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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":["Detailed malaria diagnostics with intelligent microscopy"],
      "description_narrative":["The best way to diagnose malaria remains microscopic examination of blood smears, to identify the plasmodium parasites that are responsible.  This takes around 30 minutes of microscopy, done by a trained technician - skilled workers who are in short supply.  This project will create an intelligent microscope that can greatly multiply the skills of a technician by scanning over the smears automatically, and allowing them to review only the suspicious blood cells on a tablet computer after the smear has been scanned.   Malaria is one of the world's most prevalent infectious diseases.  It affects 200 million per year, and causes around 400 thousand deaths - most of them children in ODA countries in sub-Saharan Africa.  Impressive progress is being made in reducing the incidence of malaria, which makes good diagnosis of the condition ever more important; it is increasingly inaccurate to assume that every patient with a fever has malaria, and doing so will waste drugs and leave potentially life threatening fevers untreated.   The key to reliable, useful diagnosis with an automated microscope lies in computer vision; simply acquiring digital images and tiling them together into a digital smear is an important first step, but robust analysis of the digital images means the technician need not sift through many images of healthy cells.  Instead, they can concentrate their efforts on parts of the image where the algorithm identified suspicious features.  Once trained, our algorithm will be able to identify many parasites, only asking for the technician's opinion in challenging, ambiguous cases when it could not identify objects with certainty.  Fully automated counts of healthy and infected cells will then allow consistent quantification of test results, informing the clinician prescribing treatment and aiding in disease monitoring.   Analysis of medical images raises fundamental issues with the standard \"deep learning\" approach of training a multi-layer neural network on hundreds of thousands of images.  Such algorithms cannot accurately quantify their uncertainty (i.e. flag up when a diagnosis may be inaccurate), nor describe the reasoning that led to a given classification for an image.  They require extremely large training datasets, which must often be labelled by hand.  We will build a generative probabilistic model which, while not feasible in most applications due to the huge range of objects that might conceivably be found in a photograph, is possible in the relatively controlled imaging environment of a microscope.  This will allow us to give a probabilistic verdict on each cell, and highlight cells that couldn't be reliably classified as healthy, infected, or something else.  The generative model will also be able to identify features that led to a classification, for example highlighting infected cells in a large image of a smear.  Both of these features will enable greater trust in the algorithm, and allow it to be used to support, rather than replace, existing clinical staff as well as collecting images that will allow us to improve the algorithm's performance.   Computer vision is a powerful technique, but it requires high-resolution digital representations of blood smears in order to work.  Our project therefore has a hardware component, where we will build on our earlier work with the OpenFlexure Microscope to create a slide-scanning instrument, capable of digitising blood smears in the field.  This instrument will use low cost components and desktop digital manufacturing, so that it can be produced locally - freeing clinics from expensive international supply chains, and creating opportunities for local entrepreneurs that build valuable engineering and design skills.  We have already trialled this approach with the first version of the microscope, which will shortly be available for purchase in Tanzania and Kenya, and we hope to achieve an even greater impact with a fully automated instrument.","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":["UKRI COVID-19 Grant Extension Allocation (CoA) - Brunel University London"],
      "description_narrative":["The UKRI COVID-19 Grant Extension Allocation (CoA) provided organisations with resources to sustain UKRI grant-funded research during the period of pandemic disruption and its immediate aftermath. Some of the funding was directly used supporting the following ODA-eligible grants at Brunel University London: InnovateUK 105316 - Tassou; \nBB/R012695/1; \nBB/R012695/1; \nInnovateUK 42088 Singh  (SOFTS); \nInnovateUK  41760   Singh    (C-SPEL); \nInnovateUK 105669 Singh (INset4KTI); \nBB/P024149/1; \n InnovateUK 48979 Singh (SolCos).","Research and development activity contributing to the UK’s strategy to address key development challenges."],
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