May your 2015 be filled with magic, dreams and good madness!

With more than 2,400€ collected, our team - Bordet’s angels - can be proud, for a first participation! We are 12th of more than 100 Belgian teams. One key learning is that the gold, old paper display still works better than anything else to raise money. And it was fun for me, a bit itchy in the end. But with the right trimming tools, this goes away very quickly. Thanks for all my supporters ;-) - your support is worth a thousand thank-you! ...

We’re nearly halfway through Movember, the month we grow our moustache in order to raise awareness about men’s health. I am in Amsterdam, for a congress and this was the hardest day of the month so far: since 8am, nearly every single person I met said it didn’t look good. And this can be harsh when you talk with (potential) business partners! However, practically, when you have time, this is an unique opportunity to initiate discussions with others about prostate cancer. ...

A contact matrix is a representation of contacts between individuals. For instance, in order to model the spread of rumors on social media, you ideally have to rely on contact matrices to compute the strength of bonds between types of individual agents. In the infectious disease world, a contact matrix is used to approximate contacts between individuals, e.g. between grand-parents and grand-children. In this blog post, after a short explanation of POLYMOD contact matrices, I will show how to get the data, process it and 3D print these matrices. Ready? 1. Finding contact matrices The most used contact matrices in epidemiological modelling are coming from the POLYMOD study, published by Mossong et al. in 2008. The study is a population-based prospective survey of mixing patterns in eight European countries (Belgium, Germany, Finland, Great Britain, Italy, Luxembourg, The Netherlands, and Poland). For that purpose their method consisted in common paper-diaries used by individuals to record information about their daily contacts (you might think this is so old fashion but nobody reproduced this study or did better so far!). So what does it look like (I’ll take Belgium as an example here)? You can see above a heatmap of physical contacts between participants and their contacts. The more towards the blue indicates fewer contacts. The more towards white indicates more contacts. Therefore the diagonal towards the top right shows that most Belgian participants have contacts with people of the same age. And this diagonal has two “wings”, representing interactions between parents in their 30s and their children. There are also two “bumps”, representing interactions between grand-parents and their grand-children. So these heatmaps are already something pleasant to the eye. But what if you could actually touch them? Can you actually physically play with them? This was made possible thanks to 3D printing, a manufacturing process that transform practically any custom 3D model created on a computed into a physical artifact. We’ll first need to get the data, process it in a suitable format and finally print it …

I recently read with interest Dr. Gammino’s post on how geospatial data and microplanning is helping the CDC and its partners to work towards the eradication of polio. 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.). ...

With a few days of interval I received two very different ways of reviewing data collected by users of “activity trackers”. The first one came from Jawbone (although I don’t own the UP, I might have subscribed to one of their mailing-lists earlier) and is also publicly available here. Named “2013, the big sleep” it a kind of infographics of how public (and mostly American) events influenced sleep of the “UP Community”. Here data about all (or at least a lot of) UP users were aggregated and shown. This is Big Data! This is a wonderful and quantitative insight on the impact of public event on sleep! But this is also a public display of (aggregated) individual data (something that UP users most probably agreed by default when accepting the policy, sometimes when they first used their device). ...

After a bit less than 2 hours, jepsfitbitapp retrieved my sleep data from Fitbit for the whole 2013 ( read previous post for the why (*)). Since this dataset covers the period I didn’t have a tracking device and, more broadly, I always slept at least a little bit at night, I removed all data point where it indicates I didn’t sleep. So I slept 5 hours and 37 minutes on average in 2013 with one very short night of 92 minutes and one very nice night of 12 hours and 44 minutes. Fitbits devices do not detect when you go to sleep and when you wake up: you have to tell tem (for instance by tapping 5 times on the Flex) that you go to sleep or you wake up (by the way this is a very clever way to use the Flex that has no button). Once told you are in bed the Flex manages to determine the number of minutes to fall asleep, after wakeup, asleep, awake, … The duration mentioned here is the real duration the Fitbit device considers I sleep (variable minutesAsleep). ...

After previous posts playing with Fitbit API ( part 1, part 2) I stumbled upon something a bit harder for sleep … Previous data belong to the “activities” category. In this category it is easy to get data about a specific activity over several days in one request. All parameters related to sleep are not in the same category and I couldn’t find a way to get all the sleep durations (for instance) in one query (*). So I updated the code to requests all sleep parameters for each and every day of 2013 … and I hit the limit of 150 requests per hours. ...

I continue to explore data about my physical activity in 2013 ( see part 1). We moved from an apartment (on the third floor of a building) to a house (with two floors) on July 1st, 2013. I was wondering if the change would have an impact on the number of floors I climbed: I now have to climb to reach bedrooms and go down to go in the living room. A standard house. ...

2013 is near its end and it’s time to see what happened during the last 360 days or so. Many things happened (graduated from MBA, new house, holidays, ill a few days, …) but I wanted to know if one could quantify these changes and how these changes would impact my daily physical activity. For that purpose I bought a Fitbit One in March 2013. I chose Fitbit over other devices available because of the price (99 USD at the time) and because it was available in Europe (via a Dutch vendor). At that time the Jawbone Up was unavailable (even in the USA) and the Nike Fuelband couldn’t track my sleep. ...