mobility data are gonna provide useful insights in containing the COVID-19 as well as any other epidemics in the future. However, I do want to understand more, especially, where did this data originally come from? It seems that I cannot find the specific data source anywhere in Github and the paper. I believe it would be very helpful for the readers to know where these mobility data actually come from to determine if there is any biases in the sampling process.

Once again, thanks for the great work and I will keep updated with any news from your team!

Is the data before March 1st available? if so, it would be good to make some comparison.

My group has been using your mobility data to analyze covid policy responses in the US. One issue we had was that there appear to be some "spikes" in the data for certain locales, where mobility might be 20x or 30x higher than it's typical value. For example on July 17th and 14th in Philadelphia county, there was a huge jump in mobility.

I initially thought this might have been due to the blacklivesmatter protests or something, but it seems too big to be explained by that, and didn't find the same thing in other cities. Similar spikes exist for a number of other counties, but necessarily not at the same date.

• For Guadalupe County, New Mexico, m50, the median of the max-distance mobility, show the following statistics: median 613 Km, minimum 286 Km, maximum 702 Km. This is for a median sample size of 154.

• There are 3 further counties where the median values for m50 over the complete time series so far is of similar order of magnitude. Specifically Culberson County, Texas and Quay as well as Hidalgo County in New Mexico.

• For Carbon County, Wyoming, m50 shows a median of 16 Km. However, there's a maximum of 240 Km. The sample size for this day (2020-03-11) is 448.

Given the methodology, all of these values for m50 seem rather large. (E.g. moving 600 Km away from your starting point in a single day for the sample median in a single day.)