As I’m sure many of you know, Samoa is being ravaged by a devastating measles epidemic. Moe, the Measles Outbreak Explorer, is a tool to explore epidemiological data on the Samoa outbreak.
I am super curious what you think, and how you could possibly make this tool even more better. And, of course, if you can, please consider donating to UNICEF NZ’s appeal or Samoa’s direct donation fund.
What I noticed first was that it wasn’t clear if the rate of infection was close to linear or if you were plotting aggregates. The second thing is I’m not clear on CFR means. Finally plotting the additional horizontal lines (e.g. one between 2500 and 3000) because you are using two different scales isn’t immediately clear.
That might sound like a lot of negatives but I also know it’s hard to make an excellent graph.
You make some fair points. A lot of these plots are standardised in epidemiology and often for no better reason than ‘this is how we’ve been doing it since John Snow and the Broad Street pump handle’. Like specialised plots in all fields, you develop a reflex of sorts of being able to compare it to similar plots from other outbreaks.
The CFR plot is a good case in point. Age breakdowns of CFR usually show days as individual data points. It’s particularly important for epidemiologists to see not just the mean CFR for an age group but also if it’s ‘widening upwards’ (more bad days with high casualty rates) or slowly going downwards (improvement in CFR).
I will take your points to heart and probably add some more explanations and make the plots a little clearer. Thank you!
Ps. Welcome to the Plotly community! I feel honoured that your first post here was in response to me
is being ravaged by a devastating measles epidemic. Moe, the Measles Outbreak Explorer, is a tool to explore epidemiological data on the Samoa outbreak.