The Spatiotemporal Epidemiological Modeler (STEM) Project
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.
STEM is designed to make it easy for developers and researchers to plug in their choice of models. It comes with a large number of existing compartment models and a new model building framework that allows users to rapidly extend existing models or to create entirely new models. The model building framework provides a simple graphical users interface and automatically generates all of the model code and hot injects the code into STEM at runtime. In many cases, no knowledge of Eclipse or Java is required. The STEM code generator even allows users to build models affected by changes in climate data.
Any STEM model can be run either stochastically or deterministically - simply by switching between solver plugins. Users can choose between many different numerical solvers of ordinary differential equations (including finite difference, Runge-Kutta, 4 solvers from The Apache Commons Mathematics Library, and Stochastic). The stochastic solver computes integer (individual) based transitions picking randomly from a binomial distribution (also from Apach Math). Simulation results can be output with a choice of pluggable loggers, including delimiting files, video loggers, and map loggers. STEM can be used to study quite complex models (for example a model of Dengue Fever with 51 differential equations) and can run global scale simulations. Click here for the complete STEM documentation.
News15-Jul 16: Re: Milestone Build 3.0.3
26-May 16: Milestone Build 3.0.3
30-Apr 16: STEM wiki updates
11-Apr 16: Re: How to use built in mosquito data
10-Apr 16: How to use built in mosquito data
Videos and presentationsSTEM V2.0 Model Generator (new!!)
STEM Tutorial (English)
STEM Tutorial (Spanish)
STEM Tutorial (Hebrew)
STEM Tutorial (Japanese)
5 min. STEM Video (English)
Downloadable ScenariosPlease Read-me first Installation Instructions
Documentation To Learn more about the downloadable scenarios please see the tutorials on the STEM wiki
(new) Importing Shape Files This single archive contains an example of how to import and create a STEM graph from a shape file (.shp) in your workspace. If uses STEM's pajek graph generator.
Ebola Models This single archive contains three different projects with several Ebola scenarios. Requires the latest STEM Integration build on or after Sept 26, 2014.
...more Downloadable Scenarios
Recent PublicationsHu K, Bianco S, Edlund S, Kaufman J. The impact of human behavioral changes in 2014 west Africa Ebola outbreak.
Kaufman J, Lessler J, Harry A, Edlund S, Hu K, Douglas J, Thoens C, Appel B, Kasbohrer A, Filter M. A likelihood-based approach to identifying contaminated food products using sales data: performance and challenges.
Hu K, et al., 21 Feb 2013 The effect of antibody-dependent enhancement, cross immunity, and vector population on the dynamics of dengue fever.
Upcoming (and recent) talksHu, K., et al. Modeling the Dynamics of Dengue Fever
Edlund, S., Hu, K., Kaufman, J.H., Lovett, D., Van Wijgerden, J., Yagci Sokat, K., Poots, A.J Estimating the impact of measles immunization uptake in GP clinics in a North West London Borough
Davis, M., Edlund, S., Kaufman, J.H. Extending a Spatiotemporal Epidemiological Modeling Tool for Subject Matter Experts
S. Renly The SpatioTemporal Epidemiological Modeler
J.H. Kaufman and S. Edlund The SpatioTemporal Epidemiological Modeler
Hu, K., et al. Modeling the Dynamics of Dengue Fever