State‐transition model

Involves various health states and assumes that individuals are able to transition between the health states. These models are often built upon Markov or agent-based models.

Stochastic model

In contrast to deterministic models, stochastic models have random components; parameters or variables could be random (i.e., can be represented with a probability distribution).

Theory‐driven model

Builds upon assumptions or existing knowledge about relationships (e.g., effectiveness of an intervention) (compare to data-driven model).

Transmission dynamics model

Primarily describes the dynamics of infectious diseases between infected and susceptible individuals. The model accounts for the risk of infection in the susceptible individuals by considering the prevalence of infected individuals and may consider other environmental factors (e.g., reservoir, vectors).

Pair model

A type of compartmental models describing formation and dissolution of pairs (i.e., couples) in a population. Usually used to model sexually transmitted infections, pair models can account for time spent within and between partnerships.

Parsimonious model

The number of assumptions or predictors needed to formulate the model are minimized. Can be predictive or descriptive.


In classical metapopulation models, patches describe habitable areas in a landscape that are either occupied or vacant. A patch is made up of individuals and patches make up a metapopulation.

Discrete‐time model

A type of Dynamic model which treats time as a discrete variable (i.e., a variable with finite number of values) in contrast to the continuous-time model; in a discrete-time model, other variables or outcomes can only change at specific time points.

Population‐based model

Can be deterministic or stochastic. To account for potential heterogeneity between the individuals, those who share the same or similar characteristics are put into the same group or (sub)population.