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。虽然目前没有有效的治疗方法，但有有效的治疗方法（例如抗逆转录病毒疗法）可以使感染者活得更长，更健康。目前的目标是让95%的艾滋病毒感染者了解自己的艾滋病毒状况，95%的确诊艾滋病毒感染者接受持续的抗逆转录病毒治疗，95%的接受抗逆转录病毒治疗的人得到病毒抑制。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，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，这是一个计算效率高的基于个人的模型，以模拟艾滋病毒在非洲高流行率社区的传播。在轮换项目中，我添加了女性性工作者结构和cabotegravir药物成分，以模拟基于当前世界的更现实的场景。然后，我们可以比较结果，并向政府或当地社区提出建议，以减少艾滋病毒的传播。目前的模式有几个限制，例如，固定的风险群体（低、中、高），这决定了一个人可以拥有的伴侣的最大数目。因此，需要更大的灵活性和变化，因此应该从分布中得出这个数字。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的参数进行模拟，这将更好地模拟真实的社会。该项目福尔斯EPSRC AI和工程，健康和政府数据科学（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。该项目福尔斯属于EPSRC生物信息学，因为研究目标是非洲的艾滋病毒传播，使用的方法是数学和计算建模。该项目福尔斯属于EPSRC临床技术，因为在我们的模型中，正常背景HIV检测、社区HIV护理提供者提供的额外基于家庭的干预试验和cabotegravir药物都是模拟的。我们还将进行敏感性分析，为诊所提供关于这些检测的适当比例以及哪些人是需要跟进的关键人群的建议。该项目福尔斯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，其中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处理成为病毒抑制更健康的生活。这个项目福尔斯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，使用幂律分布来模拟伴侣的数量。这个项目福尔斯EPSRC软件工程，因为我们通过计算模拟建立模型，它是用C语言编写的，以提高效率和维护性。

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.