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Weekly COVID-19 Pandemic Briefing – Mathematical Modelling and Response Decisions Transcript Webinar: Weekly COVID-19 Pandemic Briefing – Mathematical Modelling and Response Decisions Professor David Heymann CBE Distinguished Fellow, Global Health Programme, Chatham House, Executive Director, Communicable Diseases Cluster, World Health Organization (1998-2003) Professor Azra Ghani Chair of Infectious Disease Epidemiology, Imperial College London Chair: Emma Ross Senior Consulting Fellow, Global Health Programme, Chatham House Event date: 01 July 2020 The views expressed in this document are the sole responsibility of the speaker(s) and participants, and do not necessarily reflect the view of Chatham House, its staff, associates or Council. Chatham House is independent and owes no allegiance to any government or to any political body. It does not take institutional positions on policy issues. This document is issued on the understanding that if any extract is used, the author(s)/speaker(s) and Chatham House should be credited, preferably with the date of the publication or details of the event. Where this document refers to or reports statements made by speakers at an event, every effort has been made to provide a fair representation of their views and opinions. The published text of speeches and presentations may differ from delivery. © The Royal Institute of International Affairs, 2020. 10 St James’s Square, London SW1Y 4LE T +44 (0)20 7957 5700 F +44 (0)20 7957 5710 www.chathamhouse.org Patron: Her Majesty The Queen Chair: Jim O’Neill Director: Dr Robin Niblett Charity Registration Number: 208223 2 Webinar: Weekly COVID-19 Pandemic Briefing – Mathematical Modelling and Response Decisions Emma Ross Good morning and thank you for joining us for this week’s Chatham House COVID-19 briefing with Distinguished Fellow David Heymann. Our guest today is Professor Azra Ghani, a Professor of Infectious Disease Epidemiology in Imperial College London. Her research combines the use of mathematical models and statistical methods to understand the transmission dynamics to control of a range of infectious diseases, to explore the impact of interventions, and to help guide policy. Her group in Imperial College has done modelling for the COVID-19 pandemic response in the UK, but also, globally, to understand how the disease is spreading in different contexts, what the best responses might be, and how generalisable those responses might be to other countries. So, we’re really glad to have her with us today. But before we launch into that, I’ll just cover the housekeeping stuff, again. This briefing is on the record and questions can be submitted during – using the ‘Q&A’ function on Zoom, and upvoted questions are more likely to be selected. So, if you like a question and you want it definitely moved forward, upvote it, and tweeting is absolutely fine. So, Azra, thank you for being with us today, and welcome. Professor Azra Ghani Thank you. Thank you. Emma Ross I thought we could start by you taking us through some of the issues around using mathematical models to guide responses to the pandemic, but before we get onto the specifics of modelling, in the context of this pandemic, I was hoping you could start us off by outlining what role modelling plays in responding to epidemics generally, what is modelling for infectious disease control, what does it do for us, what does it not? So, basically, what is it and why do we do it? Professor Azra Ghani Sure. So, mathematical modelling has really come to the forefront, I think, in this epidemic, but actually has a very, very long history, going back – right back to some of the early work that was done for malaria, to understand how that particular disease was being transmitted. What it is, is a simplification of our understanding of how a disease is transmitted. So, what we do is we try to look at how it is transmitted onwards, so, for example, from person-to-person, and put that into a very simple set of equations, taking into account, for example, how long somebody might be infectious, how many contacts they might make, and therefore, what that might look like as an infection spreads out in a population. So, modelling is a simplification, it isn’t reality. We observe reality. The model tries to take our understanding of the epidemiology, formulise that, and put it into a set of mathematics, or increasingly computer code, in order that we can better understand how transmission is occurring, and then also to be able to look at how we best respond, and what the different responses are that one could make and how they might combine together to best reduce transmission. So, for epidemics, obviously things are highly uncertain early on, so actually, one of the key aspect is not the mathematical modelling per se, but it’s actually trying to get the key parameters that we know determine how fast something spreads and how widely it spreads and then how severe it might be, in 3 Webinar: Weekly COVID-19 Pandemic Briefing – Mathematical Modelling and Response Decisions terms of its impact on the population. Things like how – the R number, very well-known now, but how many onward infections does one initial infection generate, and that helps us track how fast it’s spreading. Also, the time between each generation of infection is important, so for an epidemic like we’re experiencing now with COVID, it’s very rapid, it’s in a matter of days. Something much longer is – an example would be HIV. So, those aspects are brought in and that’s really the focus early on, and certainly during this pandemic, that was really the focus of our work, how severe is the infection, how rapidly is it transmitting, and trying to understand, at the same time, what the likely interventions could be. So, once we’ve got some sense of how transmissions occur, we can then use these models to say, well, if we manage to reduce transmission by a certain amount, for example, through isolating cases early on, and reducing their infectious period, what impact will those have? And the types of things we looked at early on for this particular pandemic is things like school closures, or workplace closures, contact tracing of cases and isolation of contacts, and really, the typical epidemiological responses that we might expect to this sorts of pathogen. Emma Ross Thank you for that really great introduction. David, I wanted to touch on something around an often cited aphorism in this field, which is that all models are wrong, but some are useful. British Statistician, George Box, who’s credited with that quote, has also said that “Any model is at best a useful fiction and that the practical question is how wrong do they have to be to not be useful? Or is the model good enough for this particular application?” And a bit closer to home, Professor Robert Dingwall, Sociologist and Member of the New and Emerging Respiratory Virus Threats Advisory Group, the NERVTAG, in the UK, which feeds into SAGE, the main government expert advisory group, warns that, to quote him, “Frankly, modelling is not that much more of an upgrade on crystal balls.” So, scientific modelling seems to have a lot of complexity. What would you say is the value and the limitations of using mathematical modelling for shaping epidemic response decision-making, when you have a new virus, where there’s a lack of reliable data, and we hardly know anything about it at the beginning? Professor David Heymann CBE I think Azra clearly said that modelling is not the reality, it’s a model. It’s an estimate of what might happen, and this is very useful for public health leaders, for public health people who are trying to plan, because models usually have a best-case scenario and the worst-case scenario. The best-case being what would happen, if you do certain things, and the worst-case, if you don’t do certain things, or if you continue on the current trajectory. So, for that, modelling is very useful and you can sometimes fit in ideas of how you think you could control the outbreak into a model, and that can tell you whether or not that’s a feasible way forward. So, modelling is very important for the public health community. Where modelling goes bad is when the press gets it, or when the Politicians get it, and treat that maximum worst case scenario as reality and they say, “This is what will happen,” and that causes concern and panic in the general population, it causes Politicians to want to do something to show that they can modify this, and it does a whole series of things that it doesn’t need to do, if it’s kept within the public health community. But it can’t be kept in the public health community because it needs to be peer reviewed, it needs to be published and it needs to be put out so that people can read it. So, interpretation, especially by the press, is what’s very difficult for Modellers and for the public health community in general, because they often take the models as being reality, when actually, they’re only modelling and they only – they depend on, as Azra said, on parameters of what you put into the model, the current understanding, which may change tomorrow and make the model entirely a different way – give the 4 Webinar: Weekly COVID-19 Pandemic Briefing – Mathematical Modelling and Response Decisions model a different outcome. So, modelling is an important public health tool in the right hands. Modelling is also very dangerous, if it gets into the communication channels in a way that causes undue concern and sometimes panic among the population and the political leaders.
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