 So antimicrobial resistant infections, or AMR, are a huge global health care threat, contributing to an estimated 50,000 deaths in the US and Europe and 700,000 worldwide. Infectious agents that are responsible for a urinary or a respiratory bacterial infection are increasingly becoming resistant to the drugs that are used to treat them. And left unchecked it's estimated that the death toll could rise to 10 million by 2050, costing the world 3.5% of its gross domestic product. Moreover, the distribution is unevenly spread with the global South and Asia suffering more and paying more in terms of AMR. My research at Imperial focuses on tracking the transmission of AMR infection at an individual system on a global level. So what is AMR? What happens when we take an antibiotic? Of the multitude of bacteria in our system, a number are resistant. Antibiotics act to kill sensitive bacteria. This allows resistant bacteria to multiply and they can transfer resistant other bacteria creating resistant infection. Now we know as patients that the more we use antibiotics the less effective that they become. However, my research has shown that after taking an antibiotic there's a three to five-fold increase in the risk of subsequent AMR infection and that this increased risk can last for up to 12 months after taking an antibiotic. We need to think about this at the individual and at the population level. If we can interfere with population transmission dynamics we can lessen the impact of AMR. Now increasingly we're collecting vast quantities of healthcare data and this includes information on the incidence and the prevalence of infection. So traditionally I might have used descriptive statistics to analyze these data but as the data sets become ever more large and more complex novel methods are necessary. So we asked rather than focusing on developing a new pill or a new drug for AMR can we harness the capacity of this big data and can we utilize novel methods to bring it to life. So at Imperial College we're using novel data visualization techniques to bring this data to life. This allows us to generate and to test hypotheses and importantly to track AMR infections across the globe. So we started off by studying the movement of infection through a London hospital network. We took a set of patient journeys patients traveling through hospital wards in this London hospital network. Then using novel layout algorithms that we built in House at Imperial we added extra dimensions to the data, information on time or level of infection risk. When you combine this information with the patient journeys a picture emerged of the infection transmission around the hospital network. These visualizations are interactive and we can segment based on patient features to identify trends. And what we found was several types of pathway. So most patients enter hospital stay for a time in one ward and hopefully return home. But there's a subset of patients that are moving much more frequently around the hospital network. And these represent the greatest threat in terms of infection. By monitoring this movement in real time we can begin to identify AMR hot spots in the network. We can then begin to predict where outbreaks might occur allowing us to intervene early. For example closing hospital wards or temporarily suspending patient transfers. Now we can take these exact same methods and we could utilize them on a global scale to identify AMR hot spots around the world. And we can combine this information with population factors such as genomics, healthcare use and patient movement to begin to make population level predictions. And one example where we've done this is looking at the Ebola outbreak in Western Africa. So here we identified clusters of outbreaks of infection. By utilizing population data on funeral practice and healthcare practices we are able to apply causal influences to those outbreaks and thereby influence health policies to prevent further outbreaks. Big data visualization represents a novel method to identify, predict and control AMR transmission. Thereby avoiding this scenario of 10 million deaths by 2050. There's enormous potential in utilizing this method to harness the capacity of healthcare data and to bring it to life. The key to utilizing this method is ensuring that we have access to the vast quantities of healthcare data that we collect. And this means that we need healthcare systems that are real-time monitored to link clinical research and operational arms of the healthcare system. AMR is a looming global crisis and the answer isn't simply more drugs. Big data visualization could be part of the solution. By creating these visuals we can see where the problems lie and we can see where we can intervene early to reduce AMR transmission. Thereby limiting the impact of AMR on the world's health. Thank you.