RAPID: Understanding the Evolution Patterns of the Ebola Outbreak in West-Africa and Supporting Real-Time Decision Making and Hypothesis Testing through Large Scale Simulations

全球流行病传播在多个(局部和全球)尺度上发生：在局部暴发期间，一个亚群内的个人可能通过局部接触而感染。然后，这些人可能会将感染带到世界上的一个新地区，开始新的疫情。因此，疾病传播模拟需要数据和模型，包括社会接触网络、个人的本地和全球流动模式、传播和恢复率以及暴发条件。虽然存在非常强大、高度模块化和灵活的流行病传播模拟软件，如GLEAM和STEM，但这些模拟软件和现有模型是为高传染性疾病设计的，如流感。相比之下，埃博拉病毒的传播是通过社区体液的密切接触推动的，已有证据表明，在医疗保健环境中或由于当地文化习俗和仪式(如涉及触摸死者身体的埋葬做法)，传播会被放大。与流感和其他呼吸道疾病不同，埃博拉病毒的特殊流行病学特征，包括高致病性、疾病引起的人群行为变化和疾病后期的传染性，显著影响着传播动态。现有的模型还没有考虑到这些方面。通过实时和连续的决策有效地管理当前的紧急情况，需要专门针对埃博拉的时空动态建立计算模型，并为其传播进行数据和模型驱动的计算机模拟。迫切需要帮助运行和解释埃博拉模拟总体(与真实世界的观察结果保持一致)以产生及时的可操作结果的工具。鉴于这一特殊疫情的紧迫性，以及开发针对埃博拉病毒的必要模型和工具的迫切需要，该项目将侧重于埃博拉病毒的传播动态和控制，特别是针对这次埃博拉疫情的产品和过程。有两个主要推动力：埃博拉特定疾病模拟模型，包括特定的传播模式、当地社会和文化变量和不断演变的干预战略(包括干预措施的成本和限制)及其副作用；以及用于(A)在大量不同假设/情景下执行大规模埃博拉模拟组合和(B)分析、探索和可视化埃博拉模拟组合的工具，包括估计埃博拉的传播率，预测埃博拉在不同空间尺度上的时空传播，评估干预措施的成本和影响。流行病模拟跟踪数百个相互依赖的参数，跨越多个层和地理空间框架，受以不同分辨率运行的复杂动态过程的影响。此外，考虑到埃博拉疫情的不可预测性，决策者需要生成集合，进行数千次模拟，每个模拟都有不同的参数，对应于不同但看似合理的情景。随着疫情和干预机制的演变，这些模拟需要根据真实世界的数据不断修订。这项研究将产生专门为官员量身定做的新算法和工具(即E2DMS)，以持续评估不同干预情景的影响，并根据本地和全球范围的真实世界数据修改估计。拟议的E2DMS将填补埃博拉紧急期间数据驱动决策的一个重要漏洞，从而使应用和服务能够产生重大的经济和健康影响。这一结果将转化为对埃博拉疫情的预测-S的特征，包括持续时间和总体规模，并有助于全球努力防止疾病演变为大流行。拟议项目的教育影响将是指导博士后研究人员，并将研究挑战和成果纳入本科生和研究生班级。

Global epidemic propagation occurs at multiple (local and global) scales: individuals within a subpopulation may be infected through local contacts during a local outbreak. These individuals then may carry the infection to a new region of the world, starting a new outbreak. Thus, disease spread simulations require data and models, including social contact networks, local and global mobility patterns of individuals, transmission and recovery rates, and outbreak conditions. While very powerful and highly modular and flexible epidemic spread simulation software, such as GLEaM and STEM, exist, these simulation software and existing models have been designed for highly communicable diseases, such as influenza. In contrast, Ebola spread is driven by close contact via bodily fluids in the community and transmission has been shown to be amplified in the healthcare settings or as a result of local cultural practices and rituals (such as burial practices that involve touching the body of the deceased). In contrast to influenza and other respiratory diseases, the particular epidemiological characteristics of Ebola including the high pathogenicity, disease-induced population behavior changes and infectiousness at later stages of the disease significantly affect the transmission dynamics. Existing models are yet to consider these aspects into account.Effectively managing the current emergency through real-time and continuous decision making requires computational models specifically tailored to the spatio-temporal dynamics of Ebola and data- and model-driven computer simulations for its spreading. Tools that help running and interpreting Ebola simulation ensembles (aligned with the real-world observations) to generate timely actionable results are critically needed. Given the urgency of this particular epidemic and the critical need for the development of the necessary models and tools specific to Ebola, this project will focus on Ebola transmission dynamics and control, specifically targeting products and processes for this Ebola epidemic. There are two main thrusts: Ebola specific disease simulation models, including specific transmission patterns, local social and cultural variables and evolving intervention strategies (including cost and constraints on the interventions) and their side-effects; and tools for (a) executing large-scale Ebola simulation ensembles under a large number of diverse hypotheses/scenarios and (b) analysis, exploration, and visualization of Ebola simulation ensembles, including estimating transmissibility of Ebola, forecasting the spatio-temporal spread of Ebola at different spatial scales, assessing the cost and impact of interventions. Epidemic simulations track 100s of inter-dependent parameters, spanning multiple layers and geo-spatial frames, affected by complex dynamic processes operating at different resolutions. Moreover, given the unpredictability of the Ebola epidemic, decision makers need to generate ensembles, with many thousands of simulations, each with different parameters corresponding to different, but plausible, scenarios. These simulations need to be continuously revised based on real-world data as the epidemic and intervention mechanisms evolve. The research will result in novel algorithms and tools (namely E2DMS) specially tailored for officials to continuously assess the impacts of different intervention scenarios and revise estimates based on real world data, at local and global scales, for the Ebola epidemic.The proposed E2DMS will fill an important hole in data-driven decision making during the Ebola emergency and, thus, will enable applications and services with significant economic and health impact. The results will translate into predictions of the Ebola epidemic?s characteristics, including the duration and overall size, and help the global efforts to prevent the disease from turning into a pandemic. The educational impact of the proposed project will be on mentoring of a post-doctoral researcher and the incorporation of research challenges and outcomes into undergraduate and graduate classes.