The story behind the spread of infections
Having completed her PhD at the University of Edinburgh in 2010, Katie Atkins had a spell at Yale School of Public Health in the USA, working on the dynamics and health economics of infectious disease control measures. Dr Atkins joined London School of Hygiene and Tropical Medicine in 2014, and continues to serve there as Associate Professor of Infectious Disease Modelling. In August 2018, Dr Atkins took up a Chancellor’s Fellowship at the Usher Institute of Population Health Sciences and Informatics, an opportunity she describes as affording her the ideal space in which to grow the research group she leads.
Using data to model infectious diseases has significant implications for public health control, as highlighted in a recent paper Dr Atkins co-authored which was published in Nature: Ecology & Evolution. The article explores an aspect of the serious threat to public health from the spread of antibiotic resistance. Although this has attracted news headlines over the past decade or so, the relationship between antibiotic consumption and resistance remains poorly understood. The model presented by Dr Atkins and her colleagues reveals how within-host dynamics interact with both resistant and sensitive pathogens, thus providing valuable insight into the factors shaping the evolution of antibiotic resistance in bacteria.
It’s the kind of behind-the-scenes investigation that the public doesn’t often hear about. “Usually, we are only aware of infectious diseases when someone gets ill or we see an epidemic happening,” Dr Atkins points out. “But there is a story we don’t see that takes place before that. For example, what underlying conditions did the person have that made them susceptible to infection? When, and from whom, did they acquire the infection, and why?” These are among the questions that mathematical modelling can help to answer, providing scientists and public health officials with a better understanding of epidemics by tracking the processes behind their spread.
“By following epidemics over time, we can use mathematical modelling to predict the impact of potential interventions,” Dr Atkins explains. “For example, if public health authorities believe that closing schools may be an important control measure in the case of a flu pandemic, mathematical models can quantify how best to use this intervention to have the greatest possible effect.” Dr Atkins’ work at the Usher Institute builds on the University’s heritage of statistical research into public health, general practice and epidemiology. The aim is to improve care for patients and populations, by making new connections between data-driven innovation and social and medical sciences.