 My name is Naomi Wethera. I had data management at Moro Tropical Health Network. In my group, we work with medical experts and researchers to conduct clinical studies with our focus being on infectious diseases that affect the population in the tropics. This would be diseases caused by bacteria, viruses, or other organisms. So in the course of the clinical studies, we collect information from various sources to help us determine whether certain drugs are safer or more effective at treating diseases and also to identify drugs and vaccines that can be used to control to protect individuals from contracting specific diseases when they come into contact with them. So this data comes from a variety of sources. One example is through talking directly with a patient and asking them how they feel and how the disease is affecting their daily life. Another source of data is through looking back at medical records to understand the history of the patient. And another source of data is looking at laboratory results and imaging results. So this data comes to us in the form of words, numbers, and images when you think about x-rays or ultrasound recordings. So the first step is we remove any parameters that could potentially identify the patient because we want to protect the privacy of the patient. And then we organize this data in a way that can be understood by both humans and machines and used for analysis to draw conclusions of the research work. For instance, in determining whether drug A is more effective than drug B in treating malaria among pregnant women. Now, in addition to that, we store this data securely and grant access to individuals who are authorized to do analysis based on the laws of the countries we are working in. Clinical studies can be quite expensive and they can also be quite time consuming. And very often the data that's collected in a clinical study is used primarily for analysis of the research questions for that study. Once that's finished, the data is put away securely and safely. Is there a chance that we could use data that has been collected in past clinical studies to gain new insights on how diseases manifest and also to get ideas about what measures we could take in control of these diseases? There are a lot of technological advancements that can help us to do that. That include mathematical modeling and even more recently, we've seen a lot of progress in artificial intelligence. So using such tools, AI tools, we're able to equip medical professionals with information that could help them to diagnose and treat diseases more effectively. The other application is in mathematical modeling where data from climate data can be combined with population data and then combined also with medical data to predict when seasonal diseases such as influenza would come back where would the outbreak likely be? Which populations are they likely to affect the most? And with this information, government agencies can put in place measures to control the outbreaks or even prepare for the outbreaks through public education, for instance. So looking at this potential for use of informatics and AI in the field of medicine, we have an opportunity to transform the way medical care is delivered and our fight against diseases. And in fact, using AI tools, patients themselves can interact with software from the comfort of their homes to get information about their conditions that would have come from a doctor where they would have had to wait much longer, would probably have had to travel a long distance and maybe would have cost them even more. But one important thing to note about that is these tools need to be accurate and they need to be as knowledgeable or perhaps even more knowledgeable than a human doctor. So how do we achieve that? For these tools to be accurate and to function the way they should, they need to be trained using high quality data that is well organized and well linked. So what we are doing is to set up pipelines that facilitate that through collecting the data and then organizing it in a way that can be easily understood by both machines and humans. And then in addition to that, we're looking back at old data sets that were collected a long time ago. Moro has been in existence for 50 years, so we're looking at fairly old data sets and curating them in a way that looks like the data sets that we're currently developing, generating. So overall, it is our hope that we're able to provide data that's of high quality and that can be used for modelling and for artificial intelligence and various other types of analysis to generate key insights for health of the community. Moro conducts research in diseases that are not very well investigated. That means those data sets generated from those studies are precious and they are rare, there are few of them. So using those data sets and applying technology to generate insights from that data can help us provide health solutions to the communities who need it the most.