Some notes on them here: http://nbviewer.ipython.org/gist/aflaxman/c93489dd19cee2eabf00
Monthly Archives: December 2014
This interesting article crossed my desk recently, A Year of Gun Deaths: What Slate learned from trying, and failing, to record every death by gun in America since Newtown. It is a long piece that touches on many of the things that make population health metrics hard.
It also drew my attention to an IOM report on Priorities for Research to Reduce the Threat of Firearm-Related Violence. Gun deaths are a public health problem.
Did you know I have a fork of PyMC3 that you can run at the same time as PyMC2? I don’t keep it up to date, but people seem to want it every once in a while. Maybe this will help someone find it: https://github.com/aflaxman/pymc
import pymc as pm2 import pymc3 as pm3
Good for head-to-head comparisons…
Playing around with that GIS optimization stuff was a chance for me to revisit some maze-making code I wrote a few years ago: https://healthyalgorithms.com/2011/01/07/piggies-and-mazes/ I wonder what age kids it will be good for.
A real, applied problem in spatial epidemiology crossed my desk last week, and it turns out that it is a super-fun combinatorial optimization challenge, too.
I don’t have time to play around with it a lot now, but I did try a little stochastic search, which makes me think that this will not be trivial to solve:
Is there a nice, simple reference for just what exactly these graphical model figures mean? I want more of them.
I wanted to include some old-fashioned statistics in a paper recently, and did some websearching on how to calculate R^2 in Python. It’s all very touchy, it seems. Here’s what I found:
I eventually went with this:
%load_ext rmagic x = np.array(1/df.J) y = np.array(df.conc_rand) %Rpush x y %R print(summary(lm(y ~ x + 0)))
I had an inspiration to make something a couple weeks ago for #MakeSomethingDay (the productive alternative to #BuyNothingDay). It is a finger-painting app that neighborhood kids have been enjoying. http://bl.ocks.org/aflaxman/a31763011f9da46fc6d2
I learned about a “big” data source for understanding air travel at the eScience incubator project talks last week, the DB1B database, aka the Airline Origin and Destination Survey. This is a 10% sample of all tickets for flights originating in the US, released quarterly since the 1993: http://www.transtats.bts.gov/DL_SelectFields.asp?Table_ID=289&DB_Short_Name=Origin%20and%20Destination%20Survey This must be good for something in global health.
Speaking of cool IHME seminars, last month we heard one on a social network analysis of the health policy actors involved in national-level policy change. So cool: http://www.healthdata.org/video/policy-development-integrated-community-case-management-iccm-national-and-global-levels-mixed