Ash is part of the ASPIRES consortium, which is aiming to improve the use of antibiotics in surgical settings; specifically, he focuses on how pathogens (disease causing bugs) spread in nosocomial infection (these are infections which are picked up by patients when they are in the hospital).
Investigating the spread of hospital-acquired infections is particularly important because they are often caused by pathogens that are resistant to anti-microbial drugs – this means the anti-microbial drugs (like antibiotics) that are normally used to treat such infections are ineffective against the disease-causing bugs. This makes diseases caused by these pathogens very tricky to treat. One example, which most people will have heard of, is MRSA (full name: methicillin resistant Staphylococcus aureus – methicillin is a type of antibiotic). By identifying where inpatients might be picking up a pathogen like MRSA, interventions that limit transmission of these bugs could be implemented to reduce the spread of infections picked up in healthcare settings.
In order to gauge the spread of disease, Ash is using network science (which is defined as the study of patterns of connection). This allows him to analyse the movements of patients throughout hospitals, particularly between different rooms and wards. By doing this, he is hoping to identify the points where the pathways of these patients intersect – i.e. the moments at which the pathogens might be spreading from one patient to another (sometimes known as ‘transmission hotspots’). This process is known as network analysis. This type of analysis can provide insights into how hospital-acquired infections are spread, and therefore which points in a patients hospital pathway which can be targeted to limit the spread of disease.
One key aspect of Ash’s work has been demonstrating that the movement of patients within hospitals is not random, as it often assumed when analysing movement across hospital networks. In his analyses, Ash has examined what are known as higher order interactions – in this context, this means that instead of the movement of patients throughout a hospital being completely by chance, where a patient goes to is dependent on where they were previously. By doing this, more accurate representations of patient pathways can be designed. This can better identify the transmission hotspots, and therefore significantly improve how hospitals are able to manage infection outbreaks.
What’s more, in his more recent work, Ash has refined his analyses to be able to predict the spread of infection for a specific pathogen. Not all bugs behave in the same way, and this high level of detail can help predict and prevent the spread of individual disease-causing bugs in healthcare settings. Overall, this work is making great strides towards a reduction in spread of antimicrobial resistance pathogens.
Behind the science – Ashleigh Myall
What interested you about the area of AMR?
“I was interested in carbapenem resistance because, more and more, we are seeing bacteria resistant to this class of last-line antimicrobials. At the patient level, carbapenem resistance infections are dangerous and very difficult to treat. Just a few decades ago, this form of resistance was rarely seen. Now it is common across healthcare facilities globally. With this class of resistance spread primarily in hospitals, by contact patterns between patients and staff, it is an area of AMR where network analysis can provide powerful insights and policy recommendations.”
What does a typical day as a computational PhD student look like?
“Typically, I spend time writing code to analyse hospital data. This can mean taking the movement histories of patients and constructing a network of contact patterns. On different days, this can be for different healthcare-associated infections, depending on the current outbreaks of the hospital trust I work with.”
Ultimately, where do you see your work fitting into the landscape of AMR research?
“AMR does not happen in isolation. It is spread, and persistence happens over complex networks of interaction between humans, humans and animals, and animals. Network science allows us to better understand these giant networks, and moreover, offers solutions to combat it’s spread and persistence. As more and more data become available, both inside, and outside of the hospital, I believe network science will become more important for understanding the phenomena.