The pandemic has exposed broad problems in research: that many studies were exaggerated, flawed, or even fraudulent, and that misinformation could spread quickly. But it also demonstrated what was possible.
While it usually takes years to test drugs for a new disease, this time it took less than a year to find multiple vaccines and treatments. Scientists once discovered new virus strains only after an outbreak had already occurred, but now they have been able to use sewage samples to predict outbreaks in advance.
Not everyone saw the speed of this progress in a positive light: the belief that vaccines were “rushed,” for example, was one of the most common reasons people delayed taking them. Many people believe that doing science quickly would mean removing standards and creating sloppy, even dangerous research.
But that’s not always true, and the Covid-19 emergency has led many to adapt, produce and improve research at a quality and speed few expected. Not only could we avoid these trade-offs, but we could improve science in ways that make it faster, and the pandemic has shown us how.
Within six months of the outbreak, there were more than 30,000 coronavirus genome sequences, while in the same time frame in 2003, scientists were only able to obtain a single SARS virus sequence.
The speed at which coronavirus genomes have been sequenced is an achievement, but it hasn’t shown us the whole picture. While the UK has used an extensive genomics program to sequence almost 3 million coronavirus genomes, many countries have sequenced a few thousand in total, some less than a hundred.
Such disparities are common. In many places, across a range of topics, a lot of data is unmeasured or missing: the prevalence of mental illness, national GDP, and even records of deaths and their causes. Instead, it should be estimated with wide ranges.
It is difficult and expensive for small research groups to collect data on their own, so they tend to collect what is practical rather than comprehensive. For example, in psychology, research is often “WEIRD” – from white, educated, industrialized, wealthy, Democratic participants. In history, data comes from wherever records are common; in economics, where businesses recorded detailed accounts of their income and expenditure.
Different researchers measure the same data in different ways. Some people are contacted by several research groups studying the same questions, while others go unnoticed.
Without data measured in a standard way, it is difficult to answer questions about whether things are different and why those differences might be. For example, is anxiety more common in rich countries or more likely to be detected? Since the disease is undiagnosed in many countries and investigations are rare, we don’t have a clear answer.
This tells us one way to accelerate science: large institutions, such as governments and international organizations, should routinely collect and share data instead of leaving the burden to small research groups. This is a classic example of “economies of scale”, where large organizations can use their resources to create tools to measure, share and manage data more easily and cheaply, and at a scale that small groups cannot.
