Talk:Population Modeling Working Group

Here are suggestions for directions - challenges and goals from Jacob Barhak. Feedback on these directions for the workgroup is encouraged.

Challenge 1: Interaction amongst Multiple disease processes: disease processes affect each other - for example diabetes increases the chances of cardiovascular disease. There are also interactions between mental health and diseases. Today there are ways to mathematically model multiple disease processes and therefore interactions should be studied. The methods to study these should be formulated and compared.
Challenge 2: Use of High Performance Computing (HPC) in population modeling: with computing power available today it is possible to digitally test multiple variations of assumptions and see if they fit observations. This method consumes much computing power that fortunately is available today through many HPC platforms. Figuring out how to best use such a technology is an important challenge.
Challenge 3: Multi scale modeling from individual to population to Multi population. It is possible today to gather information from multiple populations and extract information about observed phenomena. This is a powerful idea similar to assembling a cohort from many individuals - it increases the amount of information we have.
Challenge 4: Better sharing population information: Both de-identified population sets and aggregate data are becoming increasing available. Although tools such as can act as a phone book type repository, there is no database that allows sharing such data. Many issues need resolution, yet such solutions do exist and allow accumulating knowledge.
Goal: Better understanding of disease progression and driving factors from the accumulated knowledge we have: There are still large gaps in our understanding of disease progression - observed phenomenon are still explained locally, per study, rather than globally. These explanations differ and are not always sufficient to explain new data. The community has to find better ways to gain a wider perspective using and explaining as much data as possible.
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