Github issue (Important): I was diving into the population size estimates that we were using to scale the case trends to 100,000 individuals (trying to figure out the date we were using for population size), but I found an error in the population size estimates. Looking at this line in selection.Rmd:
the source is incorrect (2022Population.tsv). It has a population size for Canada of only 33M, whereas its closer to 40M. I think what might have happened is that when we switched from tracking 70+ to tracking the whole population, the population size of only those <70 was used NOT the sum of <70 and 70+. Here’s the StatsCan table that is the source of the numbers (July 1 2022 population estimates) to see what I mean.
This will cause the case trend data to fall per 100,000 by ~13% (i.e., change the y axis slightly) but should otherwise not affect duotang. Still, an important fix to do.
Github issue (Important): I was diving into the population size estimates that we were using to scale the case trends to 100,000 individuals (trying to figure out the date we were using for population size), but I found an error in the population size estimates. Looking at this line in selection.Rmd:
populationData <- read.table(“resources/2022Population.tsv”, header=T, sep=‘\t’, check.names = F)
the source is incorrect (2022Population.tsv). It has a population size for Canada of only 33M, whereas its closer to 40M. I think what might have happened is that when we switched from tracking 70+ to tracking the whole population, the population size of only those <70 was used NOT the sum of <70 and 70+. Here’s the StatsCan table that is the source of the numbers (July 1 2022 population estimates) to see what I mean.
This will cause the case trend data to fall per 100,000 by ~13% (i.e., change the y axis slightly) but should otherwise not affect duotang. Still, an important fix to do.
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