> SIR is essentially what I'd get if I ignored the details of heat convection, conduction and radiation - which at the scale of forest fire is something you can do
How do you know? To validate this sort of model surely you need to know an awful lot about statistical modelling and about forest fires.
The main sort of insight this model would give you is that setting up clear spaces like roads through a forest would hinder the spread of fire. If you took the model literally, you might end up ignoring the fact that sparks can be carried by the wind to areas that are far away from the trees that are currently burning.
> it does seem that SIR model with modifications is widely used to study disease spread
Fair - there's a whole wikipedia page [0] about this sort of model. But like I'm saying, that page is big on theory and light on evidence. Those models are full of parameters like transmission rate that are not typically known until after the fact.
The world is full of theorists writing down academic models that are, frankly, a little useless. What I would find more convincing is a writeup of how a big practitioner health organisation like the CDC or the WHO used one of these models to gain a new insight that they couldn't have found any other way.
> How do you know? To validate this sort of model surely you need to know an awful lot about statistical modelling and about forest fires.
First-order approximation and all. You validate according to your needs, but yes, you'd also need to know a lot about statistical modelling and the domain. I'm guessing the author just read that SIR can be used for some diseases and forest fires, and that what they read was written by people who do know that. FWIW, our CA models were validated by our supervisor (who had access to firefighters) to roughly the level of "reproduces what happens on recordings of real fire" - which was good enough to show that the model has a potential to give real-time insights, but not something I'd like an actual firefighter to use on the scene.
> If you took the model literally, you might end up ignoring the fact that sparks can be carried by the wind to areas that are far away from the trees that are currently burning.
That's a very good point. However, like with all models, you need to be aware of the limitations. Maybe the author should have guarded the text for this, but I doubt the government epidemiologists and firefighters were the target audience here. I expect these professionals to understand the models in greater depth before using them (though maybe I'm hoping for too much - given how our industry is full of "professionals" mindlessly copy-pasting code from SO).
> that page is big on theory and light on evidence
Fair. I was actually going to link to a Wikipedia page in my previous comment, but I noticed a surprising lack of citations for the claims made. So instead I went and looked around Google Scholar before saying that "it does seem that SIR model with modifications is widely used to study disease spread".
> Those models are full of parameters like transmission rate that are not typically known until after the fact.
Sure. Again, I hope that no epidemiologist uses the article to develop disease spread models. But it works as giving general overview and intuition about network models, with focus on the phenomenon of criticality.
> What I would find more convincing is a writeup of how a big practitioner health organisation like the CDC or the WHO used one of these models to gain a new insight that they couldn't have found any other way.
I would absolutely love to see that.
BTW. I hope we're not just arguing about whether the word "perfect" was used in the article correctly.
> BTW. I hope we're not just arguing about whether the word "perfect" was used in the article correctly.
I guess I'm trying to make a point about theory vs. practice. I think theorists tend to be pretty cavalier about writing down models and waving away the fine details, whereas practitioners tend to appreciate how hard it is to get the details right.
As an example, the literature on finance is full of this stuff. There's a whole body of literature on how to create an optimal stock portfolio under various constraints assuming you know the joint distribution of individual stock returns. It turns out that fitting that distribution in a sensible way is extremely difficult to do. The theorists came up with a `clever' model that's mostly useless in practice, but everyone still insists that it has `applications' in finance.
Personally I find theory really interesting, and beautiful in its own right. It just annoys me when the usefulness of that theory gets overstated.
How do you know? To validate this sort of model surely you need to know an awful lot about statistical modelling and about forest fires.
The main sort of insight this model would give you is that setting up clear spaces like roads through a forest would hinder the spread of fire. If you took the model literally, you might end up ignoring the fact that sparks can be carried by the wind to areas that are far away from the trees that are currently burning.
> it does seem that SIR model with modifications is widely used to study disease spread
Fair - there's a whole wikipedia page [0] about this sort of model. But like I'm saying, that page is big on theory and light on evidence. Those models are full of parameters like transmission rate that are not typically known until after the fact.
The world is full of theorists writing down academic models that are, frankly, a little useless. What I would find more convincing is a writeup of how a big practitioner health organisation like the CDC or the WHO used one of these models to gain a new insight that they couldn't have found any other way.
[0] https://en.wikipedia.org/wiki/Compartmental_models_in_epidem...