Glossary Type: Modelling

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).

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.

Involves at least one time-varying variable.

Unlike a Markov chain, a hidden Markov model contains unobservable states and describes observations and their likelihoods for each state in addition to providing states and transition rates between the states.

A Stochastic model where individuals are described as discrete entities with their own characteristics. This model may be used when heterogeneity should be considered in a population. Also known as agent-based model/micro-simulation model.

A model based on probability for distribution on permutation. The probability of a permutation can be estimated as a function of the Kendall distance from the centre permutation when the spread of the distribution is known.

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: