Episode 65: COVID-19 Transmission, a Conversation with Dr Michael Tildesley
In this week’s episode, listen to Dr Bahijja interview Dr Michael Tildesley, an professor from the University of Warwick, as they discuss epidemiology, important terms to know regarding COVID-19, and the ongoing emergence of new variants.
Dr Bahijja: Tell us a little about yourself?
Dr Michael: My name is Dr Michael Tildesley and I am an associate professor at the University of Warwick. I work within the field of infectious disease modelling, although my original Bachelors degree was in Mathematics and my PhD was in Astrophysics. I moved into epidemiology shortly after the 2001 Foot and Mouth disease outbreak, [really getting into it more in 2003].
Dr Bahijja: What is your favourite song at the moment?
Dr Michael: I’ve really been into old school music, recently — I’ve been listening quite a bit to the Beatles, especially the song “Let It Be”. I think it represents the current situation quite well, with the lockdown, and how everything is very much out of our control — its quite a classic song.
Dr Bahijja: Can you recommend a film and/or a book?
Dr Michael: A film that came out around a year ago that I really liked, as I’ve always been passionate about history and especially WWI, was 1917. I saw it back in September and it features two soldiers taking an important message to another group of soldiers. The long one take shots were amazing and it really puts you into the scene. What happened in WWI is something we all need to remember, so I recommend that to anyone who hasn’t seen it already.
Let’s Discuss Epidemiology:
Dr Bahijja: Could you explain what an “R Number” and a “Growth Rate” are and how they are important in the context of an infectious disease like COVID-19?
Dr Michael: R Numbers and Growth rates are related measures, but have subtle differences that are quite important. The R number, which has been talked about quite a bit in the press lately, gives an average number of new infections generated by each infected person. [As an example, if the R rate was 2.0, each infected person would infect on average 2.0 other people.] So one person would infect two people, those two people would go on to infect 4 people total — and that is how the case numbers rise and an epidemic grows. If an R number is below 1, that means each person is infecting less than 1 person on average — this indicates that the infection is slowing, and hopefully dying out. Ideally, our response to the pandemic should focus on getting the R number below 1.
The growth rate, in contrast, gives us an idea of how quickly the cases are rising and falling. The R number indicates how many people get infected, but doesn’t reflect how quickly that may happen. The growth rate is more of “this is how many people are infected today, and this is how many will be infected tomorrow”. It gives a sense of the speed of the epidemic. With the growth rate, we try to see if it is greater or less than zero. A number below zero indicates negative growth, or a reduction in cases with each passing day. A number above zero indicates that cases are increasing every day — and the larger that number is, the quicker the cases will increase.
Dr Bahijja: What are the limitations of an “R Number” and a “Growth Rate”?
Dr Michael: They don’t really tell the whole story. If you have a very large R, but a very small growth rate, for example, then the disease may be growing but not very quickly — this means it may be easier to control. If the R number is small but the growth rate is quite high, then that would be of concern. We need to be careful only looking at one of these number in isolation — the context is quite important.
The severity of the disease is also not reflected in these numbers. Let’s look at chicken pox for example. It has a massive R number, but for children, while it is unpleasant, there is really no fatality risk. Even if there is a large R, nobody will need to put a lockdown in place if there is a chickenpox outbreak. You really need to think when making policy — is this virus resulting in severe symptoms and is it sending people to the hospital?
Dr Bahijja: Are there any other statistics we should be aware of to help in our understanding of the spread of COVID-19?
Dr Michael: There are many, but one obvious one worth mentioning is the number of asymptomatic cases. What we see in a sense, in the case and testing numbers, is only the tip of the iceberg — only cases with sufficient symptoms that they got tested. However, there are a substantial number of people with minimal symptoms, symptoms they may not notice, or no symptoms at all — these people oftentimes do not seek a test. Due to these asymptomatic cases, we need to be very careful looking at the cases per day and even comparing them back to the peak in April 2020, as the testing rate was different. We don’t really know with confidence how many people are asymptomatic, but we need to keep this in mind for the future.
Dr Bahijja: Could you tell us a bit more about your research before COVID and how you’ve been able to apply your understanding and knowledge using mathematical modelling to simulate the spread of other zoonotic disease to COVID19?
Dr Michael: I’ll with an example that is neither in humans nor a zoonoses — where I learnt my trade originally was in the aftermath of Foot and Mouth disease. It is a disease of livestock and it primarily effects cows, sheep and pigs. It resulted in quite severe control policies for farmers, such of movement controls. Animals that tested positive, were also sadly put down. It was incredibly sad and also devastating to the UK farming industry. In 2001, we hadn’t seen an outbreak of this disease in around 30 years, so there was some confusion. I think its quite similar to COVID-19 in that way. My role as a modeller is often to figure out how we can use these models in real time. Most of our work is retrospective — seeing how we could’ve improved past circumstances. I have been going back to previous outbreaks, replaying them and trying to forecast useful actions for the current time. […].
Dr Bahijja: Could you also tell as about your role in the Scientific Pandemic Influenza Modelling group (SPI-M) which is the modelling subgroup of the Scientific Advisory Group for Emergencies (SAGE) and how your research has informed national guidance and policy on COVID-19?
Dr Michael: SPI-M is a modelling subgroup of SAGE, and there are a number of academics of government officials in SPI-M and we meet every week. We are responsible for things such as the publishing of the R number every Friday, also doing short and medium forecasts ie. where will be be in 4–6 weeks. There are some teams focusing specifically on transmission in hospitals, or care homes — I have been focusing specifically on transmission via education providers. We have been looking at different strategies related to the opening of schools, based on our models, and determining the effect it could have on the R number and growth rate. Another high profile project was back in October, back on circuit breaker lockdowns. The group at Warwick was looking into the impact of a shorter term 2 week lockdown on transmission. More recently, we have been focusing on higher education — such as how we can get students back on campus and do that safely.
Dr Bahijja: Recently, there were also some new COVID-19 variants described in the UK, South Africa and Brazil — such as B.1.1.7. These have an unusually large number of mutations and appear to spread more easily and quicker than other variants. This variant was identified in September 2020. How and why do variants occur?
Dr Michael: I will say — I’m not an expert on these variants, but I do have some understanding. Viruses are mutating all the time — constantly changing. Most of these changes are quite benign — in fact, we have several variants of interest appearing every month since the pandemic has began. Sometimes, though, these random mutations may happen to help aid the virus in entering a cell, and may result in a more transmissible variant. B.1.17, as well as the variants that appeared in Brazil and South Africa, all seem to be more transmissible, which is the concern.
With these variants — it’s important to note, that even if the variant itself does not result in more severe disease, more people will end up in hospital if more people are getting infected at a higher rate. Unfortunately, we are seeing very high death counts in the UK and other countries due to this increase in transmission — nearly 2000 deaths a day. Oftentimes we see in the long term viruses tend to evolve to become more transmissible but less virulent. [Potentially, we may see this in the future, as the nature of a virus is not to kill the host, but to spread]. […]
Dr Bahijja: What has been the main challenge with modelling COVID-19 spread?
Dr Michael: One big challenge has been misinterpretation of our models. Oftentimes in media interviews, we have had to explain or correct misinterpretations or misconceptions. One for example, was this idea that for each day of Christmas there would have to be five days of lockdown. In reality, this model was relating to predictions regarding the R number, but this idea of fives days of lockdown per day of Christmas was really pushed to the media. Similar things have happened countless times — a number is taken out of context, a quote is misinterpreted, or even during an interview people may misinterpret our answers.
Dr Bahijja: How have artificial intelligence and data science helped in your work?
Dr Michael: I’m not an expert in AI, but I can definitely give an example of something we are currently doing with Machine Learning. One area we have been looking into has been the tier system (the lockdown system used in the UK), and there are many flaws within its implementation. However, it looks like it is possible, that when the lockdown ends, that we will revert into a tier system again. One of the challenges is then figuring out which local authority will be in which tier. There are many factors to address — the case total, the number in hospital, the R number, growth rate, etc. This becomes a massive problem when you have so many local authorities that may need customised policies for their individual circumstances. We started using machine learning to aid in this, to figure out based on the situation on any given day, what the optimal tier for each local authority should be. While tier 4 or lockdown is the best for overall reductions, in the long term we have to balance things such as education with reducing the number of cases. […]
Dr Bahijja: What other technologies do you believe could help in the eradication of COVID-19 and its variants?
Dr Michael: I think mainly there is vaccination. While we are in a fairly good position this week, we really need to ramp up further. Another action is clear government messaging — while it’s not a technology exactly, its very important. We have touched on adherence to rules, and the government can really help with that by communicating the rules clearly. For example, during a press conference, they could very clearly state the matter of the situation such as “we expect this policy to be in place until this month, if this policy exists as it is for this long”, etc. I think instead of false hope, [adherence may increase if people realise if we do certain policies now, we can avoid harsher policies in the future].
Dr Bahijja: A common question that comes up is “Can you predict when the pandemic will end?” — Is this possible?
Dr Michael: There is an awful lot of uncertainty with this sort of prediction. A lot of this comes with how you define “end”. For example, we could get to a stage where all vulnerable people are vaccinated and reduced deaths — but not zero. If COVID-19 becomes seasonal and the deaths reduce to being comparable to deaths from the flu, [would that count as the end?]. It is unlikely we will reduce the deaths to zero anytime in the near future — so the real question is just, what actually do we quantify as the end? Even if we vaccinate everyone in the UK, there are many countries that won’t be able to, potentially for years, for varying reasons. It is a really long term policy and we will have to slowly ease out of our current policies.
Dr Bahijja: Could you give a take-home message for the audience?
Dr Michael: I think the key message to me is that hopefully we will see a reduction of deaths in the next few months, but we are not out of the woods. We are starting to see the end of the tunnel, but we aren’t quite there yet. We still need to adhere to the policies [and stay inside].
If you have any questions you’d like to be answered by Dr Bahijja, feel free to send them in via the website chat, or email MondayScience2020@gmail.com. You can also send us your questions as a voice message via
https://anchor.fm/mondayscience/message. We love to hear your thoughts!