Comment on “Simple Models of Influenza Progression within a Heterogeneous Population” by

“Simple Models of Influenza Progression within a Heterogeneous Population” targets the important goal of engaging the operations research (OR) community to work on challenging and important problems in infectious disease management and control. Valuable points Larson makes include (1) the importance of developing models with a decision-context in mind, (2) the examination of system-level control alternatives such as public health interventions as well as medical interventions, including state-dependent control decisions, (3) a discussion of behavioral issues and disease control that may depend upon the progression of an outbreak. That said, I would like to challenge some statements in the paper with respect to the modeling of heterogeneity in populations, and with respect to the so-called “basic reproductive ratio” R0, a concept that is fundamental to epidemiology and to the control of infectious diseases. This commentary first reviews some existing literature on heterogeneous population models and R0 that supplements the discussion in Larson’s paper. With this supplemented view of epidemic models, some different high-level perspectives emerge about ways that OR modelers can contribute to the field of infectious disease control. There are a number of exciting opportunities. Heterogeneous populations. This section supplements Larson’s discussion of heterogeneous population models, and shows that the mixing models of Section 5 are a special case of some well-studied models. To fix ideas, we first introduce homogeneous population models that serve as a basis of discussion. Two textbook models of infectious disease transmission are the homogeneous population susceptible-infectious-susceptible (SIS) model and the susceptible-infectious-recovered (SIR) model (Jacquez 1996, Halloran 1998, Sterman 2000). The SIS model allows a given individual to become infected and recover multiple times. When expressed as a differential equation, epidemiologists tend to parameterize the SIS model by: