Acute fever may be caused by a range of pathogens, but clinical symptoms may be too non-specific to differentiate the causative organism, so correct diagnosis requires pathogen-specific diagnostic tests. These diagnostics are too expensive for routine use in resource-limited settings. This means that many patients are treated empirically with broad spectrum antibiotics, which may be unecessary, toxic, and increase the risk of antimicrobial resistance. Conversely delayed diagnosis and treatment may lead to poor outcomes. A barrier to low-cost diagnostics in the Philippines, arises from a value chain that spans the world, without Purchasing Power Parity (PPP). If we could use technologies that can be manufactured locally, using local resources, then we have the first step to providing affordable diagnostics in resource poor areas, and delivering a sustained improvement in healthcare, while also developing the local economy. We have made an enzyme (BOON-enzyme) that can do these tests and can be produced almost anywhere without special facilities. We have made the enzyme pink, so that you can see that it has been produced and we have a way of making it stick to sand so that it is very stable. We are going to use one of these enzymes - BOON-Taq in polymerase chain reaction (PCR) - to perform a clinical study in patients with suspected dengue and leptospirosis at the University of Santo Tomas in Manila, to assess the impact of the use of diagnostics on the patient pathway and the disease burden. As a result of these trials we will design a diagnostic kit that can be taken out to rural clinics and we will undertake clinical trials in such a clinic. We will support the study by developing a healthcare economics model of the impact of diagnostics on the patient pathway.

Globally, water-related diseases are a major obstacle to sustainable development (WHO, 2018). Many of these diseases, such as Cholera and Hepatitis A, have been successfully phased out in Malaysia. However, leptospirosis and malaria still affect Malaysians every year. The annual incidence rate of Leptospirosis is actually increasing, from 0.97 cases per 100,000 population in 2004 to 12.47 per 100,000 in 2012. It is well known that leptospirosis and malaria are strongly linked to environmental conditions, and humidity and temperature in particular. Although scientific understanding of this link is advancing at a rapid pace, it is still very difficult to build computational models that make quantitative forecasts of outbreaks. Yet such systems are indispensable for proactive disease management, and to optimise the allocation of resources for medical prevention and interventions. A major difficulty with predicting outbreaks of water-related diseases is the large number of driving factors, which span the environmental and socio-economic realms. Additionally, many of the processes that link the driving factors with disease outbreaks, are highly non-linear and difficult to represent in computational algorithms. This proposal therefore sets out to explore the use of artificial intelligence approaches to identify and model the physical and microbiological interactions that lead to conditions favouring disease occurrences, with the goal of developing an early warning system for disease outbreaks. The complexity and non-linearity in the processes makes AI methods such as the neural network approach highly promising as it is inherently suited to problems that are mathematically difficult to describe and highly non-linear. The scientific field of artificial intelligence is developing at a very rapid pace. This evolution is driven by the exponentially increasing amount of information available online (often referred to as the "big data" era), much of which is highly unstructured and diverse (e.g., data from social media such as twitter feeds and news posts). This has resulted in the development of many novel and powerful algorithms and routines. However, its exploration in the context of water-related diseases is still very limited. Therefore, we propose to leverage these breakthroughs, by testing and adapting these new methodologies to advance predictive modelling of the link between hydrometeorological extremes and water-related diseases. The proposed research combines extensive compilation, synthesis and integration of socio-demographic and infrastructural data alongside data of environmental extremes, with novel computational algorithms to "learn" from the datasets and leverage the outcomes to improve operational forecasting systems. We have assembled a world-leading consortium of scientists that combines expertise on hydrometeorological extremes, artificial intelligence and community health issues. We will use the Malaysian state of Negeri Sembilan as a case study, and will work in close collaboration with the State Department of Health. This will allow us to access historical records that include patients' demographic information. More recently, risk assessment have been conducted using questionnaires that includes assessment of water supply and drainage infrastructure. The epidemiological data will be complemented by environmental data from the Department of Meteorology and the Department of Irrigation and Drainage (which are either available for academic use for free or a small fee), and monthly water quality monitoring data from local District Offices. References WHO, 2018. http://www.who.int/water_sanitation_health/diseases-risks/diseases/diarrhoea

Flooding is a threat to communities in both Malaysia and the UK. Computer modelling is a widely used approach to working out which areas are vulnerable to flooding. This allows government agencies, NGOs and communities to work out how to invest time and resources to protect areas at risk. Understanding of the causes of flooding has increased rapidly in recent years. We now have good data on environmental factors like rain and temperature which can influence where floods will happen. There are now good models of climate change. If we work out where flooding is going to happen, computer models can now be used to work out how flood waters will move around cities and which buildings will flood. One problem that still remains is to include the complexities of real life in these models. We currently assume that the same flood will always lead to the same consequences. This makes models quicker to run, but we know it's not how flooding works. If floods occur just before harvests they can destroy entire crops, but if they occur when fields are empty the costs can be very low. If one flood follows another in quick succession, facilities like hospitals and power stations could remain damaged from the first flood, meaning that the second one has much greater impact on people's lives. With research into how communities are affected by flooding, which takes into account the timing of floods as well as how closely associated they are in time, a genuinely new approach to flood risk could be developed. Malaysia is a very good place to develop these models. Its economy is developing quickly, so new approaches have the opportunity to be tested in a changing environment. Similarly, climate in Malaysia includes monsoons, which are a good test of model ability for environmental modellers. From a development perspective, Malaysia is a success story which is rapidly transitioning towards developed status, but still has large numbers of people at risk and in large areas, development can be set back by severe floods. Lastly, following severe floods in 2014, there is a renewed interest in developing innovative flood risk approaches in Malaysia. Our approach to developing a new flood model in Malaysia would make use of the different experts in our group. Bringing together experts from the UK and Malaysia, both of which have invested significantly in flood research in the last decade, would allow us to combine skills from experts with different specialities. Our economists will use economic modelling to understand how different sectors of the economy might change in future and how they might be exposed to flooding. Our group's environmental scientists will use existing computer models of rivers to show where river levels are likely to become high enough to generate flooding. Our flooding engineers will apply new hydraulics models to show how flood waters move once they have left the rivers. Experts in combining computer model outputs will combine each of these into a new model of flood risks. This new model will be used to find the effects of scenarios (factors we can't control such as climate change and increasing urbanisation) and strategies (factors we can control such as new flood defences and warning systems) which will help to evaluate some of these strategies for their effectiveness and value for money. This will allow future flood planning to be better targeted within Malaysia. We hope that Malaysia will act as a good case study for this research and that it would be taken up by other countries in South East Asia and around the world.

Flooding is a threat to communities in both Malaysia and the UK. Computer modelling is a widely used approach to working out which areas are vulnerable to flooding. This allows government agencies, NGOs and communities to work out how to invest time and resources to protect areas at risk. Understanding of the causes of flooding has increased rapidly in recent years. We now have good data on environmental factors like rain and temperature which can influence where floods will happen. There are now good models of climate change. If we work out where flooding is going to happen, computer models can now be used to work out how flood waters will move around cities and which buildings will flood. One problem that still remains is to include the complexities of real life in these models. We currently assume that the same flood will always lead to the same consequences. This makes models quicker to run, but we know it's not how flooding works. If floods occur just before harvests they can destroy entire crops, but if they occur when fields are empty the costs can be very low. If one flood follows another in quick succession, facilities like hospitals and power stations could remain damaged from the first flood, meaning that the second one has much greater impact on people's lives. With research into how communities are affected by flooding, which takes into account the timing of floods as well as how closely associated they are in time, a genuinely new approach to flood risk could be developed. Malaysia is a very good place to develop these models. Its economy is developing quickly, so new approaches have the opportunity to be tested in a changing environment. Similarly, climate in Malaysia includes monsoons, which are a good test of model ability for environmental modellers. From a development perspective, Malaysia is a success story which is rapidly transitioning towards developed status, but still has large numbers of people at risk and in large areas, development can be set back by severe floods. Lastly, following severe floods in 2014, there is a renewed interest in developing innovative flood risk approaches in Malaysia. Our approach to developing a new flood model in Malaysia would make use of the different experts in our group. Bringing together experts from the UK and Malaysia, both of which have invested significantly in flood research in the last decade, would allow us to combine skills from experts with different specialities. Our economists will use economic modelling to understand how different sectors of the economy might change in future and how they might be exposed to flooding. Our group's environmental scientists will use existing computer models of rivers to show where river levels are likely to become high enough to generate flooding. Our flooding engineers will apply new hydraulics models to show how flood waters move once they have left the rivers. Experts in combining computer model outputs will combine each of these into a new model of flood risks. This new model will be used to find the effects of scenarios (factors we can't control such as climate change and increasing urbanisation) and strategies (factors we can control such as new flood defences and warning systems) which will help to evaluate some of these strategies for their effectiveness and value for money. This will allow future flood planning to be better targeted within Malaysia. We hope that Malaysia will act as a good case study for this research and that it would be taken up by other countries in South East Asia and around the world.