| compartmental model | <epidemiology> A mathematical model which divides hosts into different compartments according to their infectious state. A typical model for microparasites might be an SEIR model. Sometimes referred to as a prevalence model. (05 Dec 1998) |
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| computer model | A mathematical representation of the functioning of a system, presented in the form of a computer program. Synonym: computer simulation. (05 Mar 2000) |
| concerted model | A model used to explain the allosteric form of cooperativity; in this model, an oligomeric protein can exist in two conformational states in the absence of the ligand; these states are in equilibrium and the one that is predominant has a lower affinity for the ligand (which binds to the protein in a rapid equilibrium fashion). Synonym: concerted model. (05 Mar 2000) |
| multiplicative model | A model in which the joint effect of two or more causes is the product of their effects if they were acting alone. (05 Mar 2000) |
| multistage model | A mathematical model, mainly for carcinogenesis, based on the theory that a specific carcinogen may affect one among a number of stages in the development of cancer. (05 Mar 2000) |
| continuous time model | <epidemiology> A model in which the system changes continuously over time. Derivatives (e.g. DY/dt ) are the mathematical formalism for describing such continuous change. The differential equation which embodies a model provides the values of these derivatives at any particular time point; calculus or a computer can then be used to move the state of the model forwards in time. Continuous models have the advantage over discrete time models in that they are more amenable to algebraic manipulation, although they are slightly harder to implement on a computer. The same as a differential equation model. (05 Dec 1998) |
| cooperativity model | A model used to explain the property of cooperativity observed in certain enzymes; e.g., allosterism or hysteresis. (05 Mar 2000) |
| seir model | <epidemiology> A class of compartmental prevalence models, with compartments Susceptible, Latent (Exposed), Infectious and Recovered. Takes its name from a common notation. In the notation used in the course, this would be an XHYZ model. Many permutations possible. (05 Dec 1998) |
| sliding filament model | <cell biology> Generally accepted model for the way in which contraction occurs in the sarcomere of striated muscle, by the sliding of the thick filaments relative to the thin filaments. (18 Nov 1997) |
| spawner-recruit model | Biological model that relates the number of recruits or mature spawners in one generation to the number of spawners in the previous generation. (09 Oct 1997) |
| statistical model | A formal representation for a class of processes that allows a means of analyzing results from experimental studies, such as the Poisson model or the general linear model; it need not propose a process literally interpretable in the context of the individual case. (05 Mar 2000) |
| stochastic model | <epidemiology> A mathematical model which takes into consideration the presence of some randomness in one or more of its parameters or variables. The predictions of the model therefore do not give a single point estimate but a probability distribution of possible estimates. Contrast with deterministic. We might distinguish demographic stochasticity which arises from the discreteness of individuals and individual events such as birth, and environmental stochasticity arising from more-or-less unpredictable interactions with the outside world. (05 Dec 1998) |
| nursing model | A set of abstract and general statements about the concepts that serve to provide a framework for organizing ideas about clients, their environment, health and nursing. (05 Mar 2000) |
| surface envelope model | <biology> A way of treating the hydrodynamics of a ciliary field by considering the whole surface of the ciliate to have an undulating surface. The undulations arise because of metachronism. (18 Nov 1997) |
| deterministic model | <epidemiology> A mathematical model in which the parameters and variables are not subject to random fluctuations, so that the system is at any time entirely defined by the initial conditions chosen. Contrast with a stochastic model. (05 Dec 1998) |