Glossary Section: Types of mathematical models

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.

All variables do not vary across time; variables are constant.

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).

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

Used to describe cell-pathogen interactions (compare Between-host model).

Used to represent objects (e.g., individuals, institutions) and their relationships with one another (unipartite) or another type of object (bipartite).

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.

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.

Describes relationships between risks and outcomes with no underlying assumptions (compare Mechanistic 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.