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.
News27-Nov 20: Re: Tutorial videos
18-Nov 20: Re: Tutorial videos
18-Nov 20: Re: Tutorial videos
14-Nov 20: Re: Estimating Epidemiological Parameters Using External Data
12-Nov 20: Re: Changing Variables in the Backend
Videos and presentationsSTEM V2.0 Model Generator (new!!)
STEM Tutorial (English)
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) Advanced SarsCoV2 model with asymptomatic transmission This archive shows how to fit a SarsCoV2 model to case report data (by region).
(new) Another Adv SarsCoV2 model with asymptomatic transmission This archive shows how to fit a second SarsCoV2 model to case report data (by region).
...more Downloadable Scenarios
Recent PublicationsJudith V Douglas et al. STEM: An Open Source Tool for Disease Modeling
Kezban Yagci Sokat et al. Comparing Direct and Indirect Transmission in a Simple Model of Veterinary Disease
Saskia v. Popescu, PhD, MPH, MA, CIC Why We Need More Open-Source Epidemiological Tools
Upcoming (and recent) talksJ. H.Kaufman et al. CDC Division of Healthcare Quality Promotion (DHQP) WIP seminar on STEM