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.
News30-Oct 13: Downloadable scenarios
21-Oct 13: STEM 2.0 Release
20-Sep 13: STEM 2.0 M5 is up
22-Aug 13: New downloadable scenarios
07-Aug 13: Re: H7N9
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)Avian Influenza As an example of a stochastic model (new in STEM 2.0), this shows a hypothetical outbreak of an avian influenza (e.g. H7N9) in China spreading from birds to a few hundred people over a period of one year. It also demonstrates the effect of a mutated virus transmitting human-to-human. Requres a STEM build on or after 8/21/13.
(New)Model Generation Sample Scenario This is the bare bone demonstration scenario used in the model generation tutorial video (http://www.youtube.com/watch?v=MtQlS7g7Qnw). The scenario requires 2010 Earth Science data.
...more Downloadable Scenarios
Recent PublicationsAlexander Falenski, Matthias Filter, Christian Thöns, Armin A. Weiser, Jan-Frederik Wigger, Matthew Davis, Judith V. Douglas, Stefan Edlund, Kun Hu, James H. Kaufman, Bernd Appel, and Annemarie Käsbohrer. A Generic Open-Source Software Framework Supporting Scenario Simulations in Bioterrorist Crises.
Hu K, Thoens C, Bianco S, Edlund S, Davis M, Douglas J, and Kaufman JH., 21 Feb 2013 The effect of antibody-dependent enhancement, cross immunity, and vector population on the dynamics of dengue fever.
Edlund S, Davis M, Douglas JV, Kershenbaum A, Waraporn N, Lessler J, Kaufman JH. A global model of malaria climate sensitivity: comparing malaria response to historic climate data based on simulation and officially reported malaria incidence.
Upcoming (and recent) talksEdlund, 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
Edlund S., Davis, M., Kaufman, J. Extending Geospatial Data to Support Epidemiological Modeling
Kaufman JH (presenter), Davis M, Douglas JV, Edlund S, Hu K, Filter M, Wigger J-F, Thoens C, Weiser AA, Kaesbohrer A, Appel B. The SpatioTemporal Epidemiological Modeler: an open source framework for modeling food-borne disease.
Falenski A (presenter), Thoens C, Filter M, Kaesbohrer A, Appel B, Kaufman JH, Edlund S, Davis M, Douglas JV, Hu K. A community resource for spatial, temporal and food chain epidemiological modelling to assess risks in bio-terroristic or agro-terroristic crisis situations.