Folding in today's data release BC looks like it's on a clear upward trajectory again. This is not good. The time for coordinated counter measures was probably two months ago, but better late than never. This train is moving in the wrong direction and need to get off.
Fraser is driving this trend, and we might want to think about a regionally differentiated response. But it's not clear to me that the boundaries between Fraser and Vancouver Coastal at that meaningful, looking at finer geographies would make it easier to tailor the response.
For comparison, people in SK are worried about their 7 day incidence reaching 5 cases per 100k in the near future. From BC's perspective those numbers sounds really nice, Fraser is at 33 right now and Vancouver Coastal is at 25.
And to round this off, here is a quick run of the BC dynamic compartmental model with the new data added in. Also not looking encouraging, it tells pretty much the same story as the STL decomposition. Just in case someone is wondering, a rising curve is not a good thing.
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Catching up with yesterday's press briefing, some of the answers of the PHO are disappointing. On including private tests: "some of those are included", "even without those our testing rates remain low". What are the rates? How many are "some"?
And why has the PHO, week after week, been pointing to positivity rates that include private routine asymptomatic testing as evidence that we are doing well when they now nonchalantly point out that this (of course) skews the positivity rate.
The blurring of lines between routine asymptomatic testing with asymptomatic testing in response to an exposure event is also very unhelpful. The PHO must understand the difference, and blurring the line between these is distracting from prioritizing effective TTI strategies.
The question why Canada learned so little from its SARS experience is interesting. After all, much of the success of Taiwan fighting Covid-19 is due to their action plan developed in response to SARS. cbc.ca/news/health/co…
The senior advisor to the original SARS report has written a follow up looking at where things broke down in the Canadian covid-19 response, focusing on failures to protect health care workers and how Canada mismanaged the response. atimeoffear.com
The whole report is worth a close read, but one section that was of particular interest to me is the one on data. And how lessons from data failures during SARS weren’t implemented. While focused on health care workers, the data failures extend far beyond that.
Introducing census tract level T1FF tax filer time data for years 2000 to 2017 at the census tract level as open data. Available for mapping on CensusMapper or API access via web interfaces or {cancensus} R📦. Made possible through a project with CMHC. doodles.mountainmath.ca/blog/2020/04/2…
The data is annual, so it's great for timelines. Census tract level enables to see neighbourhood level change. And T1FF data goes beyond just income variables, here is a view into the annual change of 5-11 year olds 2001 through 2017.
Of course the data has lots of income related variables. Here is the change in (inflation-adjusted) median couple family income between 2001 and 2017.
Good to see open data. Should allow programmatic back-filling of data @ishaberry2 and @covid_canada have been collecting manually. Does not yet contain today’s new numbers, hopefully they will improve update frequency.
Would love to see this kind of data from other provinces too. BC used to publish case details in daily briefings, but stopped March 13th. The fine folks at @Data_BC have the expertise and technology to implement an API in a heartbeat, all it takes is political will. cc @adriandix
Added benefit of having clean data like this is that we can immediately pull together descriptive stats. And they are reproducible and can automatically be updated when new data is available.
If you are interested in learning about "Reproducible and adaptable workflows using StatCan data in R" but missed my CABE webinar earlier today, the slides are online. mountainmath.ca/cabe
There are other useful resources out there to help with the packages. The documentation has a convenient function reference, as well as introduction and example articles. mountainmath.github.io/cansim/index.h…
Same for the {cancensus} package, complete with instructions of how to get your (free) API key and set it up so it's ready to use in every R session without exposing it when sharing code. mountainmath.github.io/cancensus/inde…