The Data behind Texas becoming a COVID-19 Hot Spot
A month ago, I created a Texas COVID-19 dashboard on Tableau. I used data downloaded from the DSHS (Texas Department of State Health Services) website to create the visualization. I shared the viz everywhere!
Fast-forward to today, Texas is in the national spotlight as a “Hot Spot”. Texans have been receiving “STAY AT HOME” text messages from officials. There are reports of travel restrictions on flyers coming out of Texas. Travelers are being threatened with quarantine as soon as they land. Bars are closed, restaurants are rolling back and tougher measures are still on the horizon…
With all of this coverage, I had to see the data for myself. I fired up my Tableau Public, refreshed the data and wrote this article.
Here is what I found
There has been a dramatic rise in the new positive cases of COVID-19 in Texas. The metropolitan areas has been hit the hardest. Most of the dramatic rise in positive cases has occurred in the last 3 weeks.
Texas is not isolated. Several states have made national coverage for rising COVID-19 cases.
With these new developments, new questions have been raised:
Could the rise in new cases of COVID-19 be due to the increase in testing? Are we simply uncovering people who are positive for COVID-19 but are not actually sick? Are we testing too much??
To answer these questions, I plotted a new data point: hospitalizations by day. I wanted to see if there were any changes to the number of hospitalizations in Texas.
My assumption was if someone showed up to a hospital they’re likely sick. Simply because most people will exhaust all home care options before stepping out. Also, it is well understood the hospital can be quite expensive — that is if you have health insurance. I don’t have words to describe what the hospital could cost without health insurance.
After plotting the hospitalization by day, I was astounded. There has been a rise in new hospitalizations and it correlates with the rise in new positive cases.
Not only is the correlation apparent, but you can see it began to accelerate in unison about 4 weeks after the “stay-at-home” orders were lifted.
Looking at the viz, the rise in COVID-19 statistics cannot be simply explained away with, “too much testing”. It is clear people are sick and they’re heading to hospitals.
The new question to ask is how much capacity remains at our hospitals?
Lastly, it is important to mention: I’m by-no-means an expert. I cannot begin to explain causation. I am only pointing out a trend in the data and a potential correlation.
