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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":["Self-recovery housing for development: scaling up crisis preparedness and humanitarian shelter response"],
      "description_narrative":["Earthquakes, storms, floods, and conflict cause untold damage to infrastructure, services, agriculture and livelihoods. The destruction of housing is often most visible, and the most devastating to the families, with often hundreds of thousands of homes destroyed. Householders themselves are invariably the first to respond; they are never passive, and signs of shelter recovery are very apparent from the first days after a disaster. The majority - as many as 80 or 90% - receive little or no assistance from the international community and are the main drivers of their own recovery process. This process has been termed 'self-recovery'. Previous GCRF awards supported interdisciplinary teams led by ODI in collaboration with CARE International UK, BGS, UCL and Loughborough University. They developed the understanding of the self-recovery process and advanced the theoretical basis for humanitarian assistance to the process of supporting self-recovery in practice. In large part as a result of this work, self-recovery is now a strategic element of the humanitarian shelter sector discourse with many disaster responses purporting to support this inevitable process. In recognition of the value of a people-centred approach that moves away from the convention product-based modalities, the CARE Philippines post-cyclone self-recovery project won the 2018 World Habitat Award in acknowledgment of its self-recovery focus. Sixteen thousand houses were built, each one unique and reflecting the priorities and means of each family. https://www.world-habitat.org/world-habitat-awards/winners-and-finalists/post-haiyan-self-recovery-housing-programme/ The self-recovery approach that respects the primacy of agency and choice aligns well with current humanitarian and development concerns such as the 'humanitarian development nexus', the Grand Bargain's emphasis on localisation, participation and cash-based responses, the Sendai Framework on Disaster Risk Reduction, the Sustainable Development Goals and the Global Shelter Cluster strategy. This is further explained in the Business Case. However, very little has been done to develop guidance for practice. With the exception of the protocol 'Informing Choice for Better Shelter' that delivers a guide for the development of technical educational material, there are no published tools nor guidelines that provide an implementation framework for self-recovery in shelter projects. This project addresses this gap. CARE International has over 70 country offices across the world, all of them in ODA eligible  countries. All of the proposed activities will take place two hazard prone countries where there is a CARE presence and/or an activated coordination system in the form of a country level shelter cluster. The potential regions to be selected include South Pacific islands, East Africa, Central America, Philippines and SE Asia the precise locations to be dependent on ongoing and new disaster responses. In all instances the research team will be working with the Global Shelter Cluster, country level clusters and/or CARE International country teams. These will all be ODA eligible countries. The protocol developed will be adapted to become a preparedness tool for the specific ODA eligible country context. All outputs will be thoroughly scrutinised by the humanitarian sector and incorporated directly into practice.","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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