Risk prediction for Women's Health and Rights in Tanzania: novel statistical methodology to target effective interventions

该计划将扩展数学科学的新进展，以识别、衡量和纠正以前难以解决的与妇女权利有关的人道主义侵权行为（可持续发展目标5,3.1,5.3）。所建立的创新将直接用于东非坦桑尼亚政府支持的卫生和教育干预措施，在这个国家，妇女继续遭受严重的不平等，导致可怕和持续的人道主义虐待：普遍存在的女性生殖器切割（FGM）、强迫婚姻和持续的令人无法接受的围产期死亡率。为了解决这些问题，需要一个新的数学框架，以支撑可持续发展目标5的一个关键问题为动力，与大多数其他问题不同。其他可持续发展目标面临的挑战，无论是洪水、疾病，甚至是极端贫困，都是显而易见的：它们可以被观察、建模和监测。与此形成鲜明对比的是，侵犯妇女权利的挑战：这里的数据被模糊、审查和隐藏，往往是有意为之。真正存在的数据是部分的、不具代表性的、多视角的，来自不同来源的零碎数据。因此，基于数学模型的解决方案常常被全盘忽略。人们往往只知道汇总的、地区一级的数字。个体的脆弱性和风险未被建模；干预措施失败，滥用行为持续存在。这种破坏性的途径是女童和妇女的许多权利问题的症状，需要专门设计一种统计方法来处理这种性质的问题中常见的高度稀疏、嘈杂和不平衡的数据。解决这一问题的技术工作将分三个阶段进行。首先，将开发一种概率数据组装方法，以解决隐藏和混淆数据的挑战。接下来是对面向对象和拓扑数据分析的关键扩展，用于处理多视图和暂时未对齐的数据集。最后一个阶段是将开发集成到完整的预测模型中，并完成框架。通过与国内学者、政府、私营部门合作伙伴和非政府组织建立伙伴关系，并应用新数据源（数字卫生数据；无人机/地球观测图像；移动货币；蜂窝网络数据；众包事件报告），所产生的模型将在项目生命周期内用于两个关键干预流：与合作伙伴d-tree和卫生部建立围产期死亡率模型（可持续发展目标3.1）；2. 与合作伙伴十亿、女童和妇女的希望以及坦桑尼亚发展信托基金一起，为目标教育干预制定女性生殖器切割/强迫婚姻模型（可持续发展目标5.3）。虽然这些干预措施为数学科学的发展提供了初步的重点，但我们希望所产生的框架能够在这一地理范围之外得到应用，并适用于广泛的可持续发展目标。

This programme will extend novel advances in mathematical sciences to identify, measure and rectify previously intractable humanitarian abuses related to Rights of Women (SDG5, 3.1, 5.3). The innovations established will feed directly into government supported health and education interventions in Tanzania, East Africa - a country where women continue to suffer from pernicious inequality, leading to horrendous and sustained humanitarian abuses: widespread Female Genital Mutilation (FGM), Forced Marriage and continued unacceptable rates of Perinatal Mortality. To address these issues a new mathematical framework is required, motivated by a single key issue underpinning SDG5, and distinct from most others. Dreadful as they are, the challenges facing other SDGs, whether they be floods, disease or even extreme poverty, are visible: they can be observed, modelled, monitored. The challenge of Women's Rights abuses stand in contrast: here data is obscured, censored and hidden from sight, often intentionally so. What data does exist is partial, unrepresentative and multi-viewed, arriving piecemeal from disparate sources. As a consequence, solutions based on mathematical modelling are often passed over wholesale. Only aggregate, region level figures tend to be known. Vulnerability and risk across individuals are left unmodelled; interventions fail and abuses are perpetuated. This destructive pathway is symptomatic of many rights issues of girls and women, and calls for a statistical approach designed specifically to handle the highly sparse, noisy and unbalanced data common to problems of this nature. Technical work to address this issue will be undertaken in three phases. First, a methodology for probabilistic data assembly will be developed to address hidden and obfuscated data challenges. This is followed by key extensions to Object Oriented and Topological Data Analysis that handle multi-view and temporally unaligned datasets. A final stage sees integration of developments into full predictive models, and the completion of the framework.Through partnership with in-country academics, government, private-sector partners and NGOS, and application of novel data sources (digital health data; drone/earth observation imagery; mobile-money; cell network data; crowd-sourced event reporting), resulting models will be used in two key intervention streams during project lifetime: 1. Perinatal mortality modelling with partners d-tree and the Ministry of Health (SDG 3.1); 2. FGM/Forced Marriage modelling (SDG 5.3) for target educational interventions with partners onebillion, Hope for Girls and Women and the Tanzania Development Trust. While these interventions provide initial focus for mathematical sciences developments, we expect the framework generated to have application beyond this geographical extent, and to a wide range of Sustainable Development Goals.