Sure, but the error is huge. You will get similar results running an agent based sim 100k times and taking the average, upper and lower bounds as taking the results of an ODE with appropriate margins of error.

Then it is more similar to the real phenomenon than fitting lines to past data. My point was that models based on ODEs, that are remotely realistic, are also extremely sensitive to slight variations in the starting parameters. The worst and best case figures are bounded by the total population size and whether the virus is airborn (for example), but you have huge variance within that because of the error in the initial parameters.

This doesn’t mean they are not useful, but they are also ‘chaotic’ because the underlying phenomenon is fundamentally chaotic, non-linear, multiplicative, complex, etc.

Discrete versions? I don’t know why people would restrict themselves to ODEs. Especially when the underlying phenomena lends itself to graph theoretic interpretations. Contagion is fundamentally discontinuous and spreads from one discrete body to the next via a distinct intermediary.

Stochastic models are also useful if you are interested in identifying emergent behaviors, the same patterns form across multiple systems and can be isolated and abstracted. This is my point about disconnecting vertices, you don’t see that in a pure ODE, but in cellular automata and agent based sims you can experiment with it and then mathematically formalise it. Create policy based on it (i.e. automatically restrict travel between ‘vertices’, like airports, based on some rules) because you *logically* know it will be effective within reasonable bounds.

Depends on the error, you can’t account for unknown unknowns other than to set large error bounds based on limiting factors (like population size). There is also the risk that people massage the data until it shows what they want., although don’t take this to mean that I’m saying you shouldn’t try to work with the data. I’m just saying the error is much larger than people think.

This is something else you can experiment with using agent based models by sampling the model and comparing it with the underlying results. Because in a simulation, you have perfect information, unlike real life where you are looking through a tiny pin prick at reality.

To be clear, I only created this to explore emergent behaviors common to ‘complex systems’ and see whether I could implement an agent based model with Actors. In the second line of the notebook I have written “Of course this is not a remotely realistic model, it is just supposed to show some general effects observable in any complex system which resembles viral spread.”

It so happens that I don’t think any of the existing models floating around are that realistic (outside of a lab) either, but that is getting off-topic.