Public Health Research - The Spatiotemporal Epidemiological Modeler (STEM) - overview
The Spatiotemporal Epidemiological Modeler (STEM)
The Spatiotemporal Epidemiological Modeler (STEM) tool is designed to help scientists and public health officials create and use spatial and temporal models of emerging infectious diseases. These models can aid in understanding and potentially preventing the spread of such diseases.
Policymakers responsible for strategies to contain disease and prevent epidemics need an accurate understanding of disease dynamics and the likely outcomes of preventive actions. In an increasingly connected world with extremely efficient global transportation links, the vectors of infection can be quite complex. STEM facilitates the development of advanced mathematical models, the creation of flexible models involving multiple populations (species) and interactions between diseases, and a better understanding of epidemiology.
An open source tool available through the Eclipse Foundation, the SpatioTemporal Epidemiological Modeler (STEM) allows users to create spatial and temporal models of emerging infectious diseases.
Designed to be extensible, flexible and re-usable, STEM provides a set of validation tools researchers and public health officials can use to understand the spread of disease in space and time and to assess the impact of preventive actions in an increasingly global world.
Platform independent, STEM is available in versions for Microsoft, Apple, and Linux operating systems. Users can access all its main components as separate plug-ins to build on existing models and create new ones.
Users can independently deploy the plug-ins - the core representational framework, graphical user interface, simulation engine, disease model computations, and various data sets - and use them with declarative software extension points to develop, run, and analyze sophisticated simulations.
STEM's data sets describe the geography, transportation systems (including airports and roads), and population for the world's 244 countries and dependent areas down to administrative level 2 for most countries (the county level in the United States).
Its disease model computations are based on compartment models that assume an individual is in a particular state, either susceptible (S), exposed (E), infectious (I), or recovered (R), in classic SI(S), SIR(S), or SEIR(S) disease models, pre-coded with deterministic and stochastic variations. Parameters within the models can be modified by the researcher who, for example, may wish to adjust the infectious period or the initial number of infectious individuals.
STEM simulates the models using ordinary differential equation solvers (two ODE solver options are currently available) and outputs the results for viewing in a number of formats. Users can view output using maps provided by STEM, linking to Google Earth(TM), or generating graphic displays that plot data in time or in relation to other data values.
Tools provided by STEM support researchers in a range of functions, as they perform analysis, fitting, and model comparisons across multiple simulations and data sets.
Using the Analysis Perspective, researchers can visualize the results of STEM scenarios from log files and compare two scenarios side-by-side across different dimensions. Utilities in this perspective can estimate disease parameters from imported time series data and integrate historic incidence data to arrive at counts for disease models over time.
Using the Designer Perspective, users can create custom experiments, which express public health policies as a collection of predicates, modifiers, and triggers. Researchers can run a collection of simulations, based on a single scenario, modifying each simulation slightly by varying one or more parameters, and examine how the model is affected.
With the components STEM provides, users can create their own model for a country, a region, or even the entire world. If there is a sub-model for the area under study, it can simply be plugged into simulations by referencing it. For example, a country model can contain a sub-model for its transportation infrastructure and that sub-model itself can contain sub-models for air, rail, and/or roads.
The ability of one STEM model to contain another allows researchers to plug detailed and highly complex subcomponents into a single encompassing model. Because the underlying components are the same, models can be easily shared and their components validated. One researcher can import another researcher's specialized disease model, combine it with an existing country model that includes population demographics, and re-export the new combination for others to use.
By making data (with descriptive metadata) available as plug-ins, STEM makes new avenues of collaboration possible. For example, biologists studying bird migrations can contribute data of use to epidemiologists studying avian influenza. Economists studying workforce productivity contribute data of use to public health officials studying the economic impact of pandemic influenza.
By providing a common collaborative platform and components that are extensible, flexible and re-usable, STEM makes possible greater understanding of the phenomena that affect public health and potentially have social, economic, and environmental impacts as well.