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).

The simplest form of Markov model. A series of connected observable states (i.e., a chain) with specific probabilities of transition between the states. Example of Markov chain:

A Stochastic model assuming that the future state depends only on the present state of variables, not any of the previous states of variables.

Any model represented by equations (mathematical framework) to describe observed phenomena (e.g., data) or predict an outcome.

Describes real-world interactions pertaining to infection transmission, pathogenesis, and measures of control such as vaccination (compare Phenomenological model).

Accounts for subpopulations (patches) and their within and between dynamics. This model can be employed to investigate the movements and contact structures of host subpopulations.

Individual-based model/agent-based model.