Research
Model Transition Sensitivity Analysis (MTSA)
Geoffrey Jacquez, BioMedware, Inc., PI
This work was supported by 2 grants from the National Institute of Allergy and Infectious Disease: phase I in 2001 (R43-AI049007) and phase II 2003-5 (R43-AI049007).
Research Abstract
MTSA:
assessing the sensitivity
of simulation results to
changes
in model type, model complexity,
and
parameter values.
This project is developing software to assess the impacts of model assumptions on decisions regarding the analysis, surveillance, and control of infectious diseases. Most analyses consider sensitivity only to changes in a model's parameter values, and ignore how assumptions of model form (e.g. deterministic vs. stochastic, ODE vs. discrete individual) impact results and concomitant decisions.
This research will:
- Conduct a requirements analysis to specify the simulation engines and functionality to incorporate in the software.
- Develop and test a software prototype to evaluate feasibility of the proposed approaches.
- Build, test and implement a complete software package based on results of the prototype.
- Apply the software and methods to demonstrate the approach and its unique benefits for disease surveillance and control.
Feasibility of this project was demonstrated in the phase I research that accomplished the first two Aims. This phase II SBIR project is focused on Aims three and four. The technologic and scientific innovations from this project will revolutionize our ability to formulate sound decisions regarding the analysis, control and surveillance of infectious diseases. In addition to public health, MTSA may be applied to complex problems in environmental science, natural resource management, technology management, health care economics, and many other fields.
Results from a sensitivity analysis of
parameter values and model type
on infectious disease transmission.
Colors show populations with
different contact rates.
Smooth lines show populations of
infected
individuals from the
deterministic model and
stepped lines show results for
stochastic
simulations.
Software
BioMedware created prototype software, user manual, and online help that was submitted along with the Phase II application. The final software product and documents are under development. This product will be available from our commercialization partner, TerraSeer, once completed.
Conferences
BioMedware held two conferences for planning and review of the MTSA project: July 2003 and April 2001.
Publications
Koopman, J.S., S.E. Chick, C.S. Riolo, C.P. Simon, and G.M. Jacquez. 2002. Stochastic effects on endemic infection levels of disseminating versus local contacts. Mathematical Biosciences 180: 49-71.
Koopman, J.S., G. Jacquez, and S.E. Chick. 2001. New data and tools for integrating discrete and continuous modeling strategies. In Population Health and Aging: Strengthening the Dialogue between Epidemiology and Demography. M. Weinstein, A.I. Hermalin, M.A. Soto, eds. Annals of the New York Academy of Sciences 954: 268-94.
Koopman J, Chick S, Jacquez G. 2000. Analyzing sensitivity to model form assumptions of infection transmission system models. Ann Epidemiol. 2000 Oct 1;10(7):472.
Related Publications
Not your standard risk assessment, posted with permission from INSEAD
Jacquez, J.A. 1999. Modeling with compartments
Jacquez, J.A. 1996. Compartmental analysis in biology and medicine, 3rd Edition
Presentations
Jacquez, G.M. 2004. Complex systems analysis using space-time information systems and model transition sensitivity analysis. Presented at the Joint meeting of TIES 2004: The International Environmetrics Society and ACCURACY 2004: 6th International Symposium on Spatial Accuracy Assessment, June 28 – July 1 2004, Portland, Maine.