Typically models have parameters. Then the discussion is how to learn the parameters from the data.
In general there seems to be a scale of models, from those constructed solely on a priori knowledge (expert systems) to those constructed solely on data.
For example, a model of an epidemic may have parameters like, RO, .... Then given real data, the parameters are estimated and the models is tested.
Alternatively, we can start with data, and try and create models that represent that data. However, keep in mind that the choice of data in itself is a bias introduced by a priori knowledge.
So in truth there is no difference between these two approaches, just a matter of degree.
Given lots of data there is a tendency towards the later side of the scale, given little data the shift is to the former side of the scale.
The curse of dimensionality is a good metric to help decide which side of the scale is reasonable, to define 'lots' or 'little' amounts of data.
However, often overlooked in the nature of the curse of dimensionality is scale dependence. See my blog post on the topic.
So what are we measuring and what are we predicting. Are they on the same scale?
So there are two types of problematic statements. Statements that are invalid: a triangle with four sides or a wheel of a car that has a destination (cars have destinations, wheels have rolling characteristics) Statements that are not supported, by mixing levels they hide the curse of dimensionality and hence the number of observations needed to validate the statement.
mixed scales:
A pandemic model that has the virus 'intent', a virus utilizes airplanes to help itself reproduce.
Mortality is a function of hospital beds (higher level scale) and viral load (lower level scale).
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from : https://medium.com/amnon-shashua/can-we-contain-covid-19-without-locking-down-the-economy-2a134a71873f
Let m be the size of the low-risk group and let ν be the probability that a person that comes from the low-risk group will develop severe symptoms, assuming the person is currently sick
...
p* be the current, unknown, percentage of positive cases among the low-risk population and let k be the number of severe cases among the low-risk population from today until one week from now.
...
To do this, we will sample n persons, uniformly at random from the low-risk population, and will derive a lower bound on p* based on the number of people that came out positive. "
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Seems to me all the variables and analysis are at one level - that of an individual patient, either a person is in a low-risk or high-risk group based on age. A person tested positive or not. A person is severely ill or not. The observations are of individuals. Thus the entire statements computes.
compare to:
https://www.statnews.com/2020/03/16/coronavirus-model-shows-hospitals-what-to-expect/
CHIME (“Covid-19 Hospital Impact Model for Epidemics”), built by Penn’s Chivers and others in “predictive healthcare,” is a basic epidemiological tool of infectious disease spread called a SIR model. It takes what’s known about the number of susceptible (S) people in an area (which for Covid-19 is everyone, since no one has immunity to the new coronavirus that causes it), the number of infected (I) people, and the number of recovered (R) people (who are presumed to be immune from subsequent infection). Because of the disastrous rollout of Covid-19 testing in the U.S., the researchers assume that only 15% of cases have been detected (but say it could be even lower).
The model then uses the best current estimates of how long someone is infectious (14 days); how many new cases each infected person causes (called the effective reproduction number, it’s about 2.5); the percentage of Covid-19 patients who need to be hospitalized (5%, reflecting the fact that most people have only mild or moderate illness); the percentage who need to be in an ICU (2%) or on a ventilator (1%); and the length of stay for each of these three.
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and to:https://www.statnews.com/2020/02/14/disease-modelers-see-future-of-covid-19/
The computers that run disease models grind through calculations that reflect researchers’ best estimates of factors that two Scottish researchers identified a century ago as shaping the course of an outbreak: how many people are susceptible, how many are infectious, and how many are recovered (or dead) and presumably immune.=-=-
the Scottish researchers limited to three variables all about individuals virus reaction.
However, note how the CHIME model includes the 'reproduction number', which is at a different scale, that of community relationships. And it also includes ICU usage of a patient which is a function of viral load, how much virus was communicated.
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Now it is very possible that there are other factors to the Hospital Load that are not taken into account by the model and its parameters:
https://www.straightdope.com/columns/read/1734/is-there-an-anti-placebo-effect/
While Krenztman uses the term “placebo” for both positive and negative effects, “nocebo” is finding more use these days. As you might have guessed, the nocebo effect is the opposite of the placebo effect. In Latin, nocebo, which only showed up in English usage in the last decade (and, in fact, is not even recognized as a real word by my word processor’s dictionary), means “I shall cause harm or be harmful.” While the medical profession recognized a while ago that they needed to take into account the placebo effect, they have only recently recognized that they need to also take into account the nocebo effect.
Like the placebo effect, the nocebo effect is usually generated by “beliefs, attitudes and cultural factors” (http://quinion.com/words/turnsofphr ase/tp-noc1.htm). This occurs when the expectation of deterioration is created. For an extreme example, the July 1997 Harvard Mental Health Letter notes that the nocebo effect has been credited with causing “so-called voodoo deaths.” In other words, people who truly believe in voodoo and believe they have been cursed by a voodoo practitioner may be so affected by the nocebo effect that they actually get sick and die. The article further notes: “For surgical patients, the expectation of death on the operating table can be fatal. In one study of people with asthma, deliberate misinformation about the effects of medication reduced its effectiveness by nearly 50%. Also, allergic reactions can be induced merely by telling the patient that they are receiving a substance to which they are allergic, when in fact they are receiving salt water.”
