Inevitable. Trump talks about models so the media, ignoring Fauci's caveats, now starts to turn against them: Don’t Believe the COVID-19 Models.
Inevitable. Trump talks about models so the media, ignoring Fauci's caveats, now starts to turn against them: Don’t Believe the COVID-19 Models.
Tradition is not the worship of ashes, but the preservation of fire. ― Gustav Mahler
Chris (04-02-2020)
When computer models create mayhem
As an international banker who finances highly structured multimillion loans in emerging markets for power projects, railroads, toll roads, port expansions, etc., we use sophisticated financial models to predict outcomes and determine areas of potential weakness (stress testing) in order to appropriately underwrite transactions to protect lenders and investors. Often hundreds of millions — or even billions — of dollars ride on these financial projections, and their use is considered critical to making the investment decision. However, in three decades of using computer models, seldom have I seen them accurately project ultimate outcomes. Their real value is determining possible changes in the inputs and how they can impact the project, along with other quantitative and qualitative analysis.
Some of the current hysteria for the coronavirus has undoubtedly been fed by similar sophisticated healthcare computer models, most notably the Imperial College’s doomsday predictions which indicated that as many as 500,000 deaths could occur in Britain and over 2 million in the U.S. After several public policy actions had already been put into place by governments — at least in part because of computer models such as the credible scientific U.K. forecast — the projections were abruptly revised down to show fewer than 20,000 U.K. deaths were likely to occur from the virus and 200,000 in the U.S.
Those of us who use computer modeling on a daily basis to assist in our analysis know how dynamic projections can be. These types of dramatic changes are routine when there are significant changes to the inputs that go into the models. In fact, that is the reason for modeling: determining the parameters of the inputs and how they affect the underlying outcomes.
Unfortunately, a superficial, agenda-driven press, which universally reports on outcomes from these imperfect tools as “settled science,” inevitably does a disservice to the casual reader in an effort to generate news and affect government policy.
It was refreshing to have a credible scientist like Dr. Deborah Birx on the White House Coronavirus Task Force gently scold those who are promoting catastrophic outcomes from computer models when the evidence no longer supports it....
Tradition is not the worship of ashes, but the preservation of fire. ― Gustav Mahler
Six Unknown Factors in Coronavirus Models
Since the global outbreak of COVID-19, researchers have scrambled to develop and share models which can predict how the virus will spread. This is inherently tricky, as we know so little about the disease, and a model is only ever as good as the information you put into it.
...Here are six factors that are still not entirely known, and how they could lead to different outcomes.
1. Asymptomatic spread...
2. Mode of contact...
3. Flouting the rules...
4. Hotspots...
5. The incubation period...
6. How the spread differs between countries...
...To take all this uncertainty into account, we have to run a massive amount of simulations.
Each time we add a factor, any of which could render the results useless if calculated badly, the amount of computer power needed to run the simulation grows.
If, for instance, we had a total of ten factors we needed to consider, and we wanted to try ten variations for each factor, we’d end up having to run 10 billion simulations – much more than the standard researcher’s laptop can handle.
We’ll be hearing a lot about models and simulations over the coming months as they begin to play an ever more influential role in our lives. But it’s important we never forget to ask – what information did they use to get their answer, and can they show their results still hold if the reality turns out a little different from what they assumed?
Tradition is not the worship of ashes, but the preservation of fire. ― Gustav Mahler
Models, as with climate models, politicals polls, social polls, and even scientific research are frequently biased to the point the outcome is guaranteed and they're useless.
Peter1469 (04-07-2020)