Term	Definition
Agent-based models	Also referred to as individual-level models. See “Individual-level models” for definition.
Compartmental models	Compartmental models are cohort-based transmission dynamic models that involve interaction and usually treat time as continuous. The biological structure is applied to homogenous groups of individuals (compartments), which can be further stratified across other domains. Examples include SIR and SEIR models among the many.
	Also referred to as cohort-level models.
Decision-analytic models	Decision analytic models are used to analyze decisions under uncertainty. This group of models include decision trees, Markov cohort models, state-transition models, and discrete event simulation models.
	Also referred to as health economic models if used for economic evaluations.
Deterministic models	A model where random processes and uncertainty due to random chance of events are not captured. Each simulation will result in identical average results. Deterministic model results are often viewed as the average of many stochastic model simulations.
Discrete event simulation (DES) models	Discrete event simulation models are individual-level models that simulate events at a particular point in time. These models involve interaction, generally between individuals and their environment, and treat time as continuous. They require extensive individual-level time-to-event data.
Individual-level models	Individual-level models are dynamic models allowing for interactions between individuals to produce a complex network effect.
	In individual-level infectious disease models, the biologic structure, demographic characteristics, and risk factors are applied at the individual level, so that natural history, risk level, and contacts/interactions can vary between people.
	Also referred to as agent-based models.
	In individual-level decision-analytic models, the probability of transition, risk for events, or time-to-event apply to each individual. Each simulation represents an individual, often introducing heterogeneity and stochasticity.
	Sometimes referred to as microsimulations.
Phenomenological models	Phenomenological models are commonly used for estimating the time-variant Rt (effective reproductive number), and near-forecasting in real time. Such models do not simulate the causal pathway of transmission.
	These models have been used to estimate time-varying reproduction numbers during epidemics for measles (Germany, 1861), pandemic influenza (USA, 1918), smallpox (Kosovo, 1972), SARS (Hong Kong, 2003), and pandemic influenza (USA, 2009) [9].
	Examples of phenomenological models include regression analyses or statistical model, branching processes, and renewal equation models. Branching process is a mathematical process where nodes (which represent infected individuals) give rise to other nodes to show an infection tree. The probability of nodes proliferating or diminishing is described by statistics and mathematics. Renewal equation models use an equation that defines the relationship between the number of new infections as being proportional to the number of prevalent cases and their infectiousness.
SEIR	A type of infectious disease compartmental transmission model with the compartments: Susceptible, Exposed, Infectious, and Recovered.
SEIRS	A type of infectious disease compartmental transmission model with the compartments: Susceptible, Exposed, Infectious, Recovered, and Susceptible (again).
SIR	A type of infectious disease compartmental transmission model with the compartments: Susceptible, Infectious, and Recovered.
SIRS	A type of infectious disease compartmental transmission model with the compartments: Susceptible, Infectious, and Recovered, and Susceptible (again).
State-transition models	Simulations (or expected value calculations) conceptualized in terms of health states, transitions, and transition probabilities. The most common types are Markov cohort models and individual-level state-transition models.
Stochastic model	A model that captures uncertainty due to random chance. Each stochastic model simulations produce different results (realizations), but over a large number of simulations, they should converge to the average result generated from a deterministic model. Stochastic models are especially useful when events are rare or if there are smaller populations; when uncertainty due to chance can have a large effect.
Transmission dynamic models	Models for infectious disease transmission explicitly capture interactions and feedback loops as mechanisms of infectious diseases dynamics

All provided definitions and examples are adapted from Gambhir et al. [4], Mishra et al. [5], Cori et al. [9], Fraser et al. [10], Thompson et al. [11] Please refer to these papers for more details about infectious disease modeling.