Will YOU die from COVID-19?

Will YOU die from COVID-19?

That’s really the burning question. With all the floating conspiracy theories commingled with distressing talleys, there’s a definite fuzziness to what the truth is. Especially with life or death consequences, you wanna know – will you get COVID and if so, will it kill you?

We want the truth!! 

And surely . . . “numbers don’t lie”. Right? 

Well, it’s more like numbers say a lot, ironically since numbers basically were created to concretize waxing poetics in business deals. But like subtitles or reading lips, it’s more subject to interpretation than you’d think. The corollary numbers are used to make “facts” which are not so much factual as we want to believe.  Politicians or advertisers or lobbyists or your neighbor weave agendas into those numbers, out of self-promotion or ignorance. Even if the “fact” could become altruistic in doing what the person or entity feels is best for the other person, or situation or population, that doesn’t mean it is the best advice or direction. Mark Twain famously remarked on the precarious power of numbers as “lies, damn lies, and statistics.”

Mankind invented numbers seemingly to define the world in ways that fluffy and flowery words did not suffice. The pyramids weren’t built in a day and for damn sure they weren’t designed and constructed with eloquent phases and impeccable grammar.  Numbers though are necessary to create engineering feats and roiling economies. Numbers though can be used to make things come undone as much as they build monuments.

Let’s not roll back to antiquity though to explain man’s desire to use numbers to create a better life. Instead let’s take a shortcut to today’s burning question about surviving COVID-19.


You either get COVID-19 or you don’t. You either die or survive. 

Black and white. Yes or no. 1 or 0. Right?

But some get COVID and the effects linger. For some, it’s annoying. For others, perhaps a bit more obtrusive or for some too, debilitating. And how long that lasts is just at the bow wave of understanding. 

Then some get it just like a cold to get over; some get it and never even know it.


So we will start with CASE FATALITY RATE – will a confirmed case of COVID-19 die? Bingo! Sounds straightforward. If you are a confirmed case, what’s your chance of dying? 

CASE FATALITY RATE = confirmed COVID deaths/confirmed COVID cases = % chance of dying

Well, yes, but first of all, 100% of the population isn’t tested every month, week or day. So confirmation is sketchy.  Asymptomatic carriers are well documented, so unless their work or situation require testing, a percentage of the population is an unreported “confirmed” case. In addition, people with milder symptoms might ignore or discount the possibility either out of ignorance, intent, or maybe to keep a job. Herd immunity is discouraged as a strategy but that doesn’t mean it doesn’t happen. Certain pockets of humanity within the United States and perhaps the island nations have evolved that way.

These factors mean that potentially a significant count of positive cases are never tallied into the “confirmed”. The good news is that the denominator is underreported, lowering the chance of dying. Yay! 

But we’re not done yet.

The numerator (up top) and denominator are actually moving targets.  If you are a confirmed case, it is yet to be determined if you’ll die from it. Death counts are former confirmed cases. You see how it’s a bit of a phased argument? The interim period could be days or weeks.

Nuts! So now let’s consider CRUDE FATALITY RATE. Forget about the “confirmed” part of counting cases because it’s not accurate anyway, right? 

CRUDE FATALITY RATE = #COVID deaths/total population = chance of dying from COVID

This makes sense; why not say it in the first place? Well, actually Crude Fatality Rate is often mistakenly interchanged with Case Fatality Rate. The difference though is significant. Not everyone in the population gets the disease. The denominator is inflated this time. People who never get the disease have 100% chance of not dying from it. All the hermits hiding in the hills and superimmunes are skewing the possibility.

So we’re still not there. 

So what we want is actually the INFECTION FATALITY RATE. If you are infected, then what is your chance of dying? (Duh, that’s what I said in the first place.)

INFECTION FATALITY RATE = # COVID deaths/# COVID cases = chance of dying from COVID

But as mentioned in Case Fatality Rate, we don’t know the denominator. Statisticians can estimate what that margin is and it’s not all guessing. 

But there’s also more grey areas and some factors to consider. First is that survival rate has time and location factors. Where you get it and when you get it make a difference. The early infected population did not have the consistency of care that was learned through the evolution of the spread. A simple consequence among many more subtle variants:  surviving COVID is more likely in a first world versus third world country.

Do we know really when it actually started? There’s plenty of anecdotes of people suffering an unusually severe flu outbreak prior to COVID alert. Reach back into water samples in Italy indicated the COVID presence months before the reported onset. If you got a cold prior to COVID outbreak, you were unlikely or less likely to seek medical treatment unless it was severe. Early deaths too could be contributed to ignorance and still learning how to deal with it.

Then there’s variants. As the disease continues, it actually evolves with survival mentality by morphing to counter the immunities of its hosts. Being in South Africa or the UK for the supervariants has challenged even the vaccine efficacy.

Subtle factors are built into the locality of the populations themselves. Italy’s population is older and more likely to be smokers – fertile ground for pandemic efforts. Conversely in the United States, the diversity of the population – geographically and ideologically – is markedly greater – leading to equally diverse results.

So let’s look at one more approach.


Excess deaths is a more raw data extraction.

EXCESS DEATHS = #deaths today/ # deaths same day last year

This is the most intriguing one as far as gross reporting but also opens another world of questions and interpretations. Without any limitations on COVID or not, how many more people are dying this year versus the last (pre-COVID as we are rolling through the soft start annual date.) That answer is easy – quite a bit more. So basically, you are more likely to die now than in previous years. You probably don’t care what your death certificate says for cause of death. 

But cancer doesn’t’ stop for COVID. Although a significant increase in cancer rates seems unlikely, heart disease would seem affected. More stress. More comfort eating. Perhaps less exercise – depending on location and personal drive. These are second and third order effects – ones we don’t measure or hear about through mainstream reporting. Social media has its own agendas and its reach is less understood than even the oddities of old school media.

60 Percent of the Time, it works every time

If you have read all this way ALMOST to the end and still want to know your risk, I don’t want to disappoint. Try this risk mortality calculator. Knowing what you’ve just read about numbers, keep in mind this calculator was developed from a February 2020 CDC paper using the data from 70,000 Chinese.

Researchers at the Johns Hopkins Bloomberg School of Public Health came out with a calculator in December 2020 that derived from multiple studies, including from “a large U.K.-based study and state-level death rates.” They used various studies that take into account age, sex, race/ethnicity, and medical history. The findings included specific data points such as “only about four percent of the population at high risk—defined as five times greater risk than the U.S. average—is expected to contribute close to 50 percent of the total deaths.” The research then drilled down further into geolocation: “the percentage of the adult population exceeding the fivefold risk threshold varies from 0.4 percent in Layton, Utah, to 10.7 percent in Detroit, Michigan.” The calculator is reported to be refreshed weekly, which is important since as described, the numbers do move with time. 

The Johns Hopikins calculator was developed for individual use as well as public health officials. By now, your gut should be on alert and your mind questioning both of these calculators for your individual chance of getting COVID-19 and dying from it.


We all pretty much want a bottom line. In the Navy and other services, often the summation is put first – Bottom Line Up Front (BLUF). This makes sense when time is essential; however, complex, evolving problems such as global pandemic have no BLUF. You have to work with conflicting data sources – and not get wrapped up in needing to believe any numbers are ground truth. To describe data in complex, evolving problems, you must understand that numbers are often as subjective as words. They have pitch and context and syntax and grammar!

That doesn’t mean the numbers of themselves are wrong. Using data and numbers add value to decision making. Numbers provide an intelligence that the pyramids and financial markets can attest. The err is in holding any data set as ground truth from which all action responds. If you hold that data set in esteem above all emergence, you are bound to fail.

It’s a Big Data world that everyone is treating as more of small data and that is disaster, like the communication failures of 9/11 and the breakdowns in COVID-19 response. Better tools are needed to get out of spreadsheets and powerpoint. Better training is needed on how to think and act in Big Data. From these building blocks of tools and training emerge the systems that actually capture and capitalize the Big Data world.  

The strength is to accept the expectation that numbers will conflict. Pandemic and cancer and world hunger are complex problems. The data has phases and iterations and evolutions that aren’t easy and certainly not straightforward.  No matter how clear we want direction, grey and fuzzy are there.

The Bottom Line belongs at the bottom for complex problems. Showing the math to get there is just as important as the solution. 


The Hard Is What Makes It Great

What’s the Big Data Idea is Bringing Big Data to the People.

I’m passionate about Big Data.

I believe Big Data will change everyone’s lives whether you like it or not.  It’s not just about how your cell phone let’s you know about traffic or that Target knows your daughter is pregnant before you do.  The immensity of the impact cannot be overstated.

I’m passionate about solving Big Problems with Big Data.

9/11 taught us many things about the world and how small it is.  That was a pre-Big Data world, too.  Unlike Apple computers and bleeding edge technology that only applied to the affluent, Big Data touches everyone – even illiterate natives in remote regions. Big Data can solve – perhaps for now assuage – Big Problems such as hunger, disease, piracy, terrorism, human trafficking, wildlife preservation.

I’m not a smart man…

But I know what Big Data is.  I know enough to combine it with my creativity and experience to make great visions of Big Data taking on Big Problems.

I’m not a baseball fan,

But I saw this clip recently and it reminded me about perseverance.

It’s not just about baseball.  There’s something each of us are passionate about doing.

For those that quit that passion.

For those that know they shouldn’t quit.

For those that believe in making a difference.

“The hard is what makes it great.”

Show Your Tatas! Big Data on Big Boobs, Bras & Breast Cancer

Big Data Takes on Big (Boob) Problems

Big Data application comes in many shapes and sizes and the Big Data Bra is Big Data wrapped in the silk and latex of lingerie.

It starts with a simple problem.

We have come a long way baby from corsets and elastic superstructures. The width and breadth of materials and configurations is overwhelming today and surpassed only by the running shoe industry in the utilitarian fashion spectrum.

Yet bras still don’t fit well. For those who “need” to wear them, they pinch and sag and ride up your back. You push and pull to pack ‘em in and in return they pooch and groan to comply. The straps spend a lifetime slowly paving a permanent groove over the shoulder. At the end of the day, within the privacy of the house, you pop the straining band – freedom!

Open the bra drawer on the dresser and the volume and variety (2 Vs) are apparent. There’s a bra for every occasion: bras for sweaters or for t-shirts or racer back or the all-day-at-work ones. The padded, the push-ups and the breast minimizers. Going to the gym or hitting the trail is a whole different world altogether, with sports bras equipped in extraordinary configurations to look good somehow while keeping everything from exploding.

Then there’s a whole undergarment wardrobe established just for after dark: the trying-to-be-sexy bra, the one bra that goes with THAT dress bra (the dress is sssoooo cute but only one bra works with it), and the worst ever compromise – the strapless bra. These all fall in the genre of i-can-last-x-hours at the restaurant/dinner party/gala event. There are pads and patches to replace bras and yes, in a pinch, the old school – duct tape.

One Size Does Not Fit All      

And neither does band width and cup. Travel to any department store lingerie section and you’ll find a world of bra selection. Unless you don’t really need one, it’s not about size and color. You want what looks and feels great. Although you want something pretty, it’s about fit and form. This is a perfect example of lots of selection but not true fit.

So you spend as long as you can stand in the lingerie section. Like the 20 year old dating strategy, you try on bra after bra to find the perfect match, or at least one that will work for a while. It’s a simple affair then but as time drags on (downward I mean), it gets harder to meet one you like. You know it may fit in the store and “hold much promise” but one wash later and it is on the path unfortunately to break down. Keeping it clean means delicate washes and special care but it’s going to unravel on you sooner than you like.

So how about flipping the market from push to pull? A single customer doesn’t need all that selection. She needs The One(s) that fit her. Wouldn’t the manufacturer and retailer want to sell her The One(s) without holding the inventory of racks of choices and piles of discards? Instead of creating a huge variety of bras that consumers can squeeze into, how about starting with the customer and tailoring the selection to her exact dimensions?

“3D printing is creating everything from cars to human tissue. Facial recognition software commonly creates 3d renderings from its algorithms.Facial recognition technology captures data form the contours of a person’s face and then computes the ratios between each feature. Since no face is exactly the same, each person would generate a unique data set based on the shape, size, and location of their features. This mathematical output can be compared and sorted for matching and identification purposes or to track a person’s identity.”


In a world of 3D printing and people recognition software, you should be able to take some pics and make a bra – 10 bras – that specifically fit the pic.

That’s where Big Data comes in.

My Big Data Bra begins with uploading personal pictures to the Volume, Variety and Velocity of data from Victoria’s Secret or Bali or Warner or Free People or Maidenform or Wacoal. They design and manufacture a bra – a suite of bras if they’re good – and send them to me, not the store.  They send me sales suggestions on replacements. I send them back my battle worn discards, which they computer analyze for fatigue to find the next best thing – for me – using the millions of data points around me. I can send new pictures to show adapting to life’s gravity effect or cosmetic repair thereof and they use the feedback for new bras. The cycle continues.

Nice Tata Thought, but How Does This Solve Big Problems with Big Data?

This consumerism creates a huge non-medical database of biometrics. These geo-tagged, anomyzed sources are deposited in data lakes with other data associations. It creates a new lens for researching breast cancer. Several breast cancer studies have already linked larger breasts to increased risk of cancer but the correlations are only suggestions.

More data would create more patterns and new opportunities. In one capability, the information is utilized by huge research organizations, using larger resources in order to view the Big(ger) picture.

Another angle is third party specialists create ways to analyze a personal signature against other Big Data sources – location, transactional, lifestyle, etc. – to assimilate breast cancer risk factors unique to that person. Third party providers could also suggest products and services to enhance breast life choices. There could be an app for using the pictures to tap into other big data resources for your overall health.

Further outside the box

Now blow this concept out of the bra. Whole body scans could do the same for total body health by incorporating pictures into your Big Data Medical Record. Providers use them as part of a holistic view of your health. Apps analysis helps assess your strengths and weaknesses. Big Data derives suggestions for effective and very personal results.

Like your cell phone traffic app provides a comprehensive picture of traffic for everyone, it also provides specific information personally important to YOU (like where u are).

We live in a global collective. Now is only the beginning of its documentation. Big Data is the ability to utilize those billions of millions of data points. Let’s use Big Data to solve Big Problems.