problem of the Lombardy (IT) is heavy ratio of Day to Day increment, then they have 50% of the infected population from rest of 19 regions
Info I found useful at the link below...forwarded to me by my son, seems to have good info about public health actions to reduce transmission rates. Some of the stats on coronavirus cases is US-centric, but the overall information and personal actions redcommended can work for anyone/anywhere. Hope you find it useful, if so, please pass it on to others.
These guidelines are intended to help Flatten the Curve with the COVID19 outbreak, to help limit spread and reduce the load on hospitals and other healthcare.
www.flattenthecurve.com
thx for such source, I like data
but problem of the data interpretation in such case is mostly based on:
-
deep dive outlook - e.g. w/o knowledge of Diamond Princes passengers age (seems to be more important) and their health conditions we can't precisely propagate infected or mortality data of such closed community ( 4,061 passengers and crew)
-
data source accuracy - as was seen in Europe, whole medical system hasn't been prepared for the tests of COVID-19 infections (what is base of accuracy for the identified infections), then till now we don't know how many people have been infected = real impact for the data science behind, e.g. for the mortality rate
-
objective comparison - most of news portal "analytics" tried compare data in wrong way (and from different sources, frequently):
a)
total confirmed infection number between two and more countries -it's wrong approach - because there is heavy difference between
currently infected (better number for the analyze) and total confirmed infection number (incl. recovered + deaths). It's like compare total revenue of company only w/o another more important indicators.
b) two different countries with different area size (km2) instead country specific regions (similar area size, density, ...). This is another wrong example of the disease monitoring. But they do that.
c)
reason of the increments (positive or negative) - we don't have detailed information what kind of scenario change some dramatic increments (more testing, more precise testing, in-house testing, more ...), then we can't compare data samples before and after, because then we get inaccurate outcome.
to be sure this consideration isn't about detracting of the situation. Just be ready to read the objective information from such information mess.