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      "title_narrative":["A mathematical modeling framework for tuberculosis burden estimation and economic evaluation of pharmaceutical interventions"],
      "description_narrative":["Tuberculosis (TB) is a major cause of disease and death globally. In 2015, WHO estimated there were 9.6 million TB cases and 1.5 TB deaths. Nearly 500,000 of these cases were resistant to two or more of the main drugs used to treat TB. New drugs, and combinations of drugs, are being developed to treat tuberculosis, as are new vaccines that may protect against disease in adults.  Quantifying the burden of TB is fundamental to understanding its global epidemiology and for making appropriate resource allocation decisions. Most estimates of new TB case numbers each year rely strongly on the number of cases reported by countries in that year to WHO. Unfortunately, one in three TB cases are thought to go either undetected or unreported, so the number of cases reported underestimates the number of new cases. While one can correct for this, it is hard to know exactly how much to adjust the reported numbers. Some countries have good systems for recording causes of deaths, which can be used to estimate the number of deaths caused by TB. Increasingly, large and expensive prevalence surveys are being used to estimate the number of people with active disease in a population. These estimates are less subject to bias, but measure a different quantity. Little work has explored the best way of combining these three data sources.  A major goal of this work is to use mathematical transmission models for burden estimation and provide a unified framework for all data. These models yield the number of new cases, deaths, and also the prevalence of disease. They explicitly represent disease transmission and so introduce a dependence between the number of new cases in different years. These models involve parameters evidenced from previous epidemiological work, but must be calibrated to learn from data on TB reports, deaths and prevalence. Calibration means adjusting imperfectly known model parameters in order to match observed model outputs to the data. This process provides a model that may be used to make predictions about burden, but may also teach us something about the underlying processes. Many of the parameters concerning the epidemiology and disease course of TB are quite uncertain, and this uncertainty is rarely represented fully in models needing calibration, but will be done in this project using statistical techniques that also allow comparison of different models' performance.   TB burden estimation and calibration of transmission models are almost always carried out on a country-by-country basis. Many parameters describing disease progression are likely to be similar in different countries, even if their exact values differ for unknown reasons. Hierarchical modelling techniques allow such parameters to be correlated between countries. This can improve precision, particularly for countries with little data, as estimates can be informed by data from neighbouring countries. I will explore these techniques for the transmission model, and also in statistical modelling aiming to account for the observed patterns of drug-resistance. The transmission model will ultimately be extended to include different types of drug resistance.  As new treatments and vaccines emerge, those with responsibility for public health will want to understand the potential impact these new technologies can have in terms of gains in health, and changes in spending. Producing cost-effectiveness and budget impact evidence requires a model that includes transmission, in order to account for indirect benefits accrued by avoiding secondary cases. We will use our model to provide guidance to decision-makers seeking to maximise health gain with limited resources. We will also analyse sources of uncertainty in the model to identify future research that would have most value in increasing the precision of burden estimates and in reducing decision uncertainty around the introduction of new interventions.","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."],
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      "title_narrative":["MICA: Development of new agents for the treatment of cryptosporidiosis"],
      "description_narrative":["A recent clinical study in Africa and South Asia has found that cryptosporidiosis is one of the most significant causes of death and illness from diarrheal diseases amongst children in the developing world. Cryptosporidiosis is caused by a single-celled protozoan parasite; the predominant species infecting humans are called Cryptosporidium hominis and Cryptosporidium parvum. This parasite mainly lives in the cells in the gut wall and has a complex life-cycle. Infection occurs due to consumption of water or food contaminated with the parasites. Parasites are spread from an infected individual through their faeces. In people who are healthy and well nourished, the disease clears naturally within a couple of weeks. However, in people who are malnourished (particularly in young children) and people with an immune system that is not functioning properly (for example HIV/AIDS victims), the disease can have a much more significant impact. It is the major contributor to life-threatening diarrhea in young children, with 2.9-4.7 million cases in children under 24 months in sub-Saharan Africa and the Indian sub-continent, leading to more than 200,000 deaths per year. Cryptosporidiosis is also associated with malnutrition and stunted growth in children and probably causes chronic infections, which last for weeks or months. The only drug registered for the treatment of this disease is nitazoxanide, which is not very effective, especially in those patients who are most severely affected due to a weak immune system and/ or malnutrition.   Therefore there is an urgent need for the development of new drugs to treat: (1) children <24 months, especially those that are malnourished and with chronic diarrhea; and (2) immunocompromised children and adults with advanced AIDS and chronic diarrhea. Cryptosporidium may be the cause of as much as 75% of chronic diarrhea in this patient cohort.   We have discovered some chemical starting points that can be used for a drug discovery programme. We have a series of compounds that kill the parasites and also are very effective in clearing the parasites from rodent models of cryptosporidiosis. The compounds are thought to work through preventing the parasite making proteins. The aim of this project is to take these starting points and optimise them to make a molecule which has the potential to be a drug. This will require us to optimise multiple features of the molecule: its ability to kill the parasite, its ability to reach the sites in the body where the parasite resides without being broken down, and its safety. At the end of this project we hope to have a \"preclinical candidate\". This is a compound that we think should be suitable to enter human clinical trials. The steps after this project, prior to human clinical trials will be to make the compound on a larger scale under properly defined conditions and to carry out formal safety testing.","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."],
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      "title_narrative":["Developing performance-based design for foundation systems of WIND turbines in AFRICA (WindAfrica)"],
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