Genetic data as a signal of changing malaria transmission Malaria, an infectious disease spread by mosquitoes, is one of the world's most devastating illnesses. The majority of malaria infections can be found among children living in Africa, where a child dies roughly every minute from malaria. However, in recent years there have been huge advances in malaria control, with the number of yearly deaths due to malaria roughly halving since the year 2000. This is largely the result of increased investment in insecticide treated mosquito nets and effective antimalarial drugs.  As the number of people infected with malaria decreases we face a new challenge - measuring the amount of malaria that remains in a population. Once the majority of the malaria parasites have been eliminated from the population, and the number of people carrying the disease has come down, the remaining cases will tend to be concentrated in a few hard-to-reach groups of individuals. At this point we start to see diminishing returns with ordinary surveillance methods, and it can be difficult to keep track of how much malaria remains. Without accurate measurement we cannot know if antimalarial strategies are working, and it is possible for the disease to bounce back undetected.  One possible way around this problem is to make use of the information held in genetic data. First, individuals suspected of carrying malaria have blood taken and are checked for the malaria parasites (as is usual when diagnosing a case). The DNA of the parasites is also extracted and sent for analysis. Crucially, the level of variation that we observe in the parasite DNA will depend on how much malaria is circulating in the population; if malaria is contained at low levels then we would expect to see very little genetic variation, with all parasites starting to look essentially genetically identical, whereas if malaria is bouncing back to high levels we would expect to see high levels of genetic variation. In this way, genetic data gives us a window into the processes that are occurring in the parasite population without us having to exhaustively sample from every single infected individual in the population.  In my research I will take this relationship between the amount of circulating malaria and the amount of observed genetic variation and turn it into a tool for measuring ongoing levels of transmission. Through mathematical modelling of the malaria life cycle, coupled with modern computational methods, I will design programs that can be used to extract the information present in genetic data, and use this information to estimate the level of infection in a population. Once developed into a user-friendly piece of software, this tool could be used by scientists and policy-makers around the world to make the most of their available data - or to decide whether it is worth the additional cost of collecting new genetic data.  I is my hope that this tool will provide one more line of attack in the struggle to reduce malaria morbidity and mortality globally. The Global Challenges Research Fund (GCRF) supports cutting-edge research to address challenges faced by developing countries. The fund addresses the UN sustainable development goals. It aims to maximise the impact of research and innovation to improve lives and opportunity in the developing world.