Statistics for Sustainable Development > Blog > Why statistics in the times of a pandemic?

Opinion Piece

Why statistics in the times of a pandemic?

I am writing this blog post as we start to see the light at the end of the Covid tunnel. Last year was awful in many ways, with the Covid pandemic causing millions of deaths around the world, disrupting lives, businesses, the economy, education, not to mention the rise in mental health issues, the near collapse of health services and people having to live in lockdown for months on end. We are already suffering some of the consequences of the pandemic, but many issues will turn up in the years to come.

And statistics, though usually a discipline considered difficult to understand, has been present every day in media, government and health body press conferences over the last year or so. It has uncovered the crucial role that statistics and statisticians have played in tackling it, some of which are highlighted in the following five points:

1. Graphics, models and forecasts, confidence intervals and the precision of estimates have been essential in helping us understand what has happened, assessing the current situation, and making predictions about the near future. We learned about the progress of its spread using several measures: the number of cases, number of cases per 100,000 people, number of hospital admissions, number of patients in intense care units, number of deaths and excess deaths.


Figure 1: The UK’s vaccine effect: cases, hospital admissions and deaths since vaccination started

We also understood what the vaccine efficacy is like by looking at the graph in figure 2, below:

2. Statistics has been revealed as a very useful discipline in making decisions based on data and making governments accountable for their decisions. It was used to make decisions about lockdowns and the roadmap out of it, school closures and openings, vaccination rollout, trips abroad and testing. Government decisions are complex, because they have to take into account many factors that are not usually present in epidemiological models, however the use of information from data has been revealed as one of the most important tools in its overall decision making.


Figure 3: Number of deaths due to Covid since 25th death in different countries at the beginning of the pandemic

3. It has also helped improve our understanding of risk and how to measure it: is it worth getting the vaccine if there is a risk of thrombosis? Is it a sensible measure to enforce the use of masks indoors? And outdoors?

4. Statistical numeracy is key to critical thinking. It enables us to scrutinise all manner of claims made on our attention. Is Covid just like a normal flu? See the answer in figure 4 below.

Figure 4: Is Covid no different to a bad flu season?

5. Last, but not least, all the above has been made possible because data has been readily available, demonstrating how crucial it is to science, government, and the public to collect good quality data and to make it publicly accessible. Many skilled people analysing the same dataset in different ways has allowed for multiple important discoveries. 

Finally, though not only linked to statistics, we have witnessed how the cooperation among scientists, multilateral organisations, health bodies and industrial companies can make a huge difference and help solve some of the most important issues we face. The incredibly quick development of different Covid vaccines is a good example to this.

And yet, although we have demonstrated that we can successfully tackle some of the most important issues in the world, why not use this power to tackle some of the others: make vaccines available to 100% of the world’s population, eradicate poverty, malnutrition, climate change…? Yes, we can, we just need the will to do so. 


Alex Riba (old)
Author: Alex Riba (old)

Alex is an engineer with over 20 years of experience teaching statistics and conducting research. Having worked as a statistician on a wide range of projects, he is particularly interested in processes that let data speak for itself, especially in meaningful ways for non-statisticians.

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