Agent-based Modelling for Ebola Risk Reduction: Understanding and Mitigating the Potential for Global Transportation-fuelled Epidemics (ABMERR)

近年来，从2013-2016年的西非埃博拉疫情到刚果民主共和国(DRC)持续爆发的埃博拉疫情，埃博拉疫情一直吸引着世界的关注。1976年，在当时被称为扎伊尔(现在的刚果民主共和国)的地方记录了第一次埃博拉病毒暴发；确认了318例病例，其中280人死亡。自那时以来，基础设施、旅行模式、人口密度和气候的变化导致了该疾病更大、更频繁的疫情暴发。西非埃博拉疫情的病例蔓延到意大利、西班牙和美国。刚果民主共和国的疫情现已进入第11个月，没有减弱的迹象，对当地人造成了广泛的干扰和损失。包括无国界医生(MSF)和英国红十字会(BRC)在内的人道主义组织正在组织当地应对这场危机，但问题的严重性如此之大，以至于没有解决它的既定技术。人道主义工作者迫切需要行动情报，以抗击日益扩大的流行病。为此，该奖学金旨在开发一种模拟埃博拉病毒在一个地区的传播，作为人类行为和运动的函数。我将与无国界医生组织的曼森创新单位和BRC的地理信息系统团队的合作伙伴一起承担这项工作。该项目将创建并利用基于代理的模型(ABM)来模拟在受埃博拉影响的地区生活、工作和移动的个人的互动和选择。通过捕捉埃博拉病毒是如何通过葬礼、关怀关系和寻求医疗在人与人之间传播的，该模拟将帮助该项目的人道主义合作伙伴了解不同的干预措施可能如何影响这些社会背景下的疾病传播。这项研究将特别侧重于以人道主义项目合作伙伴可以访问的方式分析和可视化结果。这项工作将利用开放源码数据来估计人口并重建交通网络。这些综合生成的数据集将被输入ABM，根据适当的当地行为和选择，更深入地了解疫情可能在哪里爆发，以及它们下一步可能传播到哪里。反过来，作为该项目一部分开发的软件将成为开源软件。通过免费提供模拟软件，人道主义合作伙伴将可以访问这项研究，但政府组织和其他研究人员也可以访问。这些工具包的开发将考虑到非专业人士的需求，以便它们适合拥有潜在敏感数据集的用户，而不需要高性能的机器或不断访问互联网。该项目旨在加强学者和人道主义者之间的联系，并向非专长人士提供尖端方法。该奖学金将提供资源，帮助人道主义者和政府了解他们在抗击埃博拉病毒方面的选择。它还将使这些新工具可供其他研究人员使用，向外传播并促进新的工作。最后，它将使我成为人道主义建模领域ABM领域的国际公认的领导者，使我能够在未来推动这一领域的研究。

Outbreaks of Ebola have gripped the world's attention in recent years, from the Western Africa Ebola epidemic of 2013-2016 to the ongoing outbreak in Democratic Republic of Congo (DRC). The first outbreak of the Ebola virus was recorded in 1976 in what was then called Zaire (now the Democratic Republic of Congo); 318 cases were identified, of which 280 people died. Since that time, changes to infrastructure, travel patterns, population density, and climate have led to larger and more frequent epidemic outbreaks of the disease. The Western Africa Ebola epidemic saw cases spread as far as Italy, Spain, and the United States. The outbreak in DRC is now in its 11th month with no signs of abating and extensive disruptions and cost for local people. Humanitarian groups including Médecins Sans Frontières (MSF) and the British Red Cross (BRC) are organising local responses to the crisis, but the magnitude of the problem is so great that there are no established techniques for resolving it. Humanitarians are in urgent need of operational intelligence in order to fight increasingly large epidemics. To that end, this fellowship aims to develop a simulation of the spread of Ebola through a region as a function of human behaviour and movement. I will undertake this work in conjunction with partners from MSF's Manson Unit for Innovation and BRC's GIS team. The project will create and make use of an agent based model (ABM) to simulate the interactions and choices of individual people living, working, and moving within Ebola-effected areas. By capturing the ways in which Ebola is transmitted between people through funerary practices, caring relationships, and the seeking of medical care, the simulation will help the project's humanitarian partners understand how different interventions may influence the spread of disease in these social contexts. The research will particularly focus on analysing and visualising the results in ways that are accessible to the humanitarian project partners.The work will make use of open source data to estimate populations and recreate transportation networks. These synthetically generated datasets will be fed into the ABM, giving more insight into where outbreaks may flare up and where they may travel next based on appropriate local behaviours and choices. In turn, the software developed as part of the project will be made open source. By making the simulation software freely available, the research will be accessible to the humanitarian partners, but also government organisations and other researchers. The toolkits will be developed with the needs of non-specialists in mind, so that they are suitable for users with potentially sensitive datasets and without high-powered machines or constant access to the internet. The project aims to strengthen the ties between academics and humanitarians, and to make cutting edge methodologies available to non-specialists.The fellowship will provide resources to help humanitarians and government to understand their options in the fight against Ebola. It will also make these new tools available to other researchers, propagating outward and facilitating new work. Finally, it will allow me to establish myself as an internationally recognised leader in the field of ABM for humanitarian modelling, allowing me to push this line of research forward in the future.