Covid-19: Forecasting and Modelling

Miriam Cates Excerpts

Tuesday 18th January 2022 Westminster Hall

[Miriam Cates (Penistone and Stocksbridge) (Con)]

I congratulate my hon. Friend the Member for Isle of Wight (Bob Seely) on securing this very important debate and making an excellent speech. I have no wish to repeat the brilliant research that he recited, but he did highlight the repeated failures of modelling throughout the pandemic, not just the modelling but how it is being used. The models have not been out by just a few per cent, as he said, but often by orders of magnitude. The way that the models have been used has had life-changing impacts on people across the country.

Before I was a politician, I was a science teacher. One of the joys of teaching science to teenagers is conducting practical experiments in the lab. Once the teacher has ensured that they are not going to burn down the lab, it is important to teach them how to conduct an experiment properly and write it up. The first thing is to create a hypothesis. They must write a statement of what they think will happen and why, using the scientific knowledge they have and some assumptions, then carry out the experiment, write up the research and, crucially, evaluate. They must look at the hypothesis and at what they have observed, and decide whether they match. If they do match, they go back to their assumptions and see why they were correct. If they do not match, if what has happened in the lab and been recorded does not match the hypothesis, they need to ask why—“What assumptions did I make that did not bear out in real life, that did not happen in the lab?”

It seems to me that those are the questions that have not been asked throughout this crisis. Perhaps we can understand why assumptions had to be made quickly the first time, for the first lockdown—assumptions that turned out not to be true. My hon. Friend said that perhaps we are repeating history of 20 years ago, and that there is not that excuse. However, during subsequent waves and restrictions, why were those assumptions not questioned? There were assumptions about how likely the different scenarios were, about people’s behaviour and fatality rates.

Even in December, when plan B was voted through, some of the assumptions could have been declared wrong in real time—the assumption that omicron was as severe as delta, and that the disease would escape the vaccine. Some of the figures were almost plucked out of the air and given no likelihood. Those assumptions should have been challenged earlier and we need to ask why.

I picked up on one assumption following an interview with Dr Pieter Streicher, a South African doctor. He suggested that SAGE models have always assumed that infection rates do not reach a peak until about 70% of the population have had the disease, whereas the real-world data suggest that the infection rates start to slow at around 30% of the population. That makes more sense from a social science point of view, because we know that people are not equally sociable.

Studies by sociologists such as Malcolm Gladwell, who wrote the best-selling “The Tipping Point”, describe the law of the few, where very few people are extremely sociable and pass on a virus, idea or whatever, to many people. Many more people do not socialise as much and are not as good at transmitting. Perhaps we should have looked a lot more at social science, at behaviour and people’s interactions, rather than pure virology and what might happen in a lab. Of course, we do not exist in labs and cannot model the interactions of human beings that easily.

The tragedy is that this was not a paper exercise. This is not an experiment that happened in a lab where one can go back and repeat until valid results are achieved. These models, and particularly the weight they have been given, have caused serious destruction of lives and livelihoods. Who was modelling the outcomes for education, child abuse and poverty? Who was modelling the impact on loneliness, despair and fear? We have to ask why those assumptions were not interrogated.

My hon. Friend the Member for Wycombe (Mr Baker) has made some excellent points about the need for institutional reform. I completely agree with him, but we also need to look at the impact on free speech. At the beginning of this crisis, the mainstream media took on the idea that lockdown was the only strategy.

[Mr Baker]

My hon. Friend spoke earlier about the repeatability of scientific experiments with hypotheses. One of the reasons I talked about C++ is that by using multithreading, it is possible to end up with code that does not produce repeatable outputs. Does she agree that it is very important that when models are run, they produce consistent and coherent outputs that can be repeated?

--- Later in debate ---

[Miriam Cates]

I absolutely agree with my hon. Friend. I would have said to my students, “It is not a valid experiment if you cannot follow the same method, repeat the experiment and produce the same results. It is completely invalid if you cannot do that.” I am not a software engineer, so I take my hon. Friend’s word for it when it comes to the use of programming languages, but he is absolutely correct that the whole experiment is not valid if the results cannot be repeated.

Over just the past few months, there has been an opening up of debate that has moved from The Spectator into mainstream media, where people such as my hon. Friends present have been able to speak more freely about the problems and costs of lockdown, and have not suffered so much criticism—I hesitate to say “abuse”—in the media and on social media. To avoid this happening again, we need institutional change, but we also need to understand that these are not black-and-white issues. It is good, right and wise to question the data and the science, and to put just as much weight on people’s quality of life—the things that make life living—as on the number of people in hospital at one time for a particular disease.