Disease modeling software can simulate the progression of a chronic disease, such as diabetes, and its complications. The model can reflect conventional standards for diagnosis and treatment, or it can model proposed changes in diagnosis or treatment, such as modeling the effect of a new drug. Although some software is available, it is for the most part proprietary. The goal of this project is to develop and distribute a computer model that is up-to-date and customizable to fit the needs of the user. We use diabetes and its complications as our example; however, the software can be customized to other disease processes.
Computer Models Can Show:
- Where more research is necessary
- The long-term effectiveness of an intervention (such as weight loss or glucose control)
- Where policy makers should focus efforts for early treatment and prevention
For example, diabetes is a complex disease with many stages and complications. Studies of diabetes are expensive and lengthy. Often, by the time a study is completed, there are new standards of treatment that were not considered by the study. Computer models of the entire process can help to provide a complete picture.
- To develop user-friendly software that helps researchers to model chronic diseases
- To improve estimates of progression rates between stages of chronic diseases.
This modeling software will be open source for the convenience of researchers. The second goal requires continual updating of the parameter estimates and comparison of those estimates to many different populations to verify that the estimates provided with the model are appropriate for all Americans, not just a few.
Computer modeling of chronic diseases allows a more comprehensive view of the disease process than a single study since the computer model can be comprised of the accumulated knowledge in the field. For a complicated disease like Type 2 Diabetes, which develops over an extended period of time, a single randomized clinical trial provides a fragmentary view of progression. However, implementation of a longitudinal study of disease progression is expensive, time-consuming, and rapidly out-dated.
Limitations of current methods:
Almost all of the existing diabetes models are not available to persons outside a proprietary health system. The few remaining models are limited in scope and applicability. Thus, individual researchers have to develop their own engine, interface, and output mechanisms in addition to the parameters. This replication of effort limits the progress and accessibility of modeling to diabetes researchers.
With disease modeling techniques, clinical researchers can place their results in the context of an entire disease process and simulate the long-term effects of an intervention. Moreover, models can inform policy makers of key areas to target for prevention or guide reimbursement policies for preventive care.