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      "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"],
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      "description_narrative":["C-RISe has delivered, in a partnership between\nthe UK, Mozambique, Madagascar, Mauritius and\nSouth Africa, access to information on sea level rise,\nstorm surge, wind speed, wave heights and current\nvelocities derived from satellite data and validated\nwith local in-situ measurements. The goal was to\nenable local stakeholders to use this information to\nreduce the social and economic impact of coastal\ninundation and increasingly variable weather\npatterns. The three main objectives of the C-Rise\nproject were to:\n• Deliver a Coastal Risk Information Service,\nproviding satellite-derived information about\ncoastal vulnerability to environmental threats\nsuch as sea level rise and extreme wind and\nwave events.\n• Apply and evaluate the C-RISe service through a\nset of Use Cases, applying the C-RISe products\nto end use applications meeting local priorities.\n• Build local capacity to use satellite data to\nprovide scientific decision support for strategy\ndevelopment, governance and management\nof coastal areas to increase resilience to coastal\nhazards.\nThese objectives were fully met, indeed exceeded, in\nsome areas, as follows:\n• The C-RISe information service provided\naccess to 5 satellite derived data sets: Historical\nsatellite altimeter data providing time series of\nsea level, significant wave height and surface\nradar backscatter (2002-2018), Satellite derived\nclimatology of wind velocity (2007-2018),\nSatellite derived climatology of significant wave\nheight (1991-2018), Satellite derived climatology\nof surface currents (2002-2017), Web page\ndelivering near real time marine conditions\ninformation\n• C-RISe partners have developed 27 Use Cases,\nof which 23 have been completed and 4 are still\nin progress. Only 4 Use Cases were originally\nforeseen\n• C-RISe has provided training to over 70 staff\nmembers of project partners in two training\ncourses.","C-RISe will deliver, through an international partnership with Mozambique, Madagascar and South Africa; access to satellite-derived data on sea level, wind speed and wave heights. The goal is to enable stakeholders to use this information to improve socio-economic resilience to coastal hazards associated with sea level changes such as inundation, floods, storm damage, wetland loss, habitat change, coastal erosion and saltwater intrusion."],
      "participating_org_ref":["GB-GOV-13","GB-GOV-13","GB-GOV-EA31"],
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      "other_identifier_ref":["GB-GOV-13-GCRF-UKSA_NS_UKSA-043"],
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      "contact_info_organisation_narrative":["Department of Business Energy and Industrial Strategy"],
      "contact_info_department_narrative":["General enquiries"],
      "contact_info_email":["enquiries@odamanagement.org"],
      "contact_info_website":["https://www.gov.uk/government/publications/beis-official-development-assistance-research-and-innovation"],
      "contact_info_mailing_address_narrative":["Department of Business, Energy and Industrial Strategy, 4th Floor, 1 Victoria Street, SW1H 0ET"],
      "recipient_country_code":["MG","MU","MZ"],
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      "default_currency":"GBP",
      "default_lang":"en",
      "iati_identifier":"GB-GOV-13-GCRF-UKSA_MU_UKSA-46",
      "reporting_org_ref":"GB-GOV-13",
      "reporting_org_type_code":"10",
      "reporting_org_narrative":["DEPARTMENT FOR BUSINESS, ENERGY & INDUSTRIAL STRATEGY"],
      "title_narrative":["Renewable Energy Space Analytics Tool (RE-SAT)"],
      "description_narrative":["The RE-SAT project is delivering an energy analytics\nplatform to equip Small Islands Developing States\n(SIDS) with appropriate information, products and\nmodelling tools to support their transition from fossil\nfuel electricity generation to renewables. For SIDS, the\neconomic imperative is to mitigate the financial risk\nof over reliance on fossil fuels. The RE-SAT platform\nprovides advanced data analytics to help energy\nplanners and investors where to locate renewable\nenergy assets in order to optimise the amount\nof renewable energy generated, minimise their\nenvironmental impact and maximise the return on\ninvestment.\nSIDS have enormous potential to be energyindependent\ndue to their natural renewable energy\n(RE) resources. To date however, many SIDS have\ninsufficient in-situ environmental data and rely on\nincomplete records or limited time-series data to plan\ntheir RE strategy. This leads to overly conservative\nestimates of the available renewable potential and a\ngreater reliance on traditional (fossil fuel) generation\nmechanisms to meet demand.\nTo overcome this data gap and to support SIDS to\nimprove their strategic RE planning, RE-SAT is using\nearth observations and other sources of environmental\ndata to support decision-making regarding the\nfeasibility and location of RE systems. RE-SAT is offering\nSIDS a powerful software application that will:\n• Improve RE strategic planning though high\nresolution power estimates;\n• Support the siting of new RE assets, taking into\naccount environmental variables and historic\nweather patterns;\n• Save money on redundant infrastructure by sizing\nschemes more accurately;\n• Increase productivity through smarter working\nusing the online data repository to share relevant\nenergy documentation with key stakeholders; and\n• Increase the chance of investment in new\nrenewable energy infrastructure through more\naccurate energy projections.\nRE-SAT has already delivered a successful proof-ofconcept\nenergy-planning platform in collaboration\nwith the Government of Seychelles and is working with\nMauritius, St Lucia, Montserrat, Tonga, Vanuatu and\nPalau to scale-up the concept.","To  develop  a  proof-of-concept  demonstrator  of  reusable  and  extendable  analytics  tools  for planners and technical specialists developing options and strategies for SIDS that:\nAssist in quantifying weather related risks and opportunities for renewable energy.\nExploit existing EO, weather model and other environmental data sources.\nAllow the capture of methodologies, results and insights  from studies for a specific country to aid planning in other SIDS"],
      "participating_org_ref":["GB-GOV-13","GB-GOV-13","GB-GOV-EA31"],
      "participating_org_role":["1","2","3","4"],
      "participating_org_type":["10","10","10","80"],
      "participating_org_narrative":["DEPARTMENT FOR BUSINESS, ENERGY & INDUSTRIAL STRATEGY","DEPARTMENT FOR BUSINESS, ENERGY & INDUSTRIAL STRATEGY","UK SPACE AGENCY","INSTITUTE FOR ENVIRONMENTAL ANALYTICS, UNIVERSITY OF READING"],
      "other_identifier_ref":["GCRF_UKSA_SC_UKSA-046b"],
      "activity_status_code":"4",
      "activity_date_iso_date":["2017-01-01T00:00:00Z","2017-01-01T00:00:00Z","2021-03-31T00:00:00Z","2021-03-31T00:00:00Z"],
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      "contact_info_organisation_narrative":["Department of Business Energy and Industrial Strategy"],
      "contact_info_department_narrative":["General enquiries"],
      "contact_info_email":["enquiries@odamanagement.org"],
      "contact_info_website":["https://www.gov.uk/government/publications/beis-official-development-assistance-research-and-innovation"],
      "contact_info_mailing_address_narrative":["Department of Business, Energy and Industrial Strategy, 4th Floor, 1 Victoria Street, SW1H 0ET"],
      "recipient_country_code":["MS","PW","LC","TO","VU","MU"],
      "recipient_country_percentage":[16.666666666666668,16.666666666666668,16.666666666666668,16.666666666666668,16.666666666666668,16.666666666666668],
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      "planned_disbursement_type":["1","1","1","1","2","1","1"],
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      "default_currency":"GBP",
      "default_lang":"en",
      "iati_identifier":"GB-GOV-13-OODA-UKRI-RYHPP58-GX4VQC3-LVGLW5J",
      "reporting_org_ref":"GB-GOV-13",
      "reporting_org_type_code":"10",
      "reporting_org_narrative":["DEPARTMENT FOR BUSINESS, ENERGY & INDUSTRIAL STRATEGY"],
      "title_narrative":["OODA GCRF and Newton Consolidation Accounts University of Exeter"],
      "description_narrative":["The GNCAs represent an additional allocation from BEIS designed to reinvest in excellent UKRI Global Challenges Research Fund (GCRF) and Newton Fund programmes and enable them to maximise development impact. This involves instances where funding can be utilized to 9 original grant objectives affected by the ODA review, or opportunities for new follow-on, knowledge exchange or impact activities. In either case, the funding is targeted to support research along the route to achieving economic or social impact in countries on the OECD DAC list.","Research and development activity contributing to the UK’s strategy to address key development challenges."],
      "participating_org_ref":["GB-GOV-13","GB-GOV-UKRI-12345","GB-GOV-UKRI-12345"],
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      "activity_date_iso_date":["2022-04-01T00:00:00Z","2022-04-01T00:00:00Z","2023-03-31T00:00:00Z"],
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