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 spatiotemporal Susceptible/Infectious/Recovered (SIR) and Susceptible/Exposed/Infectious/Recovered (SEIR) models pre-coded with both deterministic and stochastic variations. STEM simulates the models using numerical ordinary differential equation solvers (two solver options are currently available) and outputs the results to a range of sources, for instance a map view or the file system.
News09-May 13: BfR in German National TV
03-May 13: H7N9
30-Apr 13: Re: STEM Checkout from GIT is ready
30-Apr 13: STEM Checkout from GIT is ready
29-Apr 13: Re: STEM Project Migration to Git
Videos and presentationsSTEM 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)Salmonella In this example the spread of Salmonella from farm to fork is demonstrated for the production and consumption of pork in Germany based on findings from the scientific literature.
(New)Population data replay demo In this demo, we read mosquito population data from the external file system and use it to drive a model of Malaria in Thailand
...more Downloadable Scenarios
Recent PublicationsHu 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) talksHu, K., et al.Modeling the Dynamics of Dengue Fever
Edlund, S.Tooling in support of a collaborative platform for developing and sharing epidemic models and data
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.