If you’re a statistical “what if” type of person, I can relate. I’ve been doing a lot of that over the last week or two. I’ve found some tools and resources that are heavy on statistical probability and solid information rather than panicked hyperbole or “head in the sand” denial. I thought I’d share them with you.
The one source I’ve been following the longest is Worldometer’s Coronavirus page, which is updated daily.
You can drill down to breakdowns for most countries. I’ve been particularly looking at results in example countries that are ahead of us on the curve: on the plus side: China and South Korea. On the negative side: Italy and Spain. The US has further breakdowns by state. Keep on eye on Washington, which will be soon getting to the point where they’ll see if their efforts at lockdown are being effective. Governor Jay Inslee did a statewide lockdown on March 15, the first state to do so. What we want to see if that starts “bending the curve” in the right direction. We should start to see trends in the next week or so.
One note of caution on looking at these numbers. You have to factor in the ramp up of testing, which will identify many more new cases. While this looks scary, it’s very much a good thing. Increased testing is one of the most important steps in slowing down the spread.
Canadian Specific Numbers
Canada does not have a province by province breakdown on Worldometer. The best site I’ve seen for Canada is from the Globe and Mail.
This tool does offer provincial breakdowns. Again, we want to be watching the daily new case graphs to see if the curve starts to bend. Provinces that were leaders in this regard are the ones that have been hit the hardest: BC, Ontario and Alberta. In BC we’ve been stepping towards total lock down for the last week or so. On Friday, we finally shut all restaurants, so we’re about a week behind Washington State in this regard. Ontario was a little bit ahead of us.
If you prefer a map-based interface, other tools you might want to check out are Bing’s Covid Tracker:
Bing has done a nice job here, particularly if you’re in the US. You can drill down to very specific location based tracking if you’re American. It’s less useful for Canadians. I also want to see new case incident rates, which are missing.
New on the scene is Google’s Covid Tracker
To be honest, I was expecting a lot more from Google. I know it’s just been rolled out, but Bing is miles ahead in functionality
If you want to see what might happen, you need to drill down on locations that were aggressive in implementing lockdowns and see what is happening there on a day by day – new cases and new deaths – basis. Remember, there is a 7 – 14 day incubation period, so you need to factor that in. Social distancing and Shelter at Home strategies will take 2 to 3 weeks to show up on these graphs.
Sound Statistics and Modelling
Tomas Pueyo has done an absolutely stellar job of taking available information and modelling out what we might expect to see. In two posts on Medium, he has knocked it out of the park. If you need some solid statistical arguments why you should keep your ass on your couch, you’ll find it here. The first post came on March 10. It convinced me to “shelter at home”.
(By the way, I’ll be using terms around which there is a lot of confusion currently. See this guide about what these terms mean)
Pueyo, who is the VP of Growth at Course Hero, has basically assembled a team of academics, health authorities and quants to “hack” an approach to saving our collective lives. His original post has been read over 40 million times and has been translated into over 30 languages. The science is sound here. His message is straight forward and urgent: stay the fuck home.
The first post, published March 10, is entitled: Coronavirus: Why You Must Act Now:
The second post from March 19 is called: Coronovirus: The Hammer and The Dance
Neither of these posts are easy reading, but it is essential that you do. Pueyo does get into the weeds on his statistical reasoning, but it’s the best analysis I’ve seen about what we might expect.
The final resource I’ll point you to are a few statistical models I’ve found that allow you to do some what-if modelling for your own circumstances. By far the best is the Epidemic Calculator on Github:
It looks a little daunting at first, but there are really only a few adjustments you need to make. On the bottom, you can leave almost everything in the default position. The inputs are based on the latest information we have on Covid-19. The one you might want to change is the Population input. Set this for your home country, region or even city if you want.
What you want to change are the two slider controls on the top, the Intervention Threshold and the Rt Factor. These two work together, one the timing of actions and the other the severity of actions. The R factor is the transmission rate (Pueyo talks about this extensively). It appears that Covid currently has an R factor of about 2.2, which means that every infected person will infect 2.2 other people. What we want is to get that under 1. Until we do that, the disease spreads exponentially.
Drag the Intervention slider to see the impact of delaying action. Then adjust the slider to the right to see why staying at home is so important. If anything drove it home for me, this did.
There are other models out there. If you like Canadian Content in your statistical models, there is also this one from Memorial University:
Why is This Important?
The biggest problem with what we’re about to go through is the tendency to either panic or to not be aware of the urgency of the situation. Both can be equally dangerous.
It’s so important to know what might come. We have a couple of significant obstacles in this regard:
The Cause/Effect Gap
First, we are dealing with the incubation lag, that frustrating delay between what we do today and when we begin to see the payoff from it. For me, statistical analysis is the best way to drive that point home. With it, its abundantly clear to see why we need to act now and act aggressively.
The Things We Can’t See
The other problem is underestimating the number of people that are already infected. Remember, some of those infected may never show symptoms but still be contagious. Others will show symptoms at 5 or 6 days but will be contagious before that. You can’t look at the number of confirmed cases in your area and get any feeling of security from that. You can be sure the number of actual infections is much higher. This was the same dangerous path that Italy and Spain went down.
For me this has been a roller coaster ride. I need information – good information that’s grounded in fact based research and reasoning. There is far too much bad information out there. That is why I wanted to share these resources. As near as I can tell, this will give you the best baseline of where we’re at and what we need to do going forward. It is scary shit. So the last thing I’ll leave with are some tips of how to cope when it all gets too much: a conversation with psychologist and mental health expert Dr. Reyman Abdulrehman.