This project will develop software for assessing 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: (1) Conduct a requirements analysis to specify the simulation engines and functionality to incorporate in the software. (2) Develop and test a software prototype to evaluate feasibility of the proposed approaches. (3) Build, test and implement a complete software package based on results of the prototype. (4) 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 will accomplish 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. ? ?
| Jacquez, Geoffrey M; Sabel, Clive E; Shi, Chen (2015) Genetic GIScience: Toward a Place-Based Synthesis of the Genome, Exposome, and Behavome. Ann Assoc Am Geogr 105:454-472 |
| Koopman, J S; Jacquez, G; Chick, S E (2001) New data and tools for integrating discrete and continuous population modeling strategies. Ann N Y Acad Sci 954:268-94 |
