Lab-Life

Since a few weeks, I report the raw number of COVID-19 deaths in Maryland counties. If this gives an idea of the cumulative number of deaths - which is interesting - it doesn’t reflect the fact that some counties have more inhabitants than others. That’s why I plotted below the number of COVID-19 deaths adjusted for the population (i.e. the COVID-19-specific death rate): Today (May 16, 2020), in terms of absolute number of deaths, Montgomery, Prince Georges and Baltimore County are the top 3 counties (this is the same for cases but not in the same order). In terms of confirmed deaths per 100,000 population, the top 3 counties are Kent, Prince Georges and Montgomery. ...

Since a few weeks, I report the raw number of COVID-19 cases in Maryland counties. If this gives an idea of the cumulative number of cases - which is interesting - it doesn’t reflect the fact that some counties have more inhabitants than others. That’s why I plotted below the number of COVID-19 cases adjusted for the population: Evolution of COVID-19 confirmed cases in Maryland counties, adjusted and not adjusted for population, on May 11, 2020 ...

Following up on my two previous posts ( here and here), I am writing a third post on COVID-19 in Maryland because I believe we enter a new phase. Before continuing, please note that the same disclaimer as in my previous post applies here (in short: read the CDC and MDH websites for official information). In the first phase, the importance was to detect and make sure COVID-19 patients were treated (also: make sure not to overwhelm the healthcare system, flatten the curve, lower the baseline, & stay at home!). My two previous posts were following these efforts, thanks to daily data released by the Maryland Department of Health (MDH) on its dashboard. My second post will still be updated with the latest data from there, go read it! ...

Since the Maryland Department of Health (MDH) started to display number of COVID-19 cases for each Zip code in its dashboard, I was wondering how to display this information in a nice way. The MDH display the information as a map - very nice but it lacks from where each Zip code came from: is the number of cases increasing or decreasing? Following on my busy chart with the evolution of all Zip codes (and highlighting just one of them - that may not be the one you are interested in, see previous post), I created a simple dashboard where you can select the Zip code you are interested in and see how cases are evolving. You can play with it here: https://jepoirrier.shinyapps.io/md-coronavirus-zip-app/ (screenshot below). Enjoy! ...

This post was last updated on April 26, 2020. A new post from April 27, 2020 is here. Following up on my previous post, here are updated trends in Coronavirus cases in Maryland (USA), the state I live in. I am writing a second post because the Maryland Department of Health (MDH) updated its dashboard with way more data than before (more on this below). Before continuing, please note that the same disclaimer as in my previous post applies here (in short: read the CDC and MDH websites for official information). ...

Disclaimer: Although I work in infectious diseases, I’m not a specialist in Coronavirus. For the most up-to-date information on Coronavirus in the US, please visit the CDC website. For the most up-to-date information on Coronavirus in Maryland, please visit the Maryland Department of Health. That being said, now you can proceed at your own risk ;-) This post was updated by one post on March 28 and another one here on April 27. Read the latest one here. ...

I wanted to use the TwitteR package for R since a long time, I tried but didn’t do much of it. Today I found a few minutes, followed simple recipes (I admit), and looked at the number of tweets about flu today (November 13, 2018). Result: 283 tweets in English (I wanted to focus on the USA but, for some reason, I couldn’t … yet!). That’s not a lot. But remember we are only at the beginning of the influenza season 2018-2019 in the Northern hemisphere. ...

DISO1 - Data I Sit On, episode 1. This post is the first of a series of a few exploring data I collected in the past and that I found interesting to look at again … (I already posted about data I collected, see the Quantified Self tag on this blog) Life is short and full of different experiences. One of the experiences I don’t specifically enjoy but is integral part of life is commuting. Although I tried to minimize commuting (mainly by choosing home close to the office) and benefit(ed) from good work conditions (flexible working hours, home working, etc.), a big change occurred when I took a new opportunity, in 2015, to work in the Belgian capital, Brussels. ...

While parsing the general literature, I found this paper from van der Heide et al. (2017) giving some numbers about end-of-life decisions in the Netherlands these past 25 years. I was wondering if one could see similar evolution in Belgium. And I didn’t have to look very far: van der Heide cited another NEJM paper with Belgian numbers ( Chambaere et al., 2015 ; an attentive reader will notice “Belgian” data is “only” about Flanders, not the whole Belgium). ...

A few weekends ago, I was challenged by a friend to do more steps than him. Of course, I won ;-) But I noticed he was wearing his activity tracker on his wrist while I was wearing mine on my waist. As I noticed several times before, when I had an activity tracker on my wrist, these devices tend to capture some movements even if you don’t actually walk (while typing energetically on the computer or while driving for instance). So I took the opportunity of a small trip to wear 2 activity trackers, one Fitbit One on my waist and one Fitbit Charge HR on my wrist.