COVID-19 - who should we trust?

Posted on by Kate Wright

Image of a heart in nature

We are currently experiencing this strange, unprecedented and uncertain time, grounded by the COVID-19 virus, but what scientific information should be trusted? There are many, many uncertainties, some of which are acknowledged and some of which are ignored. But are we seeing a renewed respect for science from politicians and society as a whole?

Uncertainty is defined as “a situation in which something is not known, or something that is not known or certain” and is a fundamental feature of human life. Many publications which look at uncertainty include the classic quote from Donald Rumsfeld (from his response to a question at a news briefing on 12 February 2002, regarding the lack of evidence which linked the Iraqi government with the supply of weapons of mass destruction to terrorist groups):

“Reports that say that something hasn't happened are always interesting to me, because as we know, there are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns—the ones we don't know we don't know. And if one looks throughout the history of our country and other free countries, it is the latter category that tend to be the difficult ones”.

In scientific literature, the classic definitions of uncertainty types are:

· Epistemic uncertainty – this occurs due to limited knowledge or ignorance so in theory these types of uncertainty could become reduced (the ‘known knowns’ and ‘known unknowns’).

· Aleatory uncertainty – this occurs due to the randomness of the world, often regarding future events which can’t realistically be studied (the ‘unknown unknowns’). Within an economic context, this is also known as a ‘black swan’ event. It has been suggested that the current coronavirus situation is a black swan event but Kay and King dispute this and I agree as global pandemics have occurred before.

The scientific uncertainties currently faced are epistemic, current knowledge about COVID-19 is definitely limited, but will gradually become reduced as time goes on and as more data is collected and models are improved:

Data – there are uncertainties in the data reported by the media (although these are rarely acknowledged), for example, the UK daily death total given by politicians at the daily briefing comes from three sources (Public Health England, NHS England and the Office for National Statistics), each with different release times; and deaths in Care Homes and the Community were not included until 29 April 2020. In addition to this, testing of everyone with possible symptoms is not taking place in the UK, so it is unknown how many people have been affected, and therefore it is not possible to compare the death rate to the number of infections. Richardson and Spiegelhalter give an overview of the data and statistics being used for reporting and whether they should be trusted. Worldwide comparisons, particularly around the number of cases, are also difficult as different countries have different testing regimes.

Models – due to the lack of data and knowledge about the virus, models are the main tool for making UK policy decisions for the pandemic, however, there is disagreement about the models used initially. Obviously, due to the timescales involved, the science that the media is reporting has not been through the usual process of peer-review and there is a lack of consensus among academics (leading to the Royal Society Call described in a previous DSNE blog). Both in the UK and USA the models currently being used were designed for different illnesses and data for Covid-19 is limited, leading to contradictory results. There is also a controversy regarding the relationship between the spread of the virus and climate; again in the absence of peer-review, results have been published which are disputed, with questions about the modelling methodology as well as the dissemination methods used. These disagreements and contradictions can lead to confusion and an erosion of scientific credibility, especially if the uncertainties and methodologies are not highlighted, and could potentially exacerbate a public lack of trust in science already perceived. Hullman highlights concerns about the reliance on model predictions, without acknowledging uncertainties, which could in hindsight lead to reduced trust in science. Therefore, it is how the uncertainties are communicated that is the key – ignore them and lose trust; or admit to too much, leading to the potential erosion of the value of using the models for decision making.

In the last 4-5 years society has appeared to be entering a new psyche which has been labelled ‘post-truth’, leading to reduced respect for knowledge and ‘experts’. The definition of post-truth is “relating to a situation in which people are more likely to accept an argument based on their emotions and beliefs, rather than one based on facts”. In this current situation of the Covid-19 pandemic, there are various scientifically unconfirmed speculations that people are willing to believe - from the burning of mobile phone masts in the UK following the rumour that 5G has created the virus; to the preventative measures of drinking of hot water or alcohol (leading to deaths in Iran) or taking anti-malaria drugs. However, Governments have turned to science to help with their responses and it is science that is going to help us move on from the situation in the form of a vaccine or drugs that can help cure the illness. To quote Matt Hancock (UK daily briefing, 21.4.20) “every day the science gets better”.

UK Research and Innovation have launched a website to explain the science behind the virus. It is updated regularly and aims to “help to counteract web disinformation and pseudoscience about the virus”.

As more data is collected from around the world, the models and number of models will be constantly evolving, both reducing (and potentially creating more) uncertainties, it will be interesting to see how the science itself, and the reflective response to the science and credibility of scientists, will be reviewed in the future.

Related Blogs

Disclaimer

The opinions expressed by our bloggers and those providing comments are personal, and may not necessarily reflect the opinions of Lancaster University. Responsibility for the accuracy of any of the information contained within blog posts belongs to the blogger.

Back to blog listing