Covid-19 Coronovirus Data and Statistical Literacy

During the Covid-19 or Coronavirus pandemic our media, including social media, has presented many sub-optimal or plainly wrong statistical conclusions due to a lack of data or statistical literacy, and to justify libertarians’ and sovereign citizens’ beliefs placing the politics of economy and individuals’ freedom above the health of community and society.

From Wikipedia on statistical literacy:

‘Statistical literacy is the ability to understand and reason with statistics and data. The abilities to understand and reason with data, or arguments that use data, are necessary for citizens to understand material presented in publications such as newspapers, television, and the Internet.’

From The Conversation:

‘Now everyone’s a statistician. Here’s what armchair COVID experts are getting wrong.

If we don’t analyse statistics for a living, it’s easy to be taken in by misinformation about COVID-19 statistics on social media, especially if we don’t have the right context.

For instance, we may cherry pick statistics supporting our viewpoint and ignore statistics showing we are wrong. We also still need to correctly interpret these statistics.

It’s easy for us to share this misinformation. Many of these statistics are also interrelated, so misunderstandings can quickly multiply.

Here’s how we can avoid five common errors, and impress friends and family by getting the statistics right.

1. It’s the infection rate that’s scary, not the death rate

Social media posts comparing COVID-19 to other causes of death, such as the flu, imply COVID-19 isn’t really that deadly.

But these posts miss COVID-19’s infectiousness. For that, we need to look at the infection fatality rate (IFR) — the number of COVID-19 deaths divided by all those infected…..

2. Exponential growth and misleading graphs

A simple graph might plot the number of new COVID cases over time. But as new cases might be reported erratically, statisticians are more interested in the rate of growth of total cases over time. The steeper the upwards slope on the graph, the more we should be worried.

For COVID-19, statisticians look to track exponential growth in cases. Put simply, unrestrained COVID cases can lead to a continuously growing number of more cases. This gives us a graph that tracks slowly at the start, but then sharply curves upwards with time. This is the curve we want to flatten…..

3. Not all infections are cases

Then there’s the confusion about COVID-19 infections versus cases. In epidemiological terms, a “case” is a person who is diagnosed with COVID-19, mostly by a positive test result.

But there are many more infections than cases. Some infections don’t show symptoms, some symptoms are so minor people think it’s just a cold, testing is not always available to everyone who needs it, and testing does not pick up all infections.

4. We can’t compare deaths with cases from the same date

Estimates vary, but the time between infection and death could be as much as a month. And the variation in time to recovery is even greater. Some people get really ill and take a long time to recover, some show no symptoms.

So deaths recorded on a given date reflect deaths from cases recorded several weeks prior, when the case count may have been less than half the number of current cases.

5. Yes, the data are messy, incomplete and may change

Some social media users get angry when the statistics are adjusted, fuelling conspiracy theories.

But few realise how mammoth, chaotic and complex the task is of tracking statistics on a disease like this.

Countries and even states may count cases and deaths differently. It also takes time to gather the data, meaning retrospective adjustments are made.  We’ll only know the true figures for this pandemic in retrospect.

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