Ballpark first forecast based on simple math
Avg # tests per day for the last 8 days = 150,025 . . .
(fun fact: during that period on 4 out of 7 days fewer people were tested than on the previous day)
Avg # of new tests are positive in the last 7 days = 28,197
(fun fact: the days with the lowest % of positive cases were the days when the # of tests increased the most = test more people and the % positive falls)
Because the variation in the # of new tests is so great on a day to day basis, and there's no clear trajectory of increasing or decreasing rates of testing, I'm going to use the avg # of tests per day.
Because the % of new cases seems to be linked more closely to the # of tests than to the date, I"m going to use the avg % of new cases.
Therefore: I going to start off assuming that 150,025 x 7 = 1,050,175 new tests will be done by 4/26
and that 18.6% of those tests will be positive , so 1,050,175 x .186 = 195,332 new positive cases
195,332 + 749,203 (current count) = 944,535 positive cases by EoD 4/26
using the average of 28,197 new positive cases per day in the last 7 days = 197,379
197,379 + 749,203 = 946,582 positive cases by EoD 4/26
What might impact the rate of positive cases:
Decrease due to social distancing
Increase due to delay between exposure & symptoms
"France . . . went into lockdown on the 17th March 2020. . . . The lockdown reduced the reproductive number from 3.3 to 0.5 (84% reduction).. . . "
I'm assuming that the lockdown in the US will have/is having a similar effect.
Tetlock gave an interview recently:
COWEN: If you took your 10 best superforecasters and brought them into the hedge fund people at Goldman Sachs and you all sat down together, who would be teaching whom?
TETLOCK: [laughs] It’s an interesting experiment. My project manager from the first set of forecasting tournaments, Terry Murray, founded a company, Good Judgment Incorporated, which does things like that. So that’s a proprietary venture. You’d probably want to talk to Terry about how successful or not successful they’ve been in doing that. I think they’ve had some success.
It’s extremely hard to do that. It’s nontrivial. I think there’s a good deal of similarity in the cognitive ability, cognitive-style profiles of superforecasters and the kinds of people you see on the staffs of Goldman Sachs. It would be a tight race.
COWEN: What about the sports betting market, do you think there are inefficiencies in that because they don’t have enough superforecasters?
TETLOCK: I’m not an expert on sports betting. Better to talk to Nate Silver about that.
>2) Don't flip. After a year I looked at all the forecasts that I had flipped on and discovered that if I had not flipped I would have done better!
>3) If really tempted to flip, go to 50% first.
This has to be the most counterintuitive from what I thought I knew, which is to not cling to your positions. I assume what this means is that you *would* flip when there is breaking news, but be careful about flipping simply because your analysis has changed? Or at least be incremental?
If I think something is 75%, and then I do a lot more research and analysis that has made me very much change my mind, I should then put it at 50% and keep it there for awhile? I'll definitely test that out.
I am not suggesting one shouldn't CHANGE a forecast. I wrote my "rules" for myself, and the key word in "Don't flip" is "flip". "Flipping" is, for me, an emotional reaction. It's what I want to do when I've just read something unexpected, as in: "Yikes! Oh shoot! I'm screwed! I've been forecasting all wrong on this! I need to flip my forecast!" It is a panicked rush to judgment. It is frequently an over-reaction, often to just one piece of information. Hence, my rule #3, "go to 50% first". Basically, what those rules I wrote for myself are reminding me to do is to THINK and do more research, not react.
I hope that this helps and affirms your decision to adjust/change a forecast when, after a lot more research and analysis, you conclude that your current forecast is incorrect.