An interview with Dr Adam Kucharski
Dr Adam Kucharski is an Associate Professor in Infectious Disease Epidemiology at the London School of Hygiene & Tropical Medicine. His research focuses on the dynamics of infectious disease outbreaks. In this interview he tells us more about the benefits of analysing data to understand disease outbreaks and why this research is important to microbiology.
Tell us more about your research
I use mathematical models and microbiology to understand the transmission and control of infectious disease outbreaks.
During an outbreak of a new infectious disease, there are two crucial things we need to understand: how the infection is spreading and how to control it. My work uses mathematical and statistical models to investigate what factors are driving transmission, and what effect different control measures might have. For example; how much does seasonal variation in climate affect the dynamics of dengue? How do social contacts influence the shape of influenza outbreaks? How effective may a particular vaccination strategy be in controlling a new Ebola outbreak? These types of questions are crucial to health agencies, as well infectious disease researchers, so we need our analysis to be as realistic as possible.
In recent years our group has worked with a range of collaborators and stakeholders to help provide insights into patterns of disease in populations. This includes running field studies in at-risk populations to generate the data needed to power our analysis. To ensure our models are capturing the underlying dynamics of an infection accurately, we are increasingly bringing together multiple biological data sources, including serological surveys, surveillance data on PCR-confirmed cases, and virus sequence data.
Why is analysing data beneficial when it comes to understanding disease outbreaks?
Each of these datasets provides clues about a different aspect of the outbreak, from changes in the number of people currently infected (surveillance data), to estimates of the background immunity in a population.
We can then compare our model predictions with each of these data sources to test different hypotheses about what might be driving transmission, which can help us predict the shape and size of outbreaks.
Why is this research important?
As new biological tools emerge – such as multiplex assays that can test for prior exposure to a range of antigens, or cheaper, more portable sequencing – there is the potential to obtain a more accurate and fine-scale understanding of the key epidemiological processes responsible for outbreaks. This understanding will enable us to produce better outbreak forecasts and have a much clearer idea of the most effective way to contain outbreaks in future.
