Mathematical tools to inform sustainable interventions against schistosomiasis in Uganda

血吸虫病是一种被忽视的寄生虫病，其社会经济和公共卫生重要性仅次于疟疾。据估计，全球有2.4亿人受到感染，其中大部分属于许多撒哈拉以南非洲国家的最贫困人口。患有这种疾病的人，大多数是儿童，在通过钓鱼和洗澡等行为接触受感染的淡水时感染，并在卫生条件不佳的地区传播疾病。有一种药物可以治疗病例，世界卫生组织建议对学龄儿童或整个社区进行大规模治疗，具体取决于疾病负担。然而，这种药物并不能防止迅速发生的再感染。将药物治疗运动与改善卫生基础设施结合起来，可能是最终控制和最终消除这些人口中这种疾病的关键。然后，问题变成了什么是最好的干预措施，或者干预措施的组合，才能最快、最有效地减轻疾病负担。为了解决这一问题，我们建议开发一种数学方法，将生物感染过程(即对感染的传播建模)与个人行为和对不同干预措施的偏好(即使用称为博弈论的人与人之间的互动建模过程)相结合。将这两者结合起来，我们可以找到在社区取得成功并减少疾病的最合适的干预组合。然后，我们可以探索与干预措施组合相关的成本，以利用最受欢迎、最有效、最负担得起的干预措施。另一个重要方面是，一旦实施干预措施，就对其进行评估，以衡量进展情况。我们需要将不同诊断的使用添加到我们的模型中，因为它们可能会根据设置提供不同的信息。所有这些工作的结合将使我们能够编写现场试验的建议，以测试控制计划，以及有效和受欢迎的运动，我们可以有效地进行监督。该项目是高度多学科的，将汇集数学和生物学以及统计学、流行病学和卫生经济学的专业知识。我们将基于我们以前的工作和文献中的其他出版材料，使用与乌干达合作伙伴在当地收集的社会和经济数据来建立模型。项目成果，具体针对乌干达当地人口，然后可以通过与其他社区和主要利益攸关方接触，推广到其他国家。

Schistosomiasis is a neglected parasitic disease, second only to malaria in its socio-economic and public-health importance. It is estimated that 240 million people are infected worldwide, most of which belong to the poorest populations in many sub-Saharan African countries. Individuals with this disease, the majority children, acquire the infection when they contact infected fresh water through behaviours such as fishing and bathing, and transmit the disease in areas with inadequate sanitation. There is a drug available to treat cases, and the World Health Organization recommends mass treatment of school-age children or whole communities, depending on the disease burden. However, the drug does not prevent reinfection, which occurs rapidly. Combining the drug treatment campaigns with improving sanitation infrastructure could hold the key to finally controlling and eventually eliminating this disease in these populations. The question then becomes what is the best intervention, or combination of interventions, that will most rapidly and efficiently reduce disease burden. To tackle this, we propose to develop a mathematical approach, that integrates the biological infection process (i.e. modelling transmission of infections) with individual behaviour and preference for the different interventions (i.e. using a process of modelling interaction between humans called game theory). Combining these two we can find the most suitable combination of interventions that would be successful in the community and decrease disease. We can then explore the costs associated with the combination of interventions, to leverage the most popular and effective, with the most affordable. Another important aspect is evaluating the interventions, once they are implemented, to measure progress. We need to add the use of different diagnostics to our model, as they might provide different information depending on the setting. The combination of all of this work will then enable us to write recommendations for a field trial to test a control program, with an efficient and popular campaign, which we can effectively monitor. The project is highly multidisciplinary and will bring together expertise from mathematics and biology, as well as, statistics, epidemiology and health economics. We will build the models based on our previous work and additional published materials in the literature, using social and economic data locally collected with our partners in Uganda. The project outcomes, specific to local populations in Uganda, could then be expanded to other countries by engaging with other communities and key stakeholders.