 There has never been a greater potential for infectious disease to spread than right now. With urban migration, increased global travel and population growth, governments around the world are tasked with managing fast-spreading disease. Despite available therapies, the world is still battling under the burden of HIV, AIDS, TB and malaria. Now in the last two decades, we have seen a series of health innovations in the form of new and better drugs, novel surgical procedures and a greater understanding of the human body generally. Now while we can measure the impact that each of these innovations will have on a single person, how does this translate to the greater population? A vaccine may have an 80% efficacy over a period of three years, but how will this change the level of disease in the entire country or population of interest? The vaccine itself costs money, but can we expect cost savings in the future? What if we could build computer simulations or a video game, if you like, in order to measure this impact on the population? If we could do so, we could, at a fraction of the cost and in a fraction of the time, make these measurements rather than conducting field survey or a clinical trial. In order to build these models, we first need to have a good understanding of the biology of the disease. We then take the environmental and geographical context and couple it with demographic and health system data in order to get an idea of how the innovation can be implemented. We then write up a series of equations on how the disease will behave in your body, how the disease can spread throughout the population and how the innovation itself can be used to counter the disease. We then write up some computer code in order to build a model out of these equations. And this allows us to make a set of predictions. So for example, we can see in the top left-hand corner how, with the intervention being rolled out, disease incidence decreases over time. We can see from the cost perspective that we initially have a cost increase with the intervention, but cost savings are made in the future as disease decreases throughout the population. By building these models, what this allows us to do is to design a customized disease management strategy to suit different health systems around the world. Whether it is measuring the path to malaria elimination by 2030 or reaching the 1990-90 HIV AIDS targets by 2020, mathematical modeling can be of considerable use. So the greatest challenge in this kind of modeling is being able to communicate our results in a manner that is both understandable and can be used to support health policy and implementation. After all, it is mathematics. And that's quite a tough task to communicate efficiently. So in my lab at the University of Cape Town, what we do is we focus on developing these disease models for a set of diseases, but we package them in easy-to-use computer applications. And this allows policymakers and users of these models to engage directly with the models themselves. So here's an example of some of the applications that we have. We focus on implementation and user friendliness. So you can see by controlling a set of switches, we are able to design or combine several health interventions together to form a basic strategy. Now, this allows us to create outputs such as disease, incidents, prevalence, a total cost, say, for budgetary purposes, and even a cost breakdown of the different components of the health system. This can be really useful in helping a country design their national strategic plan or making funding applications. Now, we have created one such application in the Asia Pacific. The purpose of this application was to predict the path to malaria elimination by 2030. This application has already been influential in shaping policy in the region. And our task now is to attempt to recreate the successes in the Asia Pacific back home in Africa. Now, we're living in an age where data is readily available and where technology has developed to the point that we can process these complex models efficiently. The innovation that I am proposing is to take these mathematical models and package them into easy to use computer applications that allow decision makers and policy makers to engage with them directly and navigate the outputs of millions of simulations with ease. And in this way, mathematical modeling can become an invaluable tool in shaping health policy and saving lives. Thank you.