specifically, age and geography; also Erlang-y stuff
general concerns
adding stuff will slow the model down a lot. Maybe not a concern for forecasting with fixed params, but matters for estimation and parameter ensemble forecasting
want to add structure in such a way that everything is still as transparent as possible, and that doesn't impede porting to a faster platform if necessary (see previous point)
allowing for parameters to vary appropriately across classes; make parameters into a list that can include vectors? (use unlist/relist)
keep capability to aggregate the big state space appropriately after running the sim (the current method can probably be extended, with some care - use structured names for subcompartments, e.g. Ia_a1_s2 for asymptomatic I in age class 1, spatial patch 2?)
appropriate use of Kronecker products to duplicate/structure the transition matrix (other possible stacking/unstacking tools? Probably not Khatri-Rao, but vec-perm a la Caswell??)
age
age-structured data shouldn't be too hard
WAIFW matrices from Prem et al 2013 if we want them
allows possibility of comparing control measures (school closure/work-from-home etc.), as in recent MRC paper
age-specific mortality etc. from Riou?
space
need to implement spatial contact matrix (traffic flow?)
age X space WAIFW can probably also work by Kronecker product (using "other" WAIFW matrix)
county-level data also possible (county-by-age harder?)
Erlang-y
figure out convolutions?
figure out how to auto-Erlangize a transition matrix (and then aggregate states); could allow for generalized Erlang/linear chain (Dushoff, Hurtado?)