Custom Model With Diverging Paths

[gaianoseworthy]

I am back with another question! Let's say we wanted to create a custom model by importing the DiffusionModel partial class, like you mentioned in #171 and wanted to make it such that the model is SE(IA)R, such that E -> I with rate = alpha and E -> A with rate = phi, what would be the best way to do this? My current code blocks for transitions are borrowed from the SEIR class, and are pasted below:

if u_status == 2:
                if self.progress[u] < 1:
                    self.progress[u] += self.params['model']['alpha']
                else:
                    actual_status[u] = 3  # I
                    del self.progress[u]

if u_status == 2:
                if self.progress[u] < 1:
                    self.progress[u] += self.params['model']['phi']
                else:
                    actual_status[u] = 4  # A
                    del self.progress[u]

[GiulioRossetti]

Yes, the idea is that within each iteration you cycle over the node set and, depending on the current node status, apply the transition rule you designed.

I haven't checked the code (I'm on the smartphone right now) but the scheme seems the standard one.

[gaianoseworthy]

My only concern is that the rate is progressed through a simple '+=' counter in this example (taken from the SEIR model) and has no random elements, making it effectively a counter. I may just do a quick random chance instead, which matches more of what I would expect from the model, and is closer to the custom model with probabilities, where I check "if random() < (alpha): self.progress[u] = 1". This should solve the issue of the disease always doing E -> I and never E -> A.

[GiulioRossetti]

Yes, It Is something easy to achieve.