Home>R-numbers: predicting the future

R-numbers: predicting the future

This issue’s provocateur is James Munday, an infectious disease epidemiologist at the London School of Hygiene & Tropical Medicine. His work on understanding how quickly COVID-19 is spreading has been in the national spotlight for the last 12 months.

Even before mandatory masks and working from home and an endless cycle of lockdowns there was a group of scientists working on advanced mathematical techniques trying to determine the future.

James Munday was one of those scientists. He works on the team that calculates the UK’s COVID-19 Reproduction number (‘R’ number) – a number which signifies how quickly the virus is spreading and which directs the government’s response.

With the spotlight firmly centred on Munday and his colleagues, we ask about the lasting impact this important work has on public perception, trend forecasting and government policy.

Q – What exactly is epidemiology?
Epidemiology is the who, what, when and where of infectious disease. We try and understand the dynamics of transmission, who’s being affected and the implications of that. We also cross over into health economics and look at the economic burden and try to assess the impact of that burden. The other side of what we do is to look at how we can control the virus and what the most effective ways to intervene in transmission will be.

Q – You have experienced the pandemic as a civilian but also as an epidemiologist with a unique understanding of the situation. What’s that been like?
There’s a scene in the TV programme Chernobyl where the main scientist, Valery Legasov, is sitting in the bar of the hotel that he’s staying in, and a woman identifies that he’s there because of the fire. She asks: “Is there anything to worry about?” He’s just realised that the entire place has been exposed to a huge amount of radiation and all of these people’s lives have been massively cut short. He simply replies: “No. Nothing to worry about.”

It felt like that in January last year when we were getting the early data in from Wuhan, China, and observing the rate at which the virus was being transmitted and the early estimates of the case fatality ratio. I’d be sitting on a train listening to people talking about Coronavirus saying things like: “I’m sure it’s nothing to worry about,” and “it’s going to blow over.” Meanwhile, I’m working on my model that is showing just the opposite. It was a really bizarre few weeks.

Q – As far as I’m aware the COVID-19 crisis has resulted in one of the most public – and meaningful – applications of mathematical modelling the world has ever seen; it is the reason the country locked down. What do you think about that?
So, I think it’s sort of true. A lot of people don’t realise that the work of epidemiologists, and academic epidemiologists included, is always massively impacting policy. Every immunisation programme, for example, is rolled out based on mathematical modelling.

The difference this time is that it is a really acute example of how modelling can impact people’s lives. It is interesting how the media portrays it, because in reality decisions are not made on single papers. Neil Ferguson’s Report 9 wasn’t the paper that caused lockdown; there was a raft of evidence provided by a large number of people which went into that decision.

Academic epidemiologists want their work to be used to support policy-making. It is a very mission-focused field.

One interesting, yet worrying, aspect is that it seems like over the last ten months academics have become accountable, almost personally, to the public.

That is problematic; it could impact the advice that scientists give. Scientists need to be impartial and completely reply on what’s being observed to give advice.

If you’re observing something that is going to be unpopular or challenges the status quo, and it’s going to be scrutinised publicly outside of the scientific, peer-reviewed process, that can be a daunting prospect to publish your findings. It’s an interesting dynamic that’s emerged, which is different from what epidemiologists have experienced in the past.

Q – It certainly is a new dynamic, especially when the results of your research have such high stakes. What are you working on at the moment?
My team has funding from the Wellcome Trust to work on real-time modelling and forecasting. Pre- COVID-19 we were working on measles, Ebola, and cholera models, but then in January 2020 we repurposed the whole team to look at COVID-19.

The majority of our work is providing estimates of the reproduction (R) number. We have a website that estimates the R number in every country that has sufficient data, from Yemen to Nicaragua.

Alongside that, I have also been doing work on evaluating the implications of reopening schools.

Q – What are the numbers telling you the implications of reopening schools will be?
I have constructed a modelling framework which explicitly evaluates transmission between schools through households. It is a methodology I originally developed to look at measles in the Netherlands, because they have some vaccine uptake issues, and it works to track COVID-19 through schools as well.

So, I have quantified the probability of transmission between schools through children’s interactions. My model is showing that keeping secondary schools open is more likely to be problematic than anything else. The intuition is that when we reopened schools last year, there was little evidence of transmission so it is probably fine, but that is not necessarily the case.

Public Health England (PHE) conducted a big evaluation of transmission within schools, before last summer, across the UK and there was not much evidence of transmission. But it’s quite a different picture with secondary schools. What I found was that just through households,
if you only send secondary school children back, you could eventually infect 2.5 million households. It is a real potential risk in terms of infecting the population.

Q – Epidemiologists are very much in the spotlight right now. Do you think there will be an uptick in people entering your field in the next few years?
I certainly hope so. It’s always been a challenge to recruit people into mathematical epidemiology, or infectious disease modelling, as it’s highly quantitative. We need people from fields such as mathematics or physics or computer science that have a quantitative background, which is quite competitive, often those graduates get snapped up by other industries. It should help that our profession is a lot more visible than it has ever been. At the very least I hope people stop asking me if I’m a skin doctor…

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