There Dr. Gammino describes the hurdles faced by healthcare workers in countries where census data is often missing, where political, seasonal and geographical variations are making these more difficult. The description of the different social structures in urban or rural areas was also interesting. But the post also highlights how “social mapping” and geographic information systems (GIS) are helping understanding where the population resides and helping reaching them (here for polio vaccination but this could be for other purposes: maternity care, child care, etc.).
Now it would be interesting to see the two worlds collide: having these geotagged information feed a prediction model and reverting back predictions to healthcare workers in the field to inform them of potential areas to visit. This would have some implications for logistics and these efforts should also address privacy questions. But it would potentially help eradicating polio too.
The NPR has produced a nice visualization / video showing how population grew to 7 billion (original article):
If you want to model the improvement in child survival, you just turn the birth tap off (or nearly). Then, with wealth, prevention, healthcare and better food, the population will also grow older (death tap also turned off or nearly) and during a certain time, lots of adults will be economically active (i.e. they will work and consume). This is a demographic dividend. But it comes with a risk: at the next stage, there might be a disproportionately high number of people compared to / depending on a small number of active adults (the next generation). In addition, if you fill it up slowly but you also empty it slowly, the container risk to be full soon, it all depends on the various rates …
I continue in the serie of “World x Day” and for a reason still unknown even to myself, I thought today was the World Epilepsy Day (it’s in fact on March 26th, called Purple Day). But, anyway, epilepsy is “a common chronic neurological disorder characterized by seizures. […] Epilepsy is usually controlled, but cannot be cured with medication, although surgery may be considered in difficult cases.” (Wikipedia).
Out of curiosity, I was looking for mathematical models for the description of the epidemiology of epilepsy. But unfortunately, I couldn’t find anything. Probably because epilepsy is not an infectious disease for which tentative mathematical models have more predictive power (in terms of the population scale and time scale). The epidemiology of noninfectious diseases is primarily a study of risk factors associated with the chance of developing the disease. Nothing very fancy for a mathemarical model! 😉 (But if you find something, feel free to share! Thanks in advance!)
Today was World Leprosy Day. Leprosy has a high incidence in countries like India, Brazil and Burma (and other countries in the middle of Africa). But its incidence in occidental countries is rather low. This may explain why there isn’t a lot of epidemiological models of leprosy (I wish I had some time for this kind of thing).
FluTE is an influenza epidemic simulation model written by Dennis L. Chao at CSQUID. It works out-of-the box on GNU/Linux (just type make and run it).
I wanted to see how it works. But since I’m temporarily stuck with a Windows laptop, I downloaded a free C++ compiler for Windows (wxDev-C++), imported all the files in a project and compiled. For those who want to try, here is the project file and the specific makefile in a zip file (2 kb). Just decompress the FluTE archive (I used version 1.15), copy the two files from the zip file above and launch the IDE. In the project options (Alt+P), specify the custom makefile (in the "Makefile" tab) as the one from the zip file above. Compile (Ctrl+F9). Done.
On my Intel Core2 Duo T5450 (2Gb RAM), it took 6 minutes to simulate the "two-dose" example.
Please note that I didn’t try to compile with OpenMPI. Maybe for next time.