To the honourable member, thank you for that question. It's a really important one.
With traditional public health data, we count things like cases, hospitalizations and deaths, but when we're dealing with a rapidly evolving outbreak, by the time we see a case, we're already too late. There are a whole bunch of things that have already transpired. There has been, at some point earlier, a contact, an exposure. The person exposed might develop symptoms and get tested. By the time they get their test results back, we're already very far behind.
The entire use for these types of data—again I want to highlight de-identified, anonymized data—is ultimately to estimate contact rates in the population. That's what this is all about. It's just estimating how much contact is occurring in the population, because contacts are a leading indicator of what is coming next. Cases tell you that something has already happened in the past. It's a shift from being reactive to being proactive and anticipatory.
What we don't want is to be behind an outbreak. We want to try to get in front of it. We want to try to change the course and trajectory. Pretty much everything we're talking about here really comes down to one thing: trying to inform public health about contact rates in the population and where they're increasing in a way that is a precursor to exposures, cases, hospitalizations and deaths, so that an intervention can happen.
I've been working in the field of emerging outbreaks for my entire career. We know that outbreaks spread quickly. It means that we have to be able to react, understand and move even more intelligently and in a better coordinated manner.
My sense is that, as a physician, I can take care of one patient at a time. These types of analytics can support the public health response that could be impacting not only lives but all of the economic and societal implications we've had to endure for two years.