Compartment Models11 May 2020 12:07
Yet Another Inadequate Placeholder
A "compartment model" is a model for the evolution of a population. At any give time, each individual in the population is of one of a finite number of types. That is, the population is divided up among "compartments", one compartment per type. Individuals can "transition" from one compartment to another, i.e., change types. (In some versions, individuals can also be born and die.) The rates at which they make these transitions are allowed to be functions of the current distribution of the population across compartments; sometimes we allow for the history of the distribution to matter as well. There are deterministic and stochastic versions; I am particularly interested in stochastic versions which approach deterministic limits as the population size grows.
These get used a lot as models of contagious disease (where the compartments are status like "infectious", "exposed but not yet infectious", "recovered", etc.), and in demography (where the compartments are things like "Male 18--22" or "Female 90--94", perhaps further sub-divided by marital status, caste, etc.) They are also formally identical to models in chemical kinetics and physical chemistry, which, as I understand it, was the inspiration for one of the pioneers here, A. J. Lotka.
- Recommended (very misc.):
- "Stephen P. Ellner and John Guckenheimer, Dynamic Models in Biology
- Thomas G. Kurtz, Approximation of Population Processes
- James H. Matis and Thomas R. Kiffe, Stochastic Population Models: A Compartmental Perspective
- To read:
- John A. Jacquez
- "The inverse problem for compartmental systems", Mathematics and Computers in Simulation 24 (1982): 452--459
- Compartment Analysis in Biology and Medicine
- John A. Jacquez and Carl P. Simon, "Qualitative Theory of Compartmental Systems", SIAM Review 35 (1993): 43--79 [JSTOR]
- A. J. Lotka, Elements of Physical Biology
- To write:
- "Large Deviations for Markovian Compartment Models, and Related Population Processes" (I know what the result is, and I have a non-rigorous proof, but I need to turn it into a rigorous result...)