Plenty of high school discussions are easily forgotten, but I was just reminded of one from 2007. A bunch of us knew we would soon be taking science courses in university, but we were asking whether it was best to stay in the field or move onto something more applied. One thing I said was "I will keep doing research as long as I can put all my articles online for free." A friend of mine who has always been very well informed replied with "don't count on that." At the time, it was not clear whether a scientific career could reasonably follow that philosophy. But now I am thankful to say that it can. The friend I mentioned and I are both contributing papers to Open Access repositories and millions of people have joined this movement in recent years.
Last month, my campus had a symposium on some of the Open Access efforts happening in the surrounding area. I signed up as soon as I saw that it was kicking off with a documentary on Aaron Swartz. When people there heard that I was one of the school's physicists, they applauded us for being early adopters. This is a clear reference to the arXiv — the primary source for everyone in my field, which has turned publication into a pure formality. I cannot really imagine only learning what an author has been up to after peer review has finished. Another physics success story (ranked surprisingly highly for something I only learned about two years ago) is Living Reviews in Relativity.
But of course the talks did more than just suck up to these projects and bash Republicans. They taught me a few things about the journal ecosystem including areas where physics is no longer in the lead.
As those of you who read my third most recent post will know, I recently became excited about methods for predicting the spread of diseases mathematically. When I learned about compartmental models, I began searching for tips on how they could best be applied to real data. I stumbled upon a solution on Abraham Flaxman's blog, Healthy Algorithms.
In Abraham's post, he presents some code that will estimate the parameters in a dynamical system using Bayesian Inference - the most elegant thing to come out of statistics since the Central Limit Theorem. Also present is an exercise challenging the reader to estimate the parameters of a 1967 smallpox outbreak in Nigeria.
If you want to do this exercise without a spoiler then stop! Otherwise, keep reading and I will tell you how I approached the problem while making some random remarks on the strengths and weaknesses of this particular fitting routine.
Two and a half years ago, when I read the research interests of my statistics prof, I noticed that he had become interested in analyzing epidemiological models. Now, I might finally understand what he was talking about.
If we let S be the population of individuals who are susceptible to the disease, I be the population infected with it and R the population that has recovered, it is not too big a stretch to say that this plot appears to follow the progression of a non-lethal disease. Only a small number of people have the disease at the beginning, but this number grows because the disease is contagious. People who have recovered are immune to further infection meaning that the epidemic eventually dies out.