(Joint with Nathan Lauster and cross-posted at HomeFreeSociology) In this post we look at the most recent population (and household) estimates to see if we can detect any signals concerning how the COVID-19 pandemic may have impacted how (and where) we live. This is inherently tricky; lots of things changed during COVID times, including how well our normal methods of estimation work. That makes time series less reliable, even as we’re especially concerned with how conditions have changed.
BC now shares data on the vaccination status of cases and hospitalizations in their weekly Data Reports. This is progress, although calling it “data” is reaching. What is shared is graphs that need manual scraping to be turned into (approximate) data. The numbers themselves aren’t particularly meaningful. Vaccines aren’t 100% effective in preventing symptomatic COVID (approximated by “cases” in BC) or hospitalizations. This means that as more people get vaccinated, there will be more cases and hospitalizations among the vaccinated population.
At this stage in the pandemic there is good news and bad news. The good news is that vaccines are ramping up. And change in dosing schedule means more people are getting some level of protection earlier. The bad news is that variants of concern, or VOCs, are on the rise in BC. We have a decent intuition how each one of these changes our pandemic, but unclear how they interact.
Variants of concern are named such because they are concerning. The ones we worry about are B.1.1.7 (the variant first documented in UK), B.1.351 (the variant first documented in South Africa), and P.1 (the variant first documented in Brazil). Currently, B.1.1.7 is probably the most concerning in BC because we know it is significantly more infectious, with a daily growth rate average of around 10%. This means that in our current BC environment, where we have been seeing a decline by about 0.
The recent dismissal of PHAC modelling by the head of the BCCDC, coupled with some reactions we have seen on Twitter, have led us realize how hard it is for most people to understand exponential growth. Part of the fault lies with most modellers generally assume too much math literacy in others. In particular, we assume that public health officials or relevant policy makers can understand the models. Even though we have seen time and time again that this assumption is very tenuous.