Revision as of 14:39, 24 June 2010

New Models are continually being added to STEM. Today STEM comes with a variety of basic models which implement textbook algorithms such as those found in textbooks like Anderson, R. M.; May, R. M. (1991). Infectious diseases of humans: dynamics and control. Oxford and New York: Oxford University Press. ISBN 0198545991. STEM also includes more advanced models create by groups investigating current problems in epidemiology. As a framework users are also encouraged to create their own models and to even contribute them back to STEM.

Basic Models

STEM comes with implementations of many standard epidemiological compartment models. These include both Stochastic and Deterministic SI, SIR, and SEIR models as well as models with seasonal forcing and nonlinear interaction terms.

SI and SIS models

The simple two-state SI or SI(S) model is useful in describing some classes of microparasitic infections to which individuals never acquire a long lasting immunity. Certain RNA viruses such as rhinoviruses and coronaviruses (the common cold) mutate so rapidly that individuals recently recovered from a cold will still be susceptible to other strains of the same virus circulating in a population. In a simple model for this process, individuals never enter a recovered state, but rather alternate between being susceptible and being infectious.

SIR and SIRS models

More generally, after exposure to microparasitic infection, individuals who recover from a disease will enter a third state where they are immune to subsequent infection. This Recovered State, R, appears in the SIR(S) compartment models. For infections that confer lifelong immunity in the recovered state, an SIR model is appropriate. Typical examples for which an SIR model is used include Paramyxovirus (measles) and Viral Parotitis (mumps). In cases involving the Orthomyxoviridae viruses (which cause seasonal flu), immunity is not lifelong and may decrease over time. Immunity loss can reflect a decrease in an individual’s immune response, or a genetic drift in the circulating strain of virus that diminishes the effectiveness of the acquired immunity. In either case, an SIRS model represents the rate at which people in a Recovered state return to a susceptible state.

SEIR and SEIRS models

Some infectious diseases are also characterized by an incubation period between exposure to the pathogen and the development of clinical symptoms. If the exposed individual is not infectious during this incubation period (e.g., not shedding virus), it is important to model the incubation time explicitly. Note that there is a difference between an incubation time and a period of latency. A virus may or may not be dormant when an individual is in an exposed state. It is important to model the exposed (E) state explicitly when there is a delay between the time at which an individual is infected and the time at which that individual becomes infectious. In this case an SEIR(S) model is appropriate. Smallpox, for example, has an incubation period of 7-14 days

The disease models in STEM are implementations of these compartment models expressed as differential equations. These differential equations have a variety of parameters that are similar to the constants in a chemical rate equation. Users can easily change the basic parameters of any model in STEM with a text editor (called the property editor). This tunes the model based on the particular disease of interest. New models with even more advanced mathematics can easily be added to STEM - but that does take some knowledge of the Java programming language and of Eclipse. Please see the Tutorials and User Guides for detailed information on how to do this.

Once you have put together a model with a geographic region and other data (like transportation, time, and human population) you can easily export your work and share it with other users of STEM. In this way STEM is intended to promote scientific collaboration allowing researchers to build on each others work. See the section on Importing and Exporting Projects for instructions on how to do this and for information on some example scenarios.

Advanced Models

Nonlinear Models

STEM also implements nonlinear versions of the basic compartment models where the bilinear transmission kinetics term (bSI) is replaced with a modle of nonlinear transmission (e.g., bSI p, p > 1 (see Liu et al., 1987).

Models with periodic seasonal transmission rates

Modeling the interaction graphs within a population

in progess