Fighting Diseases with Eclipse STEM

Session Type: 
Standard [25 minutes]

The Spatio-Temporal Epidemiological Modeler (STEM) is an Eclipse based RCP for modeling the global spread of infectious disease. STEM started in the Eclipse Open Healthcare Framework and was recently promoted as a top level Eclipse Technology project ( designed to allow global collaboration on the development of infectious disease models.

It embodies a flexible "graph based" representational framework that allows models to be composed from different components provided by different researchers. It also includes extensive data sets that represent the entire political geography of the planet (244 countries) as well as sophisticated mathematical models for predicting disease propagation. STEM also integrates internal views for geographic visualization and as well as offering interfaces to Google Earth. Scenarios can be created using drag-and-drop editors and can include Models that incorporate different layers of abstraction, for instance it can layer economic models over disease models.

Public policy interventions can be modeled in STEM through the use of a conditional "trigger" mechanism that tests for conditions in a simulation and then changes some aspect of the Simulation\'s state as a result (e.g., significant reduction of all traffic to a country). STEM is a very general system and is designed and implemented to allow for other types of simulation scenarios including disaster planning and recovery, military planing and infrastructure deployment.
The Introduction to Compartment Models describes how an infectious disease evolves in time. STEM also allows users to represent the geographic distribution of people (or other species) and how they move about in space. A compartment model that deals only with the trajectory of a disease in time implicitly assumes that the population (or populations) in question is so well mixed that there is no need to model the spatial distribution of people. However, for very large scale simulations, the details of population distribution, transportation, trade, even wild bird migration can all be important factors in understanding the evolution of an infectious disease in space and time. presentation describes how Transportation Models are implemented in STEM.