Hospitalization Data for Texas – September 26, 2020

This is the weekly update of hospitalisation data in Texas. As usual, the source of the data is Texas Department of Health Services and the data is as of Sep. 25, 2020.

The downward movement of total number of hospitalisations in Texas seems to have stopped and levelled off :

Fig. 1 – Total number of hospitalisations in Texas April 12, 2020 – September 25, 2020

The three main contributing TSAs (Trauma Service Areas) are still E – Dallas/Fort Worth, P – San Antonio and Q – Houston, but Houston has now less hospitalisations than Dallas/Fort Worth:

Fig. 2 – Hospitalisations in TSAs E, P, Q April 12, 2020 – September 25, 2020

E – Dallas/Fort Worth and P – San Antonio look like they are going up again, if only slightly. If this is a new trend remains to be seen.

Fig. 3 – Hospitalisations in all TSAs except E, P, Q April 12, 2020 – September 20, 2020

The main driver V – Lower Rio Grande Valley is still going down, but for most others it looks like the either are stable or are going slightly up.

Conclusion: The downtrend we saw last week did not continue and for the moment, numbers are stable. We’ll have to see which way this will go in the next week.

COVID19 Situation in the Americas

I couldn’t find much of an overview of how the COVID19 situation looks like in the Americas, so I gathered some numbers from Google/Wikipedia about deaths in the countries in North and South America. For the moment I left out a handful of Caribbean Islands, I’ll add them later on.

So which countries are doing well in terms of number of deaths in relation to the total number of inhabitants, and especially, where do the United States stand in comparison. The chart was made with R, and data from Google/Wikipedia, current as of Sep. 22, 2020:

This is to be taken with a grain of salt, however. I am pretty certain that the reporting standards and data quality differ vastly from one country to another (something to be researched). However, this is the picture you get, when you take the publicly available data from Google/Wikipedia.

US Hospitalisation & Deaths Numbers – State-by-State

I thought it would be nice to have a nice and quick visual presentation that shows, how the different states of the United States compare in terms of of COVID19 hospitalisations and deaths numbers, including D.C., Puerto Rico and US Territories. It’s been done with R and with data from covidtracking.com. The data is going up to Sep. 24, 2020:

There are larger version of these charts (4096x4096px) for viewing and download :

There’s also a chart comparing the deaths numbers of the 56 US states, territories and possessions, which also was made with R and data from covidtracking.com (date current until Sep. 24, 2020):

Hospitalization Data for Texas – September 20, 2020

The data source is Texas Department of Health Services and the data is up to data as of September 20, 2020.

The downward movement of total number of hospitalizations in Texas continued past seven days, but seems to have slowed a bit:

Fig. 1 – Total number of hospitalizations in Texas April 12, 2020 – September 20, 2020

The three main contributing TSAs (Trauma Service Areas) are still E – Dallas/Fort Worth, P – San Antonio and Q – Houston, but Houston has now less hospitalizations than Dallas/Fort Worth:

Fig. 2 – Hospitalizations in TSAs E, P, Q April 12, 2020 – September 20, 2020

All other TSAs have also come down, V – Lower Rio Grande Valley even significantly so:

Fig. 3 – Hospitalizations in all TSAs except E, P, Q April 12, 2020 – September 20, 2020

Conclusion: Texas looks promising at the moment in terms of persons who require hospitalizations due to COVID-19.

Analyzing Swiss Baby Surnames with R

The Swiss Federal Statistical Office (SFSO) has some nice data with which you can play around. To hone my R programming skills, I grabbed a recently updated dataset for Swiss female and male surnames for babies in 2019. You can find the datasets here.

In fact, the have a dataset in px-format, which covers the years 2000 to 2019. Here you’ll find a description of this px-format.

The first challenge is to find out how to work with px-files. Thankfully, this is easy, the pxR package takes care of that. It imports a file in px-format and produces a data-frame that you can use like any other data-frame.

The second challenge was with one of the original column names “Sprachregion / Kanton”. This did not want to filter and kept me giving either a column name not found or an empty data-set. So I change this column name in the original file to read “Kanton” and it worked.

I thought I start with a density plot to see if this tells me anything about the names:

The names to the left are the ones that are not chosen by many, but there are an awful lot of these, lets call them rare, names.
The names to the right are the ones that are chosen by many, but there are not a lot of these, lets call them common, names.

A first look would seem to suggest that 2019 was a year in which the diversity of baby names chosen was the highest in this period (2000-2019) for both male and female baby names.

Some number crunching: Total number of (unique) names in dataset are (for 2019) 2765 (female) and 2702 (male). You can read the SFSO press release (no English version) to find out more on the most common names in 2019 and more.

If you want to have a look at the code I wrote, you can find it on github.

Tropical Storms Paulette and Rene on their Way of the Atlantic

There are currently two tropical storms making their way over the Atlantic towards America. Paulette and Rene:

https://youtu.be/5UsoapPEmSc

Their tracks for the next three days, according to NOAA are as follows:

Currently it seems that both will move in a North-Westerly direction stay out on the Atlantic Ocean and not make landfall in America, but we’ll know that in a few days.

A Reminder that Tsunami Risks also exists in the Atlantic Region

Mostly missed this Morning: An earthquake of magnitude 6.6 on the Mid-Atlantic Ridge on Sep. 6, 2020 06:51UTC.

The depth was given as 10 km by both EMSC and USGS, so it was quite shallow.
Obviously, this directly affects no one, BUT since it was in the middle of the ocean there always is a risk of some underwater events causing a tsunami.

Luckily, this time the risk was assessed by USGS on Tsunami.gov as non-existent.

Fig. 2 – Tsunami warning: No Tsunami Expected. Source: tsunami.gov

However, this is a warning for US East Coast, Candad East Coast and Gulf of Mexico States. Unfortunately, there doesn’t seem to be such a unique single source of information for Europa (or Africa or South America for that matter).

Tropical Disturbance 1 – Keep an Eye on this One

Currently there are three disturbances in the Atlantic. One of which has an 80% chance of tropical cyclone formation in the next 48%

Certainly worthwhile to keep an eye on this one. It’s a quite large area and it is expected to form a tropical depression (TD) in the next day or two.

The satellite view as of Sep. 6, 07:40 UTC looks like this:

https://youtu.be/mSS3rK4Qh1I

Hospitalization Data for Texas – September 4, 2020

The data source is Texas Department of Health Services and the data is up to data as of September 04, 2020.

The downward movement of total number of hospitalizations in Texas continued past seven days, but seems to have slowed a bit:

Fig. 1 – Total number of hospitalizations in Texas April 12, 2020 – September 04, 2020

The three main contributing TSAs (Trauma Service Areas) are still E – Dallas/Fort Worth, P – San Antonio and Q – Houston:

Fig. 2 – Hospitalizations in TSAs E, P, Q April 12, 2020 – September 04, 2020

Houston has almost come down to the same level of hospitalizations as Dallas/Fort Worth and all three are going down in numbers.

Fig. 3 – Hospitalizations in all TSAs except E, P, Q April 12, 2020 – September 04, 2020

V – Lower Rio Grande Valley is still the largest contributor, but still coming down nicely. O – Austin is also reducing hospitalizations in a significant manner.

For completeness, I include the data on total beds and icu beds available and occupied, however, the data is incomplete and I am not sure how accurately it describes reality:

Fig. 4 – Available and Occupied Beds and ICU Beds April 12, 2020 – September 04, 2020

If the data is accurate Texas is not – and was not – in danger of running out of available beds and icu beds.