Table 1 Definitions of modelling terms

Deterministic model

A model in which there is no role of chance in the evolution of the states of the system, i.e. the model is ‘predetermined’ by the parameters and initial conditions [61].

Stochastic model

A model in which random (stochastic) processes can affect whether certain events or processes occur (e.g. the rate at which individuals are infected can vary by chance) [61].

Compartmental model

A model in which the population is divided into subgroups (i.e. compartments), which represent the average values of individuals in a particular state (e.g. susceptible, infectious or recovered). Within each compartment, all individuals are homogenous [9].

Individual-based model

A model in which single individuals are tracked rather than subgroups. Hence, each individual can be assigned different characteristics such as the probability of acquiring infection or causing transmission [9].

Model fitting/ model calibration

The inference of unknown parameters by choosing their values in order to approximate a set of data as well as possible. Examples of model fitting methods are least squares approximation maximum likelihood estimation and Markov Chain Monte Carlo Methods [62].

Model validation

Comparison of model predictions to external data, that is a model should be validated against observations from alternative data to the data used for model fitting [62].

Univariate sensitivity analysis

Investigation of uncertainty in model parameters and its impact on model predictions by means of altering one parameter at a time whilst holding others at their base-case value.

Bi/ multivariate sensitivity analysis

Investigation of uncertainty in model parameters by means of alteration of two (or more) parameters at a time whilst holding others at their base-case value.

Probabilistic sensitivity analysis

A type of multivariate sensitivity analysis where multiple runs of the model are performed with random selection of input parameters.

Dynamic transmission model

A model which tracks the number of individuals (or proportion of a population) carrying or infected with a pathogen over time, where the risk of transmission to susceptible at a given point in time is dependent on the number of infected (or colonised) individuals in the community [9].

Static model

A model where the transmission risk is treated as a parameter exogenous to the model, i.e. it does not change with the number of infectious individuals in the population [9].

Force of infection

The rate at which infected individuals become infected per unit time [61]