Michelle Kendall is a Research Fellow in the Health Protection Research Unit in Genomics and Enabling Data at the University of Warwick, developing statistical methods and software for public health protection using large scale genomic and epidemiological data.

How would you explain your research to a non-specialist?

I develop ways to turn big, often patchy data about infectious diseases into information which is easier to understand and interpret for public health response. Day to day this means a lot of sitting at a computer, working with big files of anonymised patient data (age, diagnosis, medication, etc.) and/or genetic data (Sample 1: ACCTGC…) and using statistics to develop methods and software to make sense of it. For example, I have developed software to help researchers get more precise answers about how who infected whom in a disease outbreak, or how organisms evolved from a common ancestor, or to assess the impacts of interventions on the spread of a disease.

What has led you to the study of statistical genetics and pathogen dynamics? Could you explain your work on COVID-19 at the Oxford Big Data Institute?

It’s been an unusual route: my undergraduate and master’s degree was in pure mathematics, my PhD was in Information Security, and then I moved into mathematical biology, epidemiology and statistics! Strange as it may sound, each of these was relatively small steps as I moved from working with abstract structures to theoretical networks (dots and lines representing computers linked by cryptography) to evolutionary trees (dots and lines representing organisms’ evolutionary relationships to common ancestors) to disease outbreaks (dots and lines representing who infected whom) and other related statistical assessments of disease progression.

In January 2020 when we became aware of the coronavirus outbreak in Wuhan I was working at the Oxford Big Data Institute in the Fraser group, a multidisciplinary team with specialisms in statistics, genetics, epidemiology, bioinformatics and clinical medicine. We were therefore well placed as a team to try to help understand this emerging new disease. Initially, I got involved in communicating the work of my colleagues, developing software to help turn some complicated-looking equations into interactive graphs. This enabled decision-makers to try out our analysis under a range of different assumptions because there was so much uncertainty at the time (and still is!) for example in the proportion of people who are asymptomatic. This helped to make the work of the team transparent and credible.

From there I worked across various projects within the team, which included: our assessment that contact tracing could have a big impact on controlling the epidemic if it was fast enough and could work at a large enough scale, so we proposed developing digital contact tracing apps; a simulator which works as a model city, enabling you to fast-forward through an epidemic to forecast its progression and to test the impacts of interventions; and a detailed assessment of the timings of the disease, from the moment of infection to becoming infectious and (in some cases) displaying symptoms, being hospitalised, requiring critical care, and recovering or dying. The project I led was an evaluation of the Test and Trace programme which began in May 2020, first on the Isle of Wight (an island off the South coast of mainland England) and then nationally. On the Isle of Wight, the programme included the first version of the NHS contact tracing app. We found that the epidemic on the island was particularly well controlled following the Test and Trace launch, and although it was a preliminary analysis it points to more data and research is needed in case there could be lessons learned from there which would translate to local and national strategies. As part of this work, I developed a web app called LocalCovidTracker which updates daily to provide ongoing analysis of the progression of local epidemics across England and Wales.

Could you explain how your work on HIV helped in understanding the behaviour of the Coronavirus?

Although HIV and SARS-COV-2/COVID-19 are clearly very different, some of the statistical techniques we use translate between the two. Also, there are ways in which my training in HIV helped me understand some of the wider behavioural and policy considerations of our work, for example, the importance of communication and education in encouraging people to get tested, in explaining that a disease can be transmitted even if you feel well, and the importance of privacy and anonymity for patients seeking help and complying with treatment plans or quarantine requests.

You have recently joined the University of Warwick as a research fellow in the Health Protection Research Unit in Genomics and Enabling Data. What are your current research activities during the time of COVID-19?

I am continuing some of my COVID-19 projects, particularly in assessing the impact of non-pharmaceutical interventions on epidemic progression. At the same time, I am returning to developing some more general tools for understanding outbreaks and evolution, and for combining genetic and epidemiological data to get a better understanding of infectious diseases.

How has the pandemic and the lockdown in the UK impacted your research and work-life balance? 

My husband has also been working on COVID in the NHS, and we have two young children, so during the 10 weeks when the nursery was closed it was extremely difficult to juggle work and childcare. I worked every spare moment when they were asleep or distracted, with very little sleep for myself – it would not have been sustainable for much longer! Since then, I have been able to achieve a better work-life balance although there have been some very busy weeks. It has been strange and sad to come to the end of my fixed-term contract at Oxford without being able to say goodbye to everyone in person, and it feels a little surreal to be “at” Warwick whilst still working from my makeshift desk in the spare room.

I find it uncomfortable to acknowledge that the pandemic has been beneficial for my career as a researcher. I have published some high-profile papers, gained lots of technical knowledge, learned about engaging with policymakers and the media and giving interviews and taken on more responsibility within a team. There are times when I feel overwhelmed by the gravity of the topic; as the months have passed I have become better at abstracting myself from it a little, and as ever I love the maths and coding parts of my job. There is no doubt that working on covid has helped me to cope with the situation – the suffering around us, the restrictions, the frustrations – although of course, it has made it very hard to switch off from work.

Is there any problem you dream of solving?

Wow, there’s a question. Of course, there are all sorts of problems we’d all love to solve… Of those where I think I could possibly make some headway, I suppose there are two, really: I want to help develop the epidemiological toolkit so that next time there is an infectious disease outbreak we will be able to interpret the early data better and make quicker, better public health decisions throughout. On a rather different track, I have always been keen to help with public understanding and appreciation of maths and statistics. The pandemic has affected everyone and I understand that people want to see the data and interpret it for themselves; I would love to help equip everyone with the knowledge and tools to do so, aiming towards a place where the scientific messages are less confusing and scary, and where it’s harder for conspiracy theories and fake news to take hold.