Something Doesn't Add Up!

"Everything appears inevitable once you get there, but we forget the journey."

There is much that is confusing about the Covid-19 virus pandemic. One so-called expert says 'wear a mask', even at home; and others say it's not necessary. To the layman, this is unsettling.

To make matters worse, the media and pundits misuse data. The death rate transfixes everyone. And yet, at best, the data is misleading; while at worst, it may drive responses that produce unwelcome outcomes.

It's worth restating that experts are only justified in their title and position when supported by evidence. Here is the immediate problem. The experts are deploying data that is dodgy.

In this blog, I'll examine the collection of data, its manifest flaws, misinterpretation and misuse. At times I'll argue against positions I took in earlier blogs to address the issues from all angles. 

As well as death rates, we have a morbid fascination with Covid-19 cases across nations to tally the numbers in a perverse competition. Again, the data is not up to the job. Let's examine why.

From the outset, we must be clear that the number of reported Covid-19 cases depends on the ability to detect the virus. Thus, if you live in a well-resourced place, with decent health services, detection is more likely.

​People in poorer countries, without testing, may conclude the virus hasn't reached them. This assumption is wrong. 

Next to consider is how the medical authorities elect to classify Covid-19, and, significantly, its role in any deaths. For starters, there is no agreed international standard nor classification tool-kit.

Thus, comparisons between places are problematic. Following that, you must decide what caused death. 

Was it Covid-19 or a pre-existing condition accelerated by the virus overwhelming the immune system? If a cancer patient tests positive for Covid-19, then dies, how can you decide the cause? In reality, this is tough.

​Get the figure wrong, and you may end up implementing precautions that cause more harm than the virus. 

To work out a death rate, we need three sets of numbers:-

• The number of people infected with Covid-19
• The number dying from or with the virus
• A relevant period of time.

​Moreover, we need data over a large population to generate statistically significant results. Data must be adequate in quantity to removes the possibility of randomness — I won't get into that.

Consider the first set of data: the number of people infected with Covid-19. With a range of symptoms appearing over an incubation period of up to 14 days, deciding who to test is hard.

A clinical examination alone may not be adequate when Covid-19 symptoms overlap with the common cold and other ailments. Yet, testing everyone is prohibitively expensive.

​It ties up medical staff and resources when needed elsewhere. Also, we know a fair number of people exhibit mild symptoms or are asymptomatic.

From this, we can infer there are many more infected people than our regime of testing detects. In turn, this leads to the conclusion that the death rate is much lower than the available raw data suggests.

For example, as of 30 March 2020, we have 724,201 confirmed Covid-19 cases worldwide and 34,026 deaths. A death rate of 4.69%. 

Yet, if infection rates are ten to twenty times higher, as some research suggests, then the death rate drops.

Next, we need to consider the reported deaths and ask did the virus kill the patient? As mentioned above, underlying conditions are playing a role that we need to recognise. Without that distinction, we are at risk of overreacting by implementing measures that backfire. 

As an illustration, to what extent is the death rate increased by isolating vulnerable older people? Living under stressful conditions, with a lack of exercise, these people are at risk.

Stuck at home, tuned into a 24-hour news cycle, that portrays all deaths as Covid-19 related, is not conducive to good health. If these folks have a pre-existing condition, as many do, are we accelerating their demise? 

The third aspect is time. Again, without uniform standards of measurement and the lag in gathering data, interpretation is hampered.

There is some agreement that Germany is accurately recording Covid-19 within its population by a high testing rate. Plus, they have a relatively high level of critical care — 29 beds per 100,000. Their death rate is currently 0.8%, which is similar to seasonal flu.

Meanwhile, virologists are coming to a consensus that Covid-19 will eventually infect 90% of the world's population.

Further, there is compelling evidence that the virus has been circulating for years undetected. The Lombardy region of Italy reported unusual cases of flu in November 2019, just before the Wuhan outbreak. 

So, what can we conclude from all this? 

• Covid-19 is much more wide-spread than the data suggests, and people are carrying the virus unaware.
• Covid-19 is more likely to kill you if you have an underlying condition; thus, by default, the elderly are at risk - 73% of the UK dead are over 75 years old.
• Death rates are difficult to determine, although at this time Covid-19 compares to seasonal flu.

Tuned into a 24-hour news cycle with its relentless focus on exponential graphs of Covid-19 cases and deaths, is bound to panic people.

​Layered atop that is 'Carol on Facebook' who is suddenly the go-to expert on Covid-19. Her often unhelpful rumours add to the psychological impact that drives panic buying of toilet paper. 

At times analysing the data feels callous, because behind it all are individual stories of people and impacted communities. That doesn't devalue its importance nor the importance of recognising its limitations.

As I say, without international standards for the classification of Covid-19 and recording deaths, comparisons between places are somewhat incoherent.

Add to that the reduced air pollution and road traffic, resulting in fewer deaths across populations, and you can see the picture is complicated.

How many other diseases are held in check by the precautions we've taken? Could it be that overall death rates reduce? 
​
Everything appears inevitable once you get there, but we forget the journey. We must ask, are we creating a predetermined higher death rate by confining the elderly and others to home? I don't know the answer. 

​We humans have a great hunger for explanation. In this instance, that explanation may have to wait until we get a fuller picture and verified evidence.