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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. 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The Fellowships will support researchers at risk to continue their research in the UK for up to two years.\n\nThe programme is receiving £3 million of funding from the Department for Business, Energy and Industrial Strategy (BEIS), plus £8.88m from transition funding as the Global Challenges Research Fund closes and the new BEIS fund starts. The Nuffield Foundation, an independent charitable trust, is contributing £0.5 million towards the scheme.","The first priority of the Programme will be as a response to the Russian invasion of Ukraine, which has exposed Ukraine-based researchers and their dependents to direct threat.\n\nThe Programme aims to complement the work already being done by UK institutions, including through their existing cooperation with Cara as members of Cara Universities Network, offering fellowship awards of up to two years to researchers at risk with funding of circa £37,000 per year."], "participating_org_ref":["GB-GOV-26","GB-GOV-26","GB-COH-RC000053","GB-COH-RC000053"], "participating_org_role":["1","2","3","4"], "participating_org_type":["10","10","80","80"], "participating_org_narrative":["DEPARTMENT FOR SCIENCE, INNOVATION AND TECHNOLOGY","DEPARTMENT FOR SCIENCE, INNOVATION AND TECHNOLOGY","BRITISH ACADEMY","BRITISH ACADEMY"], "other_identifier_ref":["GB-GOV-13"], 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After >30 years as a permanently evacuated area, other options for the CEZ are now being actively considered in Ukraine. The President of Ukraine has recently requested the end-users of this project, the Chernobyl ECOCENTRE and the State Agency of Ukraine for Exclusion Zone Management (SAUZEM) to develop a new strategy for its future management. The purpose of the iCLEAR project is to support SAUZEM and ECOCENTRE in the development of this long term strategy by building on our NERC science to develop the first Environmental Management Information System (EMIS) for the CEZ, informed by the first stakeholder consultation exercise on scenario planning for CEZ management. The UK and Ukrainian team will work together to develop a stakeholder-informed EMIS which will provide the end users with clear answers to the key scientific questions necessary for evaluation of management options. This will include an evaluation of the spatial distribution of present and future risk of radiation to humans and wildlife and determination of the spatial distribution of resources in the zone. For Ukraine, the CEZ represents a unique and important potential resource for agriculture, wildlife conservation and (in the 10 km near-zone) radioactive waste disposal. This proposal meets ODA and Global Challenges sustainable development objectives by supporting Ukraine to increase its agriculturally productive land. If just 10-15% of the abandoned lands could be returned to agricultural use, the newly exploited area would represent a land value in excess of $100 Million. But effecting change in the CEZ is highly complex because of its political and emotional significance. So, it is essential that the future zone strategy is based on the best available scientific evidence and is as open as possible, using internationally recommended methods for stakeholder communication and involvement. The applicants, partner institutes (Ukrainian Institute for Agricultural Radiology, UIAR and Ukrainian Hydrometeorological Institute, UHMI) and end-users have extensive data and resources to provide answers to the key questions identified for decision-making. These include key monitoring data and GIS contamination maps. The project will also build on new research provided by the NERC \"TREE\" project which has successfully used the CEZ as a natural laboratory to study transfers and effects of radioactivity. iCLEAR will also use research on the impacts of forest fire in the CEZ developed within the NERC Urgency grant \"REDFIRE\". Coupled with extensive monitoring and mapping data developed by our local project partners and end users, these unique, but unconsolidated, datasets will be incorporated in a new GIS based EMIS. This EMIS will translate our NERC science into an essential scientific underpinning for CEZ management decision making. 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