{"id":986,"date":"2013-12-20T18:03:53","date_gmt":"2013-12-20T23:03:53","guid":{"rendered":"http:\/\/mathewkiang.com\/?p=986"},"modified":"2020-01-11T16:11:40","modified_gmt":"2020-01-11T21:11:40","slug":"shiny-desolve-interactive-ode-models","status":"publish","type":"post","link":"https:\/\/mathewkiangcom.local\/2013\/12\/20\/shiny-desolve-interactive-ode-models\/","title":{"rendered":"Shiny + deSolve = Interactive ODE Models"},"content":{"rendered":"

\"\"W<\/span>hile taking a disease dynamics course, I thought it would be a good opportunity to learn how to\u00a0use the <\/span>Shiny<\/code><\/a>\u00a0package in R<\/code><\/a>\u00a0and create an interactive interface for some of my problem sets. After a few trial runs with smaller, simpler setups, I have wrapped up the side project (for now). You can see it in action here<\/a>\u00a01<\/a><\/sup> and you can view the final code on my Git<\/a>.<\/span><\/p>\n

At some point, I’d like to make an analogous version of these models using network-based approaches. However, all my work in network models has been done using Python so it might take a while.<\/span><\/span><\/p>\n

If you’re unfamiliar with compartmental models, they are deterministic models that use differential equations to describe the spread of an epidemic through a population. The Wikipedia page on them<\/a> is a pretty good place to start. The parameters on the page are described below\u2014note that certain parameters are only shown when they are applicable.<\/p>\n

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