For those who are coming across this for the first time, this is my continuing journey to perform my own tracking of COVID-19 in Texas. Why? Mainly because I because fed up with the cherry picked numbers that are being represented by the state government and the media. I wanted to show the meaningful stats, in a quickly digestible format, that allow people to see what is happening and make their own decisions. Secondly, I am a stats guy in the medical industry, so I’m doing it for my own edification as well.
This was a very interesting week for multiple reasons. First, the positivity rate dropped for the 4th week in a row! That ‘s excellent news. Consecutive drops of 0.155%, 0.243%, 0.244% and now 0.203%. Second, we have a drop in the death rate per infection for the first time since I started generating the stats for this tracker (this is also directly related to the corresponding increase in survival rate/infection). More good news! Next, we have a single digit increase in deaths >65 per 100K population for the first time since I started tracking. This week’s increase was half of what it has been in recent weeks. And finally, there is, essentially, a completely flat survival rate per population. With a decrease of only two thousandths of one percent, it is virtually unchanged from last week. What does all of this mean? Well, even with a deeply flawed testing and reporting process, there are multiple tangible indicators that things are leveling off. This is with testing reporting that has been reporting multiple tests from the same individuals (re-testing for work or personal reasons, for example), reporting “probable” positive cases based on exposure, and RNA tests that can give different results for a number of reasons.
Today’s bonus statistic: Sweden, with essentially no lockdowns, has a death rate per 100K people of 57.6. Texas, which has been largely shut down for six months, has a death rate per 100K people of 50.6.
Use this as you will and please draw your own conclusions. In my mind, the picture is very clear now, but you may see it differently. My hope is that there will be enough of an open dialogue, which includes meaningful facts, that we can speak with confidence and make appropriate decisions to get the world back to normal.
Stay tuned and subscribe. I share this data weekly on my Facebook and Twitter pages, but I will try to start publishing them weekly here as well.
So, as some of you already know, I started collecting and analyzing the Texas COVID-19 data on my own a while back, largely out of frustration with the way the media (and the state) have been presenting it. There are numbers that stir a sense of fear with no basis for comparison, and there are numbers that can be utilized to compare and contrast in order to make good decisions. Most of us can probably agree that the COVID-19 numbers, in both collecting and reporting, have been dubious at a minimum. We’ve seen questionable death certificate numbers, false positives, false negatives, delayed reporting, and so on. Still, they are the numbers we have, so I wanted to use my background in statistics to at least create a clearer picture of the situation. So here we are…
I decided to include last season’s complete flu data and this season’s YTD flu data as a means of comparison. Yes, the diseases are different, but there has to be come kind of baseline in order to determine how much we need to freak out at any given number. No comparison = no frame of reference = no perspective. I update the YTD flu column weekly as the next report is published by Texas HHS. There are approximately 5 weeks remaining in the current flu season and a 2-3 week lag in the publishing of the reports. I also decided to add a column at the end for the degree of change over the previous 7 days and a directional arrow for quick reference purposes. You might say, “That’s a lot of numbers, nerd! What does this shit even mean?!” That was rude, but I’m still glad you asked! The numbers in the top half of the chart are the raw totals taken directly from the Texas HHS web site. The bottom section is where I earn my pay on this – which is exactly zero dollars. These are the calculations and trends that I believe are the most important. A statistic shown as per 100K people is a great one to follow if percentages and decimal places make your head hurt. It’s an apples to apples way to look at complex data. For example, I have used per 100K people calculations to compare different states as well, since the populations vary so dramatically. I split this out by the overall rate, over the age of 65, and under the age of 65, as a way to slice and dice what is really happening in Texas. I chose 65 because that has been the age category the state uses for flu in recent years, thus making it a quick way to compare all of the data across the rows. The bottom two rows are also good to follow and compare if you like percentages and decimal points. The “Survival Rate/Infection” is the rate of survival if you catch COVID-19 and the “Survival Rate/Population” is your overall survival rate based on the population of the Texas – so it’s your actual chance of dying from it as a resident of the state. That number is currently tracking almost identical to the previous two flu seasons. Even if you (hypothetically) double the number of COVID-19 deaths from the current report, that percentage doesn’t move very much because we have almost 30 million people in the state, which is a good thing when trying to assess severity.
Why did I move this week’s result from my usual Facebook post to my blog site? Well, I normally don’t inject much of my personal opinion into these weekly posts. I like to just present the data that I collect and let folks decide for themselves what it means to them. I still think that is very important because how I assess risk might be different than someone else because everyone’s circumstances are unique. With that being said, I have been crunching these numbers for months now, so I have started to develop my own analysis of the numbers. And that’s why we’re here. I am ready to share some opinions and I needed more paragraphs than what is practical in a Facebook post. Plus, there are probably some folks that don’t really give a shit about my opinion on this but they like to see the numbers, so I added the need to click one more time on Facebook to see this blog post. I’ll still probably post the chart in the comments section when I link to this in Facebook, just in case folks don’t want to see my analysis or opinions.
The Meaty Goodness:
This is not intended to frighten, but perspective is very important. Flu and SARS-COV-2 (COVID-19) are different diseases with some unique situations, but the numbers in my weekly chart still provide some good similarities for comparison purposes. A brand new, fast tracked, SARS-COV-2 vaccine will likely have a similar (or lower) success rate than the flu vaccine that has been around for years and is updated annually, tweaked, and perfected over time. You might also have people getting the new vaccine at a higher rate than the flu vaccine because of the current awareness level, media hype, and social climate. So, what does all of that mean? People will still get sick and there will still be deaths from SARS-COV-2 even after a vaccine is released and widely distributed. That’s just the reality of the situation. There is not a magic bullet and waiting for one is simply unrealistic. Arguably, it is also irresponsible because of the “collateral damage” that we are seeing from depression, suicide, substance abuse, domestic violence, business closures, etc.. Hunkering down to figure out the scope of this disease was the right thing to do. It was new and very unknown. We did need to “flatten the curve” to avoid overwhelming the health system. Now, we are equally obligated to make decisions that minimize the very real collateral damage. New diseases, new types of flu, and new viruses make their way around the planet every year and we mitigate the risk the best we can (wash hands, take vitamins, healthy lifestyle, vaccines, treatments, etc.) in order to carry on with our lives. This disease is no different in that regard. There will be both preventives and therapeutics readily available. Some are available now and some are being vetted out as I write this. Be informed. Look at numbers that matter. Determine your own personal level of risk. Minimize exposure if you are showing symptoms. All of that should be pretty easy because it’s the same thing you would do if we were in the midst of an unusually bad flu season or of there was an uptick in another existing disease in any particular season. Prepare to move on and re-enter the world, maybe not today, but it’s coming. It has to. While there might not be a magic bullet for this new disease, that’s ok! In the grand scheme of things, there isn’t a magic bullet for anything that we encounter in the world, yet we go about our lives and we thrive by making the best decisions possible based on the best information available. We experience the joys and disappointments of life. We celebrate. We win. We lose. Above all, we experience the world around us because that’s what humans do.
As always, regardless of your opinion on this or anything else, don’t be a dick.
Professor Neil Ferguson of the Imperial College of London was, until very recently, an advisor to the UK government for its COVID-19 response until he resigned that position on May 5th for breaking his own lockdown recommendations to hang out with his girlfriend. He must really think those restrictions are important for everyone’s safety, right? His models at Imperial have also been the foundation for many governments around the world to make their decisions about how to handle the virus. The model used for COVID-19 was developed in 2005 and has essentially gone unchanged in the 15 years since. In addition, Ferguson is the ONLY one allowed to analyze the data, despite having a large team at his disposal at the college. While that’s not necessarily nefarious, it is incredibly arrogant at a minimum, which brings with it its own inherent issues.
How have Ferguson’s prestigious models performed in the past? (He has won awards for at least one of them). I’m glad you asked!
FERGUSON’S PREVIOUS MODELS*
Foot and Mouth Disease (UK)
BSE/Mad Cow Disease (UK)
Swine Flu (UK)
1.5-2.0 Million (US)
83K (May 12, 2020)
**Ferguson even admitted that his model was based on the spread of an influenza pandemic, which tracks differently than this coronavirus.
When questioned about the disparity in numbers in his COVID-19 model, his reply on April 16, 2020 was, “I much prefer to be accused of overreacting than under-reacting. We do not have a crystal ball.” We do not have a crystal ball?? Then stop being a crystal ball salesman!!
I have also seen people defending Ferguson’s model by stating that his doomsday predictions in the examples above led to actions being taken (X), so clearly his predictions led to the actions that ultimately lessened the impact (Y). That may be the case, but it might not. It ignores the concept of Post Hoc Fallacy: Since event Y followed event X, event Y must have been caused by event X.
Modeling alone is extremely risky. It requires a lengthy list of assumptions to be built into the formulas. Simply put, assumptions create a lot of noise when trying to confidently or accurately predict the future. Even the greatest model ever developed would likely not pass a statistically based validation. Assumptions are inherently biased, not necessarily in a malicious manner, but biased nonetheless because they’re nothing more than an educated guess that forces multiple manipulations of the data processing (think: garbage in/garbage out). If you pile a stack of those into a model (and you must by nature of the process), each level of bias would add another layer of “noise” that compounds itself each time something is off in an assumption. For many years, I have developed and worked with complex statistics every day in the medical device industry, and have done the same in the automotive and defense industries as well (nearly 30 years total). Modeling can be used as a very high level “stab” at what we think might happen, but the decisions must to be made, supported, and justified, by actual proven statistical methods. Companies would never get a product to market using modeling alone. Never. To further that narrative, in today’s world, we could even argue that mass shutdowns, universal shelter in place orders and the upcoming “new normal” (whatever that is going to look like) are all “products” that have been taken to market based solely on predictive modeling.
Predictive Modeling is a still a potentially great tool when it’s used in conjunction with multiple additional tools, including a constant feedback loop to make adjustments based on real data that is coming in – a comparison of the real data to the model data to continuously improve the assumptions and make it more accurate. You also have to look at factors that models do not take into account; like psychology, unemployment, mental stress, economics, domestic violence, suicide, etc., that can’t be accounted for in a laboratory setting. This is why political leaders need to look at the entire picture and make holistic decisions that balance all facets of this very complex situation. Otherwise, wouldn’t we just have epidemiologists as our political leaders all over the world if we believe that disease is the most substantial threat to our existence? We don’t, because that would be a short-sighted and incredibly incomplete view of this crisis that would cause very real, measurable, and catastrophic collateral damage.
This is also why the, “So you think you’re smarter than an epidemiologist? You don’t have any business talking about this topic…” argument doesn’t hold water. You don’t have to be the smartest person in the room to understand that this is not a one-faceted crisis and that all aspects must be thoughtfully considered when making life and death decisions on an unprecedented scale. You need the economists, the mental health experts, the politicians (gasp!), physicians, labor leaders, small and large business owners, and more in order to understand the complete impact of these decisions. You’ve heard the old saying that “Knowledge is Power,” right? Well, willfully ignoring ALL of the knowledge as it relates to this virus is not only irresponsible, but downright dangerous. Ignoring all of the available knowledge is also choosing to live in fear rather than facing it head on and making informed decisions based on the overall calculated risk.
Stay safe. Work hard. Don’t be a dick.
*There are multiple citations available for these numbers
My name is Diana Cox. This is my husband’s blog page – but he shares with me sometimes.
I’ve taught Texas Government as adjunct faculty at Tarrant County College (North East Campus, Hurst, TX) for 5 and a half years. For those that don’t know – yes, Texas Government is a required course in Texas for a bachelor’s degree from a state school (it includes geography, economics, demographics, light history, voting procedures, elections, function of state government, local government, federalism, and public policy. So the content is not just “Yay, Texas!” but there is some of that, too – at least in my class).
Like most, throughout my educational experience, I’ve had and/or heard of many professors/instructors who teach in a politically biased manner. Partially motivated by this, I set forth the goal of teaching a (mostly) politically unbiased course – where I would strive to provide both sides of an issue, or at least present the major arguments on each side. I would advise my students of this at the beginning of the semester. I felt that students are there to learn about the subject, not to be indoctrinated (or turned off) by an opposing viewpoint – but to have the information to be able to think and form their own opinions. To be fair, this approach isn’t purely noble – as I know that to best be able to argue one’s own side, it is most effective to know what the other side believes (and why) – so it helps me to solidify my own beliefs and arguments as well.
At the end of each semester, I would set aside time (between a quiz and an exam review – ensuring my own “captive audience”) to share my partisan affiliation (after making my students guess) and share my own personal political stances and reasons.
I’ve told friends/family about this over my years of teaching – and have had several requests to either attend or to just hear it. As I’ve decided to take a break from teaching, I did record my final (for now) “speech” – I hope you enjoy.
Spoiler Alert: I’m Republican.
Note: This is “off the cuff” – my only notes being some bullet points on the subject areas I wanted to cover. It is primarily conceptual and anecdotal – given a limited time frame, it doesn’t go into deep statistics or numbers.
Another Note: This is 1 of 2 speeches/monologues/diatribes (whatever you want to call it) – the second is about 10 min longer, slightly different in some ways, but generally the same ideas. If for some reason you’d like to hear it, email me and I’ll send it to you. My sweet husband reviewed both and we agreed this was the better one for sharing.