5 things self-proclaimed COVID-19 ‘experts’ get wrong about statistics
1. It’s the infection rate that’s scary, not the death rate
Social media postscomparing COVID-19 to other causes of death, such asthe flu, imply COVID-19isn’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 (a number we can only estimate at this stage, see also point 3 below).
While thejury is still out, COVID-19 has ahigher IFRthan the flu.Posts implyinga low IFR for COVID-19 most certainly underestimate it. They also miss two other points.
First, if we compare thetypical flu IFRof0.1%with themost optimistic COVID-19 estimateof 0.25%, then COVID-19 remains more than twice as deadly as the flu.
Second, and more importantly, we need to look at the basic reproduction number (R₀) for each virus. This is the number of extra people one infected person is estimated to infect.
Flu’s R₀is about 1.3. Although COVID-19 estimates vary, its R₀ sits around amedian of 2.8. Because of the way infections grow exponentially (see below), the jump from 1.3 to 2.8 means COVID-19 is vastly more infectious than flu.
When you combine all these statistics, you can see the motivation behind our public health measures to “limit the spread.” It’s not only that COVID-19 is so deadly, but it’s also deadlyandhighly infectious.
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 trackexponential growthin 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, as shown below.
However, social media posts routinely compare COVID-19 figures with those of other causes of death that show:
Even when researchers talk of exponential growth, they can still mislead.
An Israeli professor’swidely-sharedanalysis claimed COVID-19’s exponential growth “fades after eight weeks.” Well, he was clearly wrong. But why?
His model assumed COVID-19 cases grow exponentially over a number of days, instead of over a succession of transmissions, each of which may take several days. This led him to plot only the erratic growth of the outbreak’s early phase.
Better visualizations truncate those erratic first cases, for instance by starting from the 100th case. Or they use estimates of the number of days it takes for the number of casesto double(about six to seven days).
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 testingdoes not pick up all infections.
Infections “cause” cases, testing discovers cases. US President Donald Trump was close to the truthwhen he saidthe number of cases in the US was high because of the high rate of testing. But heand othersstill got it totally wrong.
More testing does notresultin more cases, it allows for amore accurate estimateof the true number of cases.
The best strategy, epidemiologically, is not to test less, but to test as widely as possible, minimizing the discrepancy between cases and overall 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 asa month. And the variation intime to recoveryis 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 beenless than halfthe number of current cases.
The rapid case-doubling time and protracted recovery time also create a large discrepancy between counts ofactive and recovered cases. We’ll only know the true numbers in retrospect.
5. Yes, the data are messy, incomplete and may change
Some social media usersget angrywhen thestatistics are adjusted,fuellingconspiracy theories.
But few realize howmammoth, chaotic, andcomplexthe 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. Equally so, early models were not necessarily wrong because the modelers were deceitful, but because they had insufficient data to work from.
Welcome to the world of data management, data cleansing, and data modeling, which many armchair statisticians don’t always appreciate. Until now.
This article is republished fromThe ConversationbyJacques Raubenheimer, Senior Research Fellow, Biostatistics,University of Sydneyunder a Creative Commons license. Read theoriginal article.
Story byThe Conversation
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