PopART Simulation to Model the Transmission of HIV in Zambia and South Africa

人类免疫缺陷病毒(HIV)是一种攻击人体免疫系统的病毒。虽然目前还没有有效的治疗方法，但有有效的治疗方法(如抗逆转录病毒疗法)使感染者能够活得更长、更健康。目前的目标是让95%的艾滋病毒携带者知道他们的艾滋病毒状况，95%的确诊艾滋病毒感染者接受持续抗逆转录病毒治疗，95%接受抗逆转录病毒治疗的人接受病毒抑制。由于非洲是受影响最严重的地区，占全球新增艾滋病毒感染总数的三分之二，因此艾滋病毒研究的重点是降低非洲的流行率和发病率。我们希望扩展预先开发的PopART-IBM模型，该模型是一种计算高效的基于个体的模型，用于模拟艾滋病毒在非洲高流行社区的传播。在轮换项目中，我加入了女性性工作者结构和卡替格列韦药物成分，以模拟基于当前世界的更真实的场景。然后我们可以比较结果，也可以给政府或当地社区提供建议，以减少艾滋病毒的传播。目前的模型有几个局限性，例如，确定个人可以拥有的最大伴侣数量的固定风险组(低、中、高)。因此，需要更多的灵活性和变化性，因此这个数字应该从分布中提取。在未来，我们将首先推导出数学公式，并将其实现为代码，以便进一步校准。然后，我们可以使用新参数运行模拟，这将更好地模拟真实社会。该项目属于EPSRC人工智能和工程、卫生和政府数据科学(ASG)，因为我们拥有的原始数据是调查数据。它相当混乱，需要一些数据科学技术来确定哪些字段对提取更重要。该项目属于EPSRC生物信息学，因为研究目标是艾滋病毒在非洲的传播，所使用的方法是数学和计算模型。该项目属于EPSRC临床技术，因为在我们的模型中，正常背景艾滋病毒检测、由社区艾滋病毒护理提供者提供的额外家庭干预试验和卡波替格韦药物都被模拟。我们还将进行敏感性分析，为诊所提供关于覆盖这些测试的适当比例以及谁是要跟踪的关键人群的建议。该项目属于EPSRC全球挑战研究基金，EPSRC确保更好的健康、老龄化和福祉，因为我们的目标研究领域是非洲的艾滋病毒，那里大多数是发展中国家。我们的目标是让更多的人意识到他们的艾滋病毒状况和种子治疗为了更健康的生活而受到病毒抑制。这个项目属于EPSRC数学生物学、统计学和应用概率，因为我们的模型涉及数学推导，例如，使用幂定律分布来模拟合作伙伴的数量。这个项目属于EPSRC软件工程，因为我们通过计算模拟来建立模型，这是用C语言编写的，以便更好地提高效率和维护。

Human immunodeficiency virus (HIV) is a virus that attacks the body's immune system. Although there's no current effective cure, there are effective treatments (e.g. antiretroviral therapy) to enable the infected people to live a longer and healthier life. The current aim is to let 95% of all people living with HIV know their HIV status, 95% of all people with diagnosed HIV infection receive sustained ART and 95% of all people receiving antiretroviral therapy receiving ART have viral suppression. As Africa is the most affected region, accounting for two-thirds of the global total of new HIV infections, the HIV research focus is to reduce the prevalence and incidence in Africa.We want to extend the pre-developed PopART-IBM model, which is a computationally efficient individual-based model to simulate HIV transmission in African communities with high prevalence. In the rotation project, I added the female sexual worker structure and cabotegravir drug component to simulate more realistic scenarios based on the current world. Then we can compare the results and also give suggestions to the government or local community to reduce HIV transmission. There are several limitations in the current model, for instance, the fixed risk groups (low, medium and high), which determine the maximum number of partners an individual could have. Consequently, more flexibility and variation are desired, so that this number should be drawn from a distribution. In the future, we will derive the mathematical formulas first and implement them into codes for further calibration. Then we can run simulations with the new parameters, which would better mimic real society.This project falls within the EPSRC AI and Data Science for Engineering, Health and Government (ASG) as the raw data we have is survey data. It is quite messy and requires some data science techniques to determine which fields are more important for extraction. This project falls within the EPSRC Biological Informatics as the research target is HIV transmission in Africa and the used approach is mathematical and computational modelling. This project falls within the EPSRC Clinical technologies as in our model, normal background HIV testing, extra household-based intervention trials offered by community HIV care providers and cabotegravir drug are all simulated. We will also carry out a sensitivity analysis to provide clinics advice about what's the proper proportion to cover these tests and who is in the key population to follow up.This project falls within the EPSRC Global Challenges Research Fund, EPSRC securing better health, aging and wellbeing as our targeted research area is HIV in Africa, where most are developing countries. We aimed at making more people aware of their HIV status and seed treatment to become virally suppressed for healthier lives.This project falls within the EPSRC Mathematical Biology, Statistics and applied probability as our model involves mathematical derivation, for instance, using power law distribution to simulate the number of partners.This project falls within the EPSRC Software engineering as we build the model by computational simulation, which is written in C for better efficiency and maintenance.