unilever colworth prize 2021 q&a – professor azra ghani
29 april 2021
the 英格兰vs美国谁会赢？ prizes recognise excellence and are awarded to those making significant contributions in the field of microbiology, based on nominations received from our 2022世界杯对阵分析 . they are awarded at our annual conference, where the winners also present their lectures. ahead of the unilever colworth lecture 2021: getting back on track: the tools and strategies needed to achieve malaria elimination and eradication, taking place today, angharad green, policy representative of the society’s early career microbiologists’ forum executive committee, interviewed professor azra ghani to find out more about his work and how it feels to win a 英格兰vs美国谁会赢？ prize.
what made you pursue a career in research?
i think, like many people, you get to the end of your undergraduate degree and wonder what you could do next. i did an undergraduate degree in maths, so it was quite different. i went to the careers service and said i'd like to be able to use maths in some sort of applied setting. they pointed me towards operational research, which i then went and looked up and i started a master’s degree. this focused on the applications of maths, and during that year in the early 1990s, there was quite a lot of work focusing on the hiv epidemic and using mathematical models to explore scenarios for the epidemic. i think at that stage i realised i wanted to learn more about that, and research would be the way to go, so that's how i ended up moving into that area for a phd, although at the time i felt quite nervous, because as a mathematician i didn't think i could go and do a phd in biology.
what was the transition like to undertake a phd in epidemiology after studying mathematics?
it was quite daunting at the time. i think there's much more multi-interdisciplinary work now; our own phd programme has a lot of people coming from maths and biology and all sorts of areas, and people do feel more comfortable about switching disciplines. for example, one of my phd students came from maths, did a modelling epidemiology phd and now runs a lab, so it seems people are much more flexible. but i think when i was doing it, it felt like you had to stay in your discipline, so it was a big change. i also ended up doing the phd while working as a research assistant in an epidemiology department. it was challenging to do both, but it turned out to be a really good experience in epidemiology, as well as going to the clinic, designing questionnaires and interacting with people, which i enjoy doing. the research assistant role involved working with microbiologists who were doing typing of gonorrhoea strains, which introduced me to that area and to more of the basic science, and i found this very interesting.
as the unilever colworth prize is awarded to someone ‘who has demonstrated an outstanding contribution to translational microbiology’, what aspect of your research is translational?
actually, all of it from very early on was translational, so maybe it's nice to put it into a historical perspective. my phd was on gonorrhoea, and that probably didn't feel very translational. there were a lot of interactions with the clinic but not any wider than that. then, for my first postdoctoral position, i started working on the bse epidemic and i was working specifically to try and understand how big a potential human epidemic could be. so that really catapulted me quite early on into the media and into engaging more widely and communicating results. it was actually quite a challenging thing to communicate; it hit the headlines, quite like being involved with the covid-19 research now. so, i discovered that i enjoyed doing the translational side of things and all that i have done since then, on all sorts of diseases, has had more of a translational side. i think my motivation isn't just about the science bit of it, but it's actually answering questions that are relevant to public health.
in the last 10 years, i think it has been mostly the malaria work that is translational. i started working at the london school of hygiene and tropical medicine on malaria, and we've been building model frameworks to try and answer policy-relevant questions. the early engagement started happening with the world health organization (who), and we realised there was a utility to using these approaches to guide country planning and guide strategies to try and understand what could happen, in what is quite a complex system. this has led to working with the who to develop their global strategy for malaria, which was a strategy that came from 2016 to 2030 and it leads up to the un sustainable development goals, and we did and continue to do a lot of work on that with the who. we also interact on the policy committee side to try and help with new guidelines and new products coming out, and i sit on the malaria policy advisory committee. so, having started off in a maths background, i have now become a malariologist, where i use the insights that we get from the collectable perspective to understand what the impact might be of different interventions or combinations of interventions. i also work with the global fund, who are a major funder of malaria research. for example, a lot of the aid money that comes from the uk government goes into the global fund. they then deliver things like bed nets to countries, but funding is limited, so we work with the global fund to try and decide how the money can be best distributed both within countries and between countries, to maximise impact and save lives, which is a nice example of how our research is translational.
i'm sure i can't not mention covid-19 because it's all over the press, i am part of the imperial college london covid-19 pandemic response and i also worked on the sars outbreak in hong kong in 2003 with colleagues in hong kong. that gave us some experience with sars and that again was trying to control those outbreaks. it's been a difficult challenging year, but i think it has been quite translational and impactful and much of my work has been in the low-middle income settings and working with global partners, which i think has been useful.
how do mathematical models influence the way we take action on infectious diseases, and how could they be better used by the government to provide rapid responses in combating the spread of disease?
i think everyone is still learning how to best use models. they have been around for a while, but of course as computation increases, a lot more can be generated. there are multiple areas: the first is really just getting a good understanding of what is driving the cause of any disease and that's the same for an endemic disease like malaria or even a pandemic like covid-19.
an example from covid-19 early on: this time last year we were working very hard to try and understand the two things that we knew would determine what sort of action needs to be taken, and that was how fast is it spreading – the r number – and then the severity. the severity was really quite challenging to estimate from the early chinese data, but those two things really tell you what you need to do. the models can then be useful in illustrating the potential impact of not acting, and those are the large numbers that tend to come out, which are very useful. these are never the numbers that we expect to happen, but they give us a benchmark that, if the fatality rate was 100-fold less, it would have been more like flu, then you would see 100-fold less people in hospital and 100-fold less deaths and it would be like a normal flu year. we could then expect this new infection just to be managed and we would expect a certain number of deaths. but getting these numbers early on made us realise of course that this could be really quite severe, and it would overwhelm the hospitals, as we're seeing, and therefore something needed to happen to interact. the models can then be used to try and work out what that something is and that’s probably the most challenging thing. it's been very difficult for the covid-19 pandemic because we are basing it on people's behaviour. people's behaviour is inherently unpredictable and it is difficult to know what any actions will lead to. that's compounded when you've got a new pathogen; you don't know much about it and there’s lots of learning going on about how it is transmitted. for something like malaria, we’ve got a massive history of looking at that disease, so we have a lot of trial data, we know how effective bed nets and treatments are. we can combine all that different trial data, but we’ve never tested all the different combinations and so the models are really useful in just putting all those bits together and saying, "well if we increase nets by this much, but i also add in this treatment or this chemoprevention, what will happen and what will the impact be?" models can then be used to really help guide control programmes to think about what to invest in.
for diseases we have never encountered before, like covid-19, are there example models that you could use to base your predictions on?
a lot early on was taken from pandemic planning for influenza. the model structures are very similar, and the key thing is the parameters and the understanding, and that of course evolves over time as well. i think there was a lot built on that, but we had done all the work on the sars outbreaks in 2003 as well ,and there was also work done on mers within the groups, so we did have an understanding of what different ways coronaviruses might work compared to an influenza virus. but nobody really knew exactly how it was being transmitted early on, so it's less about the models and, actually, i think 90% of it is about understanding the data, understanding the science and translating that into the parameters of the model. this has been the bit that has been the most challenging.
are the models now more developed and better at predicting the course of the disease?
they are not necessarily better predicting, because making predictions is actually quite challenging in anything that changes quickly, and there is a lot changing. not surprisingly, nobody predicted the new variant, which is inherently unpredictable and we won't be able to predict subsequent variants either. there is an inherent unpredictability there. what we've probably got better at over time is getting the frameworks up, so that we can reproduce information and do things more quickly. this is taking a lot of time and energy, particularly for software engineers in the team to get that pipeline ready to respond. but things just keep coming in and in some ways we are always chasing the next thing and it's quite hard work. at the moment there is lots of work on the vaccine impact and we keep learning new information about how to incorporate that into the models.
what direction are you currently taking with your research at the moment?
we still have a big group working on malaria and we are focusing on that. it has been a really challenging time for malaria because after having made lots of gains, primarily due to the roll out of bed nets, progress stalled in around 2015 and it's been quite flat. that's partly due of course to funding, which is also flat, and it's probably going to be interrupted even more with the covid-19 pandemic. there is a lot of discussion about what could happen, and we do quite a lot of work with partners to work out what can reenergise the movement towards trying to hit some of the goals that we've laid out, which were quite ambitious and we are completely off target of as a global community.
there are exciting things going on. there's hopefully a malaria vaccine coming out and we are working quite closely with the who at the moment, looking at the potential impact of rolling out a vaccine. the vaccine has gone through phase three trials and is in what’s called a sort of implementation phase, where it has been tested in routine implementation in kenya, ghana and malawi at the moment. that decision should occur next year with the who, so we're doing a lot of work on that. also, there are some exciting new vector controls, the integrated vector control consortium is based at the liverpool school of tropical medicine and we work closely with them. they have a new way of looking at trying to kill mosquitoes by targeting them while they are sugar feeding, by putting traps out that essentially contain sugar solutions to kill them. i also work with the target malaria team at imperial college london (uk) looking at gene drive mosquitoes. it is really quite nice because we can get all these different perspectives and try to integrate them into one framework, to see what impact they would have. this is also really interesting scientifically because i have to learn about entomology, gene drive systems and how vaccines work in immunology, so it brings in whole different disciplines that i personally find quite stimulating, and there is a lot of great collaborative work.
what will you cover in your lecture at the 2021 英格兰vs美国谁会赢？ online annual conference?
my passion is still the malaria field, so i will really talk about how that has built up and the sort of work we do, really ranging from thinking about the early stages of product development. there's a lot, i think, that can be done to optimise product development, thinking about not going necessarily into trials for all these new things, but using the models to work out what different product profiles might have, what impact they might have in different settings and then to maybe optimise and improve things. it's sort of a bit like asking whether you need 70% or 90% efficacy of some new intervention to have an impact and especially compare that to existing interventions. i think the modelling is useful there to think about those profiles, and we can get down to the profile of what is the key impact of a bed net and what's the efficacy or durability of a vaccine; all these different things can be implemented into the models.
the middle bit has always been the working with the policy, to take new products that have come through from phase three trials and understand where they fit and how they would begin to fit within policy guidelines. the bit that's been changing recently, and i think is also very exciting, is helping country programmes to prioritise, because the biggest issue i think the world is facing in trying to tackle many infectious diseases, is the lack of funding, and that is particularly true in the lower income settings, so trying to combine the economics with that.
i'm going to also mention the future directions, and i have a new grant starting to try and think about what the right way to achieve malaria eradication as a long-term goal is. this will take into account not just the economics, which we've tended to focus on cost effectiveness, but also how politicians make decisions, so it's what we call a political economy. if a country decides to invest, they will probably put their money into the areas with the highest malaria burden, whereas their neighbouring country might need them to invest in their low areas to prevent importation. what's optimal for one country to do for their malaria programme may not be the best thing to do at a regional or global level. it will be the whole journey i hope, through the entire product pipeline.
what does winning this prize mean to you?
it’s always a real honour to be have anybody recognise you and i think all of us as scientists, we feel like we're sitting in a room working on our own thinking that no one is really noticing, so i think it's a great honour and a great feeling. i feel like it can't just be me, it's obviously the group as a whole because we've got a big group of people. everybody pulls together and contributes skills and input into this work. it really feels like an honour for the group and recognition of all the work they've done. also, the amount of time they have spent as a group engaging with external partners, because that can be quite a difficult thing to balance when you are trying to build a scientific career, get your papers out and grants, as well as all the things that will get you to the next level, but also have the impact which hasn't always been recognised and hopefully it is improving overtime.
what has been the most rewarding aspect of your research career so far?
i definitely think it's the engagement possibly more than the science, which is an odd thing to say as a scientist, although there is a balance. there is the engagement and feeling like you are having an impact and helping people that i think is the most positive thing.
what advice would you offer to early career researchers?
i mean the broad advice is to really follow the things that you enjoy and are your passion. i think that's actually true for everybody. often when somebody comes to either discuss a phd or think about a position, i ask them "what do you enjoy doing on a day-to-day basis?", "what things would get you out of bed in the morning and what doesn't?" and to really follow that. if i reflect back, i think all of us spend too much time being anxious about not achieving the next short-term goal, but once we've achieved it, we don't actually feel any better, we just move onto the next goal. we often think ‘i've got to get this paper out, it's going to make all the difference’, then you get a rejection and that brings you down. really thinking about the fact that those short-term goals are important to your career, but they are not life changing and you will develop a career that you are happy with as long as you just follow the things that you enjoy.
the unilever colworth lecture 2021: getting back on track: the tools and strategies needed to achieve malaria elimination and eradication, takes place this morning at the 英格兰vs美国谁会赢？ annual conference online 2021 at 09:00-09:45.
inspired by our outstanding prize winners? nominations for 2022 prize lectures and the 2023 prize medal are now open. visit the prize lecture pages for more information.
image: mark henley, who, 2017.
